最新刊期
卷 56 ， 期 6 ， 2024
- “在复杂制造过程质量控制领域，专家提出了基于mRMR-GA-ResNet的多工序质量软测量建模方法，为系统优化控制和安全稳定运行提供解决方案。”摘要：Objective Accurately perceiving key quality variables in complex manufacturing processes is essential for achieving system optimization control and ensuring safe and stable operation. Given the numerous production procedures, interconnected loops, quality inheritance across procedures, and the spatiotemporal distribution of data, precisely perceiving process quality presents many challenges. In this context, this study proposes a soft measurement modeling method for quality in multi-procedure complex manufacturing processes, considering process time delays, based on mRMR-GA-ResNet.Methods First, in complex manufacturing processes with continuous multi-procedure production, different process variables and quality variables exhibit varying time delays, complicating the selection of auxiliary variables. This study proposes a multi-sensor process variable selection method that considers the time delay between process and quality variables based on minimum redundancy maximum relevance (mRMR) and genetic algorithm (GA). Multi-sensor process variables are segmented by the procedure according to the process knowledge. For each procedure, the time delay effect between different procedure segments and quality variables is considered, and data alignment is performed. Using mRMR as the relevance measure, the initial feature subset for each procedure is obtained. GA is employed as the optimal feature search strategy, where the initial feature subset is binary encoded (1 for selected feature, 0 for unselected feature), and an initial population is randomly generated. The fitness function is constructed by combining the prediction accuracy of the extreme gradient boosting (XGBoost) model and the number of selected features, to measure the fitness of each individual in the population. Additionally, an iterative optimization search is performed to determine the final optimal feature subset, thus deriving the auxiliary variable set for modeling. Second, considering the spatiotemporal correlation coupling and quality inheritance characteristics of complex manufacturing processes, to better utilize the optimal feature subset and fully explore the deep information within and between different procedures, this study proposes a three-dimensional (feature-time-procedure) sample space representation method. Temporal-spatial feature extraction is performed using a residual network, and the final quality prediction value is obtained through local-global feature fusion. Since the number of features in the optimal feature subset of each procedure may vary, the procedure with the fewest features is taken as the reference, and the optimal feature subsets of other procedures are dimensionally reduced using principal component analysis (PCA) to ensure that all procedures have the same number of features. Based on the optimal feature subsets of different procedures after PCA dimensionality reduction, three-dimensional samples are constructed. In traditional soft measurement modeling methods, samples are generally in a two-dimensional form. In this study, within each procedure, samples are represented in a two-dimensional (feature-time) form, and procedures are constructed as channels to build three-dimensional (feature-time-procedure) samples. Upon extracting local (within-procedure) spatiotemporal features, global (inter-procedure) feature fusion is performed through a combination of three-dimensional samples and convolution operations.Results and Discussions Experimental validation is conducted using an actual manufacturing process, specifically the float glass production process. In order to verify the effectiveness of the proposed time-delay handling method, the following comparative experiments are set up: In Experiment 1, the time delay between process variables and quality variables is considered, and data alignment is performed according to the proposed method. In Experiment 2, the time delay is not considered. Four correlation-based feature selection algorithms, Pearson correlation coefficient (PCC), Spearman correlation coefficient (SCC), mutual information (MI), and minimum redundancy maximum relevance (mRMR), are used for feature selection, and the same number of features are selected for both experiments. XGBoost is used as the prediction model to predict the quality variables. The results indicate that the proposed time-delay handling method improve the prediction accuracy of the model, validating the effectiveness of the proposed method. To further verify the effectiveness of the proposed multi-sensor process variable selection method, comparative tests are conducted using the four correlation-based feature selection algorithms (PCC, SCC, MI, and MIC) with the same number of selected features for each algorithm. Both XGBoost and a residual network are used as prediction models to predict waviness.Conclusions The results indicate that, under the same number of selected features, the proposed multi-sensor process variable selection method outperforms the other four correlation-based feature selection methods (PCC, SCC, MI, MIC) in terms of predictive performance, achieving a balance between feature selection time complexity and prediction accuracy. Moreover, compared to the traditional two-dimensional (feature-time) sample construction method, the proposed three-dimensional (feature-time-procedure) sample space representation method, utilizing ResNet for temporal-spatial feature extraction and local-global feature fusion, further improves the model’s prediction accuracy. The ERMS, EMA, and ESMAP are improved by 9.2%, 10.8%, and 9.8%, respectively, verifying the effectiveness of the proposed method.关键词：Multi-process Soft Sensing;feature selection;Residual Network;Minimum Redundancy Maximum Relevance;Genetic algorithm
- 57
- |
- 51
- |
- 0
发布时间：2024-11-22 - “最新研究提出了一种融合动静特征的分布式故障检测方法，有效应对复杂非平稳工业过程的挑战，为工业过程质量控制提供可靠技术支持。”摘要：The statistical characteristics of process data time series in the production process typically vary over time due to changes in production conditions, unknown external interference, load fluctuations, and other factors. Hence, complex industrial processes often exhibit non-stationary characteristics. These non-stationary characteristics can obscure faults in industrial processes. Traditional quality-related fault detection methods cannot detect abnormalities on time, leading to the transfer and evolution of faults between different operational units of industrial processes. This failure threatens the quality of products and the stable operation of the production process and can also damage the economic benefits of enterprises, presenting significant challenges to the quality-related fault detection of complex industrial processes. It is essential to investigate quality-related fault detection methods for non-stationary processes, detect abnormal product quality in time, and take appropriate measures to reduce the adverse impact on enterprises’ production processes and minimize their economic losses. Therefore, this study proposes a new distributed quality-related fault detection method for complex non-stationary industrial processes based on dynamic and static feature fusion. This distributed method considers the local neighborhood relationships and information interaction between sub-blocks, providing an effective process monitoring approach for complex industrial systems. Initially, the minimal redundancy maximal relevance (mRMR) algorithm is employed to reveal and quantify the linear and nonlinear relationships between process and quality variables. This classical method identifies a feature subset that maximizes the correlation between features and targets and minimizes redundancy between features based on mutual information. Process variables highly correlated with quality variables are selected using the mRMR algorithm. Simultaneously, the algorithm eliminates redundancy among the selected process variables to simplify the subsequent construction of the fault detection model, enhancing its accuracy. Then, the selected process variables are categorized into stationary and non-stationary variables using the augmented Dickey–Fuller (ADF) test method, a type of unit root test. It hypothesizes that if a time series can be expressed as an autoregressive model containing a unit root, it is considered non-stationary. In addition, mechanistic knowledge of the industrial process is applied to divide the complex industrial process into multiple physically meaningful sub-blocks. Common variables facilitate the information interaction between sub-blocks. When constructing models for local sub-blocks, information from neighboring sub-blocks is incorporated, enabling the transmission of information in a physically meaningful manner. In contrast, traditional distributed monitoring methods only utilize the information of variables within each sub-block without considering the correlation between different sub-blocks. The static and dynamic features of stationary and non-stationary variables within the sub-blocks are then extracted using partial least squares (PLS) and long short-term memory (LSTM) network methods, respectively, and the features are fused. The dynamic and static information of the industrial process is fully utilized to reduce the dimensionality and feature extraction. The canonical variable analysis (CVA) algorithm maximizes the correlation between the fused features and quality variables. Specifically, the fused features and quality variables are employed to construct past and future data vectors in the CVA algorithm, further extracting the canonical variables and conducting statistical monitoring on the canonical variables. In each sub-block, quality anomaly detection statistics in the state subspace and the residual subspace are constructed using
$ {T^2} $ 关键词：quality-related;distributed fault detection;feature extraction;dynamic-static fusion;complex nonstationary processes- 56
- |
- 50
- |
- 0
发布时间：2024-11-22 - “在变压器故障诊断领域，专家提出了基于Lissajous图形与卷积神经网络的故障诊断方法，通过负载归一化和特征提取，有效区分不同程度和位置的绕组短路故障。”摘要：Objective Accurately detecting early transformer faults is critical to ensure the stable operation of the power system. Currently, commonly used offline detection methods are limited by maintenance and operational cycles, making real-time monitoring and detecting faults impossible. On-line monitoring methods, such as vibration monitoring and thermal imaging, are constrained by physical structures, and research into detecting weak signal changes that reflect fault characteristics is limited. As a result, these methods cannot accurately detect minor faults inside the transformer. Transformer load variations significantly influence fault classification, leading to reduced classification accuracy. This study proposes a transformer winding fault diagnosis method based on Lissajous graphics and convolutional neural networks (CNN).Methods First, a simulation model consistent with an actual transformer is developed, and the simulation system is utilized to obtain magnetic flux leakage signal data from different measurement points outside the winding under both normal and fault conditions. After processing the collected data, it is randomly divided into a training dataset (validation dataset), and the remaining data is set as a testing dataset. The original signal is then constructed into a high-dimensional space, and parameters such as length, swing angle, area, least square radius, and roundness of the long and short axes in the Lissajous diagram are derived as characteristic parameters that reflect changes in the amplitude and phase angle of the leakage magnetic field. Second, the Lissajous curve is employed to extract the characteristic quantities formed by the leakage magnetic field, and the data is converted into 5 × 5 grayscale image data. When a fault occurs, the changes in characteristic quantities are extracted as the input for CNN, and artificial intelligence technology is employed to analyze differences in transformer winding fault classifications. This allows for the diagnosis of actual transformer faults using simulation data. Finally, 80 simulated data groups for each fault state are used as training samples and 20 waveforms are employed as test samples to verify the effectiveness of the proposed diagnosis method. The results are compared to other models constructed from the original data.Results and Discussions When the transformer model is operated under rated load to produce data, the radial leakage magnetic fields measured at 1 and 5 are plotted as Lissajous diagrams, respectively. The Lissajous number changes with variations in the load, which negatively affects classification accuracy. After normalizing the magnetic flux leakage signal load, the Lissajous diagrams at measurement points 1 and 5 are obtained after load normalization. This normalization eliminates the influence of load-induced changes on the Lissajous number, ensuring that load variations no longer impact the characteristic quantity of the leakage magnetic field. When setting the transformer model to a fault state and using the magnetic flux leakage signal data generated from both normal and fault states, the amplitude and phase angle of the leakage electric field at the measurement point are different. Setting 2-turn, 4-turn, and 8-turn short-circuit faults at the end of the winding of the simulated transformer reveals that the parameters of the Lissajous diagram change as the severity of the short-circuit fault at the winding end increases. Four parameters are utilized to analyze the Lissajous diagram: long axis (a), short axis (b), slope (k), and eccentricity (e). The differences between the Lissajous diagrams of the normal winding and the fault winding are as follows: the values of parameter a in the 2-turn short-circuit fault, 4-turn (end), 8-turn, and 4-turn (middle) are −0.07%, −0.05%, 0.09%, and 0.04%, respectively. The values of parameter b in the 2-turn short-circuit fault, 4-turn (end), 8-turn, and 4-turn (middle) are 11.5%, 25.42%, −14.01%, and −28.35%, respectively. The values of parameter k in the 2-turn short-circuit fault, 4-turn (end), 8-turn, and 4-turn (middle) are −1.2%, −1.32%, −3.11%, and −1.96%, respectively. The values of parameter e in the 2-turn short-circuit fault, 4-turn (end), 8-turn, and 4-turn (middle) are all 0. The results indicated that the maximum variation occurs in the 4-turn short-circuit fault. When a short-circuit fault occurs, the equivalent circuit undergoes significant changes. However, as the severity of the short-circuit fault increases, the Lissajous diagram does not change linearly as the fault location moves closer to the middle of the winding with increasing fault turns. This indicates that the Lissajous diagram is sensitive to winding faults. The variation in the characteristic quantity during fault occurrence is extracted as the input to CNN by selecting 80 simulation group data samples for each fault state as training samples and 20 waveforms as test samples, converting them into 5×5 grayscale image data. This process is performed on an Intel Core i7-13700k CPU with 32 GB of memory. The deep learning network is constructed using Pytorch, with Python as the programming language. A batch size of 10 data sets, an initial learning rate of 0.001, and 70 training epochs are utilized. The accuracy of the LS–CNN model is 92.61%, that of the LN–SVR model is 95.27%, and that of the LN–LS–CNN model proposed in this study is 97.48%. These results demonstrated that the proposed method is effective for the timely detection and diagnosis of internal transformer winding faults through real-time acquisition of transformer fault data, providing a reference for planning periodic transformer shutdown maintenance.Conclusions The results demonstrated that the proposed method suits transformer magnetic flux leakage detection, even when influenced by load changes. The transformer fault is diagnosed by extracting the characteristic quantity of the leakage magnetic field, improving the utilization of leakage magnetic field information. This method is verified to be effective for the timely detection and diagnosis of internal transformer winding faults through real-time acquisition of transformer fault data and offers a reference for reasonable periodic shutdown maintenance of transformers.关键词：power transformer;fluctuation of load;winding fault;leakage magnetic field;Lissajous graphics;convolutional neural network
- 44
- |
- 48
- |
- 0
发布时间：2024-11-22 - “在工业自动化产线异常检测领域，专家提出了基于多层级视频Transformer的视频分类模型MLVT，有效选出最清晰帧，为自动定位提供强有力保障。”摘要：Objective This study investigates the advanced application of a six-axis robotic arm equipped with a high-resolution industrial camera to capture precise images of workpiece surfaces. The setup is designed to acquire a dynamic video sequence illustrating the transition of image clarity, starting from blurry, achieving optimal clarity, and then reverting to blurry. The primary goal is to select the clearest frame from this sequence, which is critical in determining the precise focusing distance required for automated machining processes. The industrial camera is strategically mounted on the robotic arm, which meticulously controls the camera’s downward trajectory, ensuring the capture of varying image qualities. As the camera descends, it records the shifting focus on the workpiece surface, from out-of-focus (blurry) to in-focus (clear), and back to out-of-focus. This fluctuation is crucial, as blurry images can significantly impair the performance of subsequent tasks, particularly those involving deep learning-based intelligent recognition systems utilized in modern manufacturing. Blurry images may result in inaccurate feature recognition, adversely affecting the quality and precision of automated operations. An effective and precise video processing methodology is utilized to address these challenges. This approach incorporates advanced image processing techniques to analyze video sequences captured by the industrial camera. Sophisticated algorithms enable the system to identify the frame with optimal clarity and sharpness. This frame is critical feedback for adjusting the robotic arm, ensuring that the camera aligns precisely with the position where the focal length is accurately calibrated to the workpiece’s surface. This process guarantees the high quality of captured images and boosts the overall efficiency of the machining process. The system significantly reduces human error and enhances the consistency of output, which is crucial in high-precision manufacturing environments by automating the focus adjustment based on the clearest image. In addition, integrating this technology into existing industrial setups is expected to streamline operations, decrease waste, and enhance the speed and accuracy of production cycles. This study highlights the technological integration, addresses the challenges, and the substantial enhancements in automated machining processes facilitated by this innovative approach.Methods This study introduces a sophisticated Multi-level Video Transformer algorithm-based video classification model, the Multi-level Video Transformer, designed for high-level semantic video representation learning. This innovative model is developed to identify the clearest frame within a video sequence, a pivotal step for enhancing automated machining precision. The methodology commences with a novel token segmentation approach named Multi-level Tokenization (MLT). This approach divides the original video data into token sequences across four levels: 2D Patch, 3D Patch, Frame, and Clip, capturing a comprehensive range of spatial and temporal details. After token segmentation, positional encodings are applied to these tokens to preserve the sequence order, which is crucial for processing time-dependent data. The tokens are input into the newly developed Multi-level Encoder (MLE) for advanced attention calculations. At the core of the MLE are its dual attention modules: the Level-wise Learnable Attention (LWLA) and the Multi-level Cross Attention (MLCA), each stacked multiple times to deepen learning and integrate features more effectively. The LWLA employs a deformable attention mechanism, an innovative replacement for global attention that calculates feature similarity more flexibly and efficiently, reducing computational costs and mitigating slow convergence issues commonly associated with traditional models. In contrast, the MLCA transcends traditional layer limitations by conducting global attention calculations across the entire token sequence, fostering a deeper integration of features at all levels. This integration is further enhanced by incorporating classification tokens in the MLCA layer, which develop concurrently with global tokens. It is maintained throughout the processing stages. After multiple iterations through the MLEs, these tokens are fed into a Multi-Layer Perceptron for final classification predictions, focusing on semantic-level video classification. The empirical validation of this model employs data gathered on-site with a robotic arm equipped with an industrial camera, creating a unique dataset. The operational setup involves positioning the camera vertically downward above metal samples. The data collection protocol is meticulously designed: it starts with the camera at a height where the sample appears blurry due to exceeding the focal length, then the camera is gradually lowered until the clearest possible image is captured and continued beyond to capture the re-emergence of blurriness. This methodical movement generates a video sequence of “blurry - gradually clear - clearest - gradually blurry - blurry” images. From this sequence, the clearest image is meticulously selected, and its index is recorded as the actual value label, serving as a critical dataset for training and testing the proposed model. This comprehensive approach ensures the precision of frame selection and significantly contributes to the reliability and efficiency of automated processes that rely on precise visual data for operational accuracy.Results and Disccussions The empirical assessment of the Multi-level Video Transformer model demonstrated encouraging outcomes across its three variants, achieving classification accuracies of 87.2%, 88.6%, and 88.9% on a custom video dataset. These results signify substantial advancements in video processing for precision tasks. The proposed models display superior performance when these variants are compared to mainstream video transformers of comparable parameter sizes. This success highlights the effectiveness of the specialized approach in managing the complexities of selecting the clearest frame from video sequences. The refinement and precision inherent in the proposed models facilitate the identification of the sharpest frames and reduce potential errors in subsequent automated tasks that depend critically on image clarity for accuracy. The Multi-level Video Transformer affirms its robustness and reliability, establishing a new benchmark for video classification tasks in industrial applications by attaining such high classification accuracies. In addition, these results offer compelling evidence that the proposed methodological innovations, such as MLT, deformable attention mechanisms, and cross-level attention integration, significantly enhance model performance. These advancements are particularly advantageous for tasks requiring great detail and precision in frame selection, which are crucial in many industrial and manufacturing environments.Conclusions Accordingly, the Multi-level Video Transformer meets and surpasses current industry standards for video classification, marking a significant advancement over existing technologies. This model’s triumph lays the groundwork for more nuanced and effective automated systems capable of operating with heightened accuracy and reduced human intervention. This will be particularly transformative in sectors where precision is critical, laying a robust foundation for further research and development in intelligent automation and machine learning applications in visual data processing.关键词：video transformer;video classification;visual automatic localization;deformable attention
- 59
- |
- 42
- |
- 0
发布时间：2024-11-22 - “在航空器导航领域，专家提出了基于注意力机制和时域卷积神经网络的多阶段高度预测算法LTCA–TCN，有效提升了飞行安全性与可靠性。”摘要：Objective Combining an inertial navigation system (INS) and a global positioning system (GPS) in stable conditions of GPS satellite signals offers the most accurate altitude indication, termed inertial satellite composite altitude. When GPS signals are lost or unstable, aircraft must rely only on INS altitude, introducing a discrepancy compared to the composite altitude. This reduction in altitude indication accuracy significantly affects navigation performance. Hence, the predictive recovery of aircraft altitude without GPS signals is crucial. Current research faces challenges in mining high-dimensional flight data and enhancing prediction accuracy. This study proposes a multi-stage altitude prediction model using attention mechanisms and temporal convolutional neural networks (TCNs).Methods The aircraft’s flight stages are determined to facilitate targeted altitude prediction for different flight phases. Traditional clustering algorithms often struggle to capture transitional states in time-series data. Therefore, a fuzzy logic approach is adopted to map ambiguous inputs to explicit output states, enabling the extraction of climb, cruise, and descent phases from the aircraft’s entire flight process. This segmentation aids in better capturing phase-specific features for the prediction model and provides data reserves for the three stages. Addressing the longitudinal nature of aircraft flight parameter time series, a long temporal correlation attention (LTCA) mechanism is designed for feature extraction, enhancing spatiotemporal correlation representation. LTCA efficiently exploited attention mechanisms to extract key features from multi-dimensional flight parameter data samples through adaptive global average pooling (GAP) and one-dimensional convolution, considering global and local information. This approach provided a more effective feature representation for aircraft altitude prediction in the absence of satellite signals. Then, an LTCA–TCN altitude prediction model is constructed using the temporal data processing capability of TCNs. Finally, due to the inability of classical regression model performance metrics to account for error tolerance across different flight phases, a novel evaluation metric called “Score” is proposed to assess the multi-stage altitude prediction capability of the model. This metric considered overestimation and underestimation scenarios compared to ground truth, setting error upper and lower bounds to penalize data points exceeding error tolerance limits. The Score aggregates the prediction scores of each data sample for each stage, yielding an overall score and providing a comprehensive evaluation of the model’s performance across flight phases. Results and Discussions This study conducts a series of experiments on a dataset of atmospheric inertial navigation data for fixed-wing aircraft to predict the inertial satellite composite altitude in the event of satellite signal loss, using inertial pressure altitude as the reference baseline. Several comparative experiments are designed to comprehensively evaluate the LTCA–TCN altitude prediction algorithm proposed herein. Three subsets of data corresponding to different flight phases are utilized for altitude prediction, and the performance is compared to commonly used convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), gated recurrent units (GRU), TCN, and the LTCA–TCN algorithm proposed in this study. Experimental results showed that the LTCA–TCN algorithm outperforms other comparative algorithms in root mean square error (RMSE) and average Score metrics. It achieves the best RMSE and Score across all three phases. Compared to RNNs and their variants, the LTCA–TCN algorithm yields superior prediction results while maintaining a simpler structure and requiring fewer computational resources. In addition, compared to the baseline TCN algorithm, the proposed LTCA–TCN algorithm reduces RMSE by 1.43 m and lowers Score by 0.08. Particularly in the cruise phase, the RMSE reaches 7.97 m, within a 10 m range, demonstrating high prediction accuracy. Therefore, the LTCA–TCN algorithm indicates significant advantages in multi-stage altitude prediction tasks. Specific GPS satellite signal loss scenarios are simulated to evaluate the LTCA–TCN algorithm’s performance. Corresponding periods of satellite signal loss are set for the ascent, cruise, and descent phases to predict the Inertial Satellite Composite Altitude during these periods. Experimental results indicated that the LTCA–TCN model exhibits good fitting capability, effectively capturing different change modalities with high flexibility and adaptability in each phase. A relative error ratio is calculated to measure the extent to which the predicted altitude is closer to the actual value of the inertial satellite composite altitude compared to the inertial pressure altitude provided by INS. The relative error ratios of the LTCA–TCN model for all three phases are within 1, indicating that the model’s predictions are generally more accurate than the inertial pressure altitude.Conclusions The results indicated that the LTCA–TCN model achieves high prediction accuracy across multiple stages, outperforming commonly used neural network algorithms in the field and providing optimal predictive performance. This model can offer more reliable multi-stage altitude indications for aircraft in the event of satellite signal loss.关键词：aircraft;satellite signal;altitude prediction;attention mechanism;temporal convolutional neural network
- 32
- |
- 59
- |
- 0
发布时间：2024-11-22 - “在聚合物挤出领域，专家提出了基于多源数据融合与CNN-LSTM的熔体密度监测方法，显著提高了实时监测精度，为聚合物质量控制提供技术支持。”摘要：Objective This study addresses the challenging task of real-time monitoring of melt density during polymer extrusion, specifically for the polycarbonate-acrylonitrile-butadiene-styrene (PC/ABS) blend system, a critical parameter profoundly impacting the quality of the final product. Ensuring precise control over melt density is imperative for achieving desired product characteristics and maintaining process stability in polymer blending operations.Methods The research proposes a novel methodological framework that integrates multi-source data fusion with a convolutional long short-term memory (LSTM) neural network architecture to address this challenge. Using an independently developed multi-source sensory data acquisition device, three distinct sensor modalities, near-infrared, Raman, and ultrasound, are strategically positioned at the die head of the extruder to provide real-time data streams. These sensors capture vital insights into the dynamic changes occurring within the polymer melt during the extrusion process. The proposed model effectively learns the intricate mapping relationship between sensory data and melt density by amalgamating these multi-source sensory inputs and using the feature extraction capabilities of convolutional neural networks and the temporal dependencies modeling capabilities of LSTM networks.Results and Discussions The application of the proposed method demonstrates significant efficiency in real-time monitoring of polymer melt density by monitoring the melt density during the PC/ABS blending extrusion process. Empirical evaluations reveal a root mean square error (RMSE) of 0.975 g/cm3 and a coefficient of determination (R2) value of 0.0063, underscoring the superior predictive accuracy of this approach compared to conventional methods. In addition, the proposed method exhibits robustness in handling the inherent complexities and variabilities in polymer extrusion processes, thus offering a reliable solution for ensuring product quality and process efficiency. The average prediction time for ten inputs is 1.523 5 s, highlighting its suitability for real-time monitoring in actual production environments. Conclusions The proposed approach facilitates accurate and timely monitoring of melt density during polymer extrusion and represents a significant advancement in polymer processing monitoring by harnessing the power of multi-source data fusion and advanced neural network techniques. This enables manufacturers to make informed decisions in real time, enhancing product consistency, minimizing defects, and optimizing process parameters for improved productivity and cost-effectiveness.关键词：polymer extrusion processing;melt density;multi-sensor data fusion;CNN–LSTM;online monitoring
- 68
- |
- 43
- |
- 0
发布时间：2024-11-22 - “在炼钢生产领域，专家采用冶金机理和贝叶斯优化XGBoost方法建立钢液温度预测模型，实现钢液温度精准控制，降低生产成本，提高VD炉精炼效率。”摘要：Objective The vacuum degassing (VD) furnace is an essential piece of equipment for producing high-quality steel. Unlike other furnaces, the VD furnace lacks a heating function, leading to a significant drop in the temperature of molten steel during the refining process. If the refining endpoint temperature of molten steel is excessively high, it results in energy waste and can even disrupt the continuous casting process. If the endpoint temperature is too low, the molten steel must be reheated in the ladle furnace (LF), severely impacting production efficiency. Therefore, the refining endpoint temperature significantly influences the quality of molten steel, production efficiency, and continuous casting. In recent years, adopting new technologies such as machine learning and big data has become crucial for transforming and upgrading steelmaking plants, facilitating the shift towards intelligent manufacturing. This study proposes a method that combines metallurgical mechanism analysis, data analysis, and machine learning to develop a temperature prediction model for VD furnace refining. Methods Initially, the key factors influencing the endpoint temperature of molten steel are identified through metallurgical mechanism analysis of the VD furnace. Then, production data from the steelmaking plant are processed following operational procedures to eliminate missing and abnormal data. The 3σ principle is applied to preprocess the actual production data, removing outliers to improve data quality and ensure the accuracy of subsequent model training and prediction. Pearson correlation analysis is then employed to discard factors with a minor impact on the molten steel temperature, helping to determine the input variables for the prediction model. The dataset is randomly shuffled, with 80% selected as the training set and the remaining 20% as the testing set. Following this, based on metallurgical mechanism (MM) analysis, the initial feature importance in the input variables of the extreme gradient boosting (XGBoost) model is partially amplified. Finally, the model’s prediction accuracy is further enhanced by optimizing the hyperparameters of XGBoost through Bayesian optimization (BO) algorithms, resulting in the development of MM–BO–XGBoost models. Results and Discussions This study employs grid search (GS) and random search (RS) for hyperparameter optimization of the model to compare the results to BO hyperparameter optimization. The prediction of molten steel temperature is performed using the metallurgical mechanism model, multiple linear regression model, and back-propagation neural network model as comparisons, demonstrating the superior performance of the MM–BO–XGBoost model. The results indicated that BO hyperparameter optimization is the most effective, providing the model with the best performance and higher prediction accuracy than the other two optimization algorithms. In addition, this model also exhibits good training efficiency, with the training time being slightly higher than RS and significantly shorter than that of GS. The optimal hyperparameter combination is determined to be max_depth = 7, learning_rate = 0.05, n_estimators = 345, with a model training time of 16.84 seconds. The fluctuations in the actual VD furnace refining endpoint molten steel temperature similarly impact the prediction errors of the MM–BO–XGBoost model and the other three existing models. Among the heats with endpoint molten steel temperatures below 1530 ℃, the mechanistic model predicts more heats with high molten steel temperature values, while the other three models predict fewer such heats. However, when the endpoint prediction temperature of molten steel exceeds 1530 ℃, the prediction accuracy of the mechanistic model diminishes, with many heats having prediction errors exceeding ±10 ℃, and a greater number exceeding ±20 ℃. The MM–BO–XGBoost model performs best, achieving the highest R2 of 0.909, the lowest RMSE of 6.741 ℃, and the lowest MAE of 5.114 ℃. The hit ratios of this model are 87.81% and 96.42% for ranges of [–10,10] and [–15,15], respectively. The MM–BO–XGBoost model demonstrates excellent training efficiency and prediction performance.Conclusion The analysis of the mechanism of the VD furnace refining process and Pearson correlation analysis are conducted, and the input variables for the prediction model of VD furnace endpoint liquid steel temperature are identified. Following the metallurgical mechanism, the characteristic importance of XGBoost input variables is partially enhanced, and the model’s hyperparameters are optimized using Bayesian optimization. Therefore, the MM–BO–XGBoost model is developed with strong applicability and high accuracy. This model is practical for precisely controlling molten steel temperature at the end of the VD furnace, ensuring continuous casting progression, and enhancing production efficiency. The input variables for the MM–BO–XGBoost model are identified as follows: initial temperature of molten steel, amount of calcium wire added, high vacuum holding time, amount of aluminum wire added, static stirring time, feeding time after vacuum break, vacuum degree, weight of molten steel, vacuum pumping time, argon gas flow during vacuum pumping, argon gas flow during high vacuum maintenance, and ladle arrival waiting time. The MM–BO–XGBoost model exhibits the best prediction performance of hyperparameters obtained through the Bayesian optimization algorithm. Compared to the traditional metallurgical mechanism, multiple linear regression, and BP neural network models, the MM–BO–XGBoost model in this study shows the best prediction effect.关键词：VD furnace refining;steel temperature prediction;mechanism analysis;MM–BO–XGBoost model
- 56
- |
- 66
- |
- 0
发布时间：2024-11-22 - “在炼钢连铸排产领域，专家提出了基于“炉–机对应”策略的灰狼优化算法，有效解决了炉次等待时间问题，为复杂排产提供解决方案。”摘要：Objective The steelmaking plant’s production is characterized by multiple varieties, small batches, and multi-specification orders, which, coupled with a complex workshop layout, lead to low production efficiency in the steelmaking–continuous casting section and an unreasonable production schedule. It becomes evident that random device assignments result in low device operation efficiency and poor coordination by formulating different scheduling plans based on orders and considering the significant differences in operation cycles between upstream and downstream processes/devices. This misalignment often causes ladles to wait excessively before processing, leading to production delays and potential interruptions. Thus, it is essential to consider the operating cycles of steelmaking, refining, and continuous casting, as well as the transportation time between different processes in the workshop, to generate a reasonable production schedule.Methods When the number of casts, steel grades, and heat sequences are known, excluding emergencies such as rush orders and device failures, a steelmaking–continuous casting scheduling model is established with the minimum waiting time as the objective function. This model selects the average transportation time for ladles between devices. Although the refining process offers some buffering capacity, the heat transported along the same process path is not identical in actual production; they are treated as identical in the production schedule. The buffering capacity mainly adjusts the time for different situations; for example, if the transportation time for a particular heat is too long, the refining time can be appropriately shortened to ensure the smooth progress of the production schedule. The grey wolf optimization algorithm, enhanced by the “furnace–caster matching” strategy, addresses the issue that the algorithm can be easily affected by the initial population and prone to local optima. The “furnace–caster matching” strategy includes two main components: 1) When the casting time exceeds the refining time, one refining furnace is sufficient to supply the molten steel to one continuous caster, achieving a one-to-one match between refining and continuous casting processes; otherwise, to ensure continuous pouring, other refining furnaces must allocate a certain amount of molten steel. 2) When the casting time exceeds the converter time, one converter can supply enough molten steel for one continuous casting, establishing a one-to-one match between steelmaking and continuous casting; otherwise, additional converters are required to allocate molten steel to ensure continuous operation. This “furnace–caster matching” mode assigns devices based on steelmaking, refining, and continuous casting operation cycles, effectively avoiding random assignments. A waiting time constraint of 30 minutes is set. If the waiting time for heat exceeds 30 minutes before a process, that heat and subsequent heat are recalculated. The heat is skipped if the recalculated waiting time still exceeds 30 minutes. In this scenario, the greater the number of heats that conform to the “furnace–caster matching” strategy, the better the alignment between pre- and post-processes, enhancing the operational efficiency of each process. A weight coefficient method is employed when updating the positions of individual gray wolves. The individual gray wolves aligning with the “furnace–caster matching” strategy achieve higher positions and lead the pack closer to the potential optimal solution. Under the same constraints, heuristic algorithms, genetic algorithms, and enhanced gray wolf optimization algorithms are utilized to solve the model, and the outcomes are compared to highlight the advantages of different approaches.Results and Discussions In a steelmaking plant where the production operation mode involves four furnaces and three caster machines, which account for more than 80% of the output, C# and MATLAB are utilized to implement heuristic, genetic, and improve gray wolf algorithms to solve ten calculation examples, respectively. The heuristic algorithm exhibits the longest waiting time for solution results. This longer duration is attributed to significant deviations from the pouring start time by the heuristic algorithm to resolve device resource conflicts, making it less suitable. In contrast, based on “furnace–caster matching”, the gray wolf optimization algorithm reduces the total waiting time of furnaces by an average of 20% and decreases the proportion of furnaces experiencing a waiting time of over 30 minutes before the process by 3%. In addition, the matching between the furnaces and caster machines shows improvements. For instance, the No. 4 refining furnace’s matching with the No. 4 continuous casting production line increases the proportion of laminar flow “one-to-one matching” molten steel from 45% to 51%.Conclusions Addressing issues such as complex production processes and inadequate process/device connectivity and matching, which arise from demands for multiple varieties, small batches, and multiple specifications, a scheduling model is developed to minimize waiting time in the steelmaking–continuous casting section. Various algorithms are applied to resolve the problem. The results demonstrated that the improved gray wolf optimization algorithm, based on the “furnace–caster matching” mode proposed in this study, outperforms both the heuristic and genetic algorithms. The production schedule generated by this algorithm reduces the cross-supply of molten steel and improves the waiting times, furnace–caster articulation, and the degree of “one-to-one matching” between furnaces and caster machines. In addition, the laminar flow operation efficiency of the equipment is effectively enhanced, offering a viable solution for complex production scheduling in steelmaking plants.关键词：steelmaking–continuous casting;production scheduling;gray wolf algorithm;furnace–caster matching
- 41
- |
- 93
- |
- 0
发布时间：2024-11-22 - “在氢气压缩机领域，专家提出了一种非直接测量方法，有效提取压缩周期参数，为状态监测和故障诊断提供基础参数。”摘要：Objective This study aims to address significant challenges associated with the complex vibration signals in hydrogen compressors, which are susceptible to noise interference. This interference complicates the extraction of accurate compression cycle parameters. It develops and validates a novel non-direct measurement method that enhances the reliability and efficiency of parameter extraction in liquid-driven reciprocating hydrogen compressors, which are crucial for operational efficiency and diagnostic accuracy in hydrogen fuel systems.Methods The proposed method integrates Variational Time-Domain Decomposition (VTDD), Hilbert transform, and autocorrelation functions to enhance signal analysis and parameter extraction in noisy environments. The approach commences with developing an elaborate mathematical model that simulates dynamic interactions within the compressor, specifically focusing on piston impacts at cylinder limit positions. This simulation is pivotal for determining the intrinsic mechanical behaviors characteristic of the compression cycle. The model facilitates the extraction of clean piston impact signals from a noisy background using the VTDD algorithm, isolating pure signal components essential for subsequent analyses. The next step involves applying the Hilbert transformation to these isolated signals for envelope demodulation, enhancing signal clarity by amplifying its amplitude modulations, which are pivotal for accurately identifying cycle parameters. The amplitude sequences derived from the envelope are then examined using autocorrelation functions. This assists in precisely identifying piston impact periodicity and enables the accurate calculation of compression cycle parameters.Results and Discussions The effectiveness of the proposed method is rigorously validated through simulations and real-world applications in a liquid-driven reciprocating hydrogen compressor under various operational conditions. The method in simulation tests demonstrated an 88.89% success rate in precisely extracting compression cycle parameters across nine different signal-to-noise ratios, exemplifying its robustness against environmental noise and precision in signal analysis. In practical applications on actual compressor setups, the method achieves a 100% success rate in parameter extraction, confirming its effectiveness and reliability. In addition, a comparative analysis with traditional methods such as spectral, cepstral, envelope spectra, and autocorrelation indicated that the novel method surpasses these conventional techniques in terms of extraction success rates and computational efficiency, establishing it as a superior choice for real-time monitoring and diagnostics in critical hydrogen energy applications.Conclusions This study successfully develops and validates a non-direct measurement method that significantly enhances the process of extracting compression cycle parameters in hydrogen compressors. The method provides a comprehensive and effective solution for monitoring the operational status of hydrogen compressors, which is vital for ensuring system reliability and safety by integrating VTDD, Hilbert transform, and autocorrelation functions. The high accuracy and robustness of the method under diverse operational conditions underscore its applicability across different sectors within the hydrogen energy industry, offering a dependable tool for proactive maintenance and efficient operation of energy systems. Finally, this advancement promotes the broader adoption and optimization of hydrogen technologies, contributing to sustainable energy solutions and the advancement of green technology initiatives.关键词：compression cycle;liquid-driven reciprocating compressor;vibration analysis;hydrogen energy
- 51
- |
- 108
- |
- 0
发布时间：2024-11-22
ANOMALY DETECTION AND SAFETY CONTROL OF CYBER-PHYSICAL SYSTEMS FOR MAJOR EQUIPMENT
- “在高压电脉冲岩体破碎领域，专家建立了电-热-力耦合数值模型，揭示了岩体破碎过程，为解决岩体破碎问题提供解决方案。”摘要：This study develops a coupled multi-physics field numerical model of rock breaking under the action of high-voltage electrical pulses based on electrical, thermal, and mechanical theories. Additionally, a coupled electro-thermal-mechanical numerical model that considers electrical breakdown is proposed to describe the fragmentation process of rock under high-voltage electrical pulses. The random distribution model is employed to simulate potential conductive mineral particles in the rock, and the formation of plasma channels during the electrical breakdown process is comprehensively analyzed. The accuracy of the numerical model is verified by comparisons with the experiments. The calculation results indicate that the breakdown field strength is a critical factor in the formation of plasma channels. The rock-breaking process under the influence of high-voltage electrical pulses can be divided into three stages: the initial stage of plasma channel formation, the stage of plasma channel expansion, and the stage of subsequent plasma channel formation. Prior to the formation of the plasma channel, the temperature and stress were approximately 500 K and 10–2 MPa, respectively; when the plasma channel formed, the temperature and stress increased abruptly within a short duration, with the temperature reaching 103 K and the stress reaching 10 MPa. The temperature and stress at the plasma channel continued to rise as the electrical pulse persisted. When the maximum stress exceeded the critical stress strength of the rock, the rock fractured; concurrently, the channel continued to branch and expand, causing extensive damage to the rock. The conductive mineral particles facilitate the formation of plasma channels, and the denser the particle distribution, the more favorable the channel formation. The longer the duration for the pulse voltage to reach its peak, the relatively faster the rate of damage in the rock-breaking process. The calculation results of the numerical model improve the understanding of the rock-breaking process during the high-voltage electrical pulse.关键词：high-voltage electrical pulse;electro–thermal–mechanical coupling;conductor mineral particles;plasma channel;rock breaking
- 32
- |
- 126
- |
- 0
发布时间：2024-11-22 - “在桥梁和抗爆结构领域，专家利用数字图像相关技术研究超高性能混凝土在冲击荷载下的抗拉性能，建立了极限抗拉强度的动态提高因子模型，为提高结构抗冲击能力提供新思路。”摘要：Objective Ultra–high–performance concrete (UHPC) is widely used in long-span bridges, airport pavements, and explosion-resistant structures, which are often subjected to high strain rate impact loads during service. Given that the tensile properties of concrete are generally weaker than its compression properties, and tensile failure is a main damage mechanism in concrete, it is crucial to investigate the tensile properties of UHPC under impact splitting tensile load. Currently, research on UHPC primarily focuses on materials, mixing ratios, and mechanical properties under static load, with less focus on dynamic impact and microscopic damage mechanisms.Methods Digital image correlation (DIC) is an innovative optical detection technology that monitors the deformation and displacement of a test object's surface by collecting grayscale digital images and analyzing the changes over time. A split Hopkinson pressure bar is utilized to conduct dynamic tensile tests at different strain rates. The damage and crack development of the specimen were analyzed, and the crack growth rate is measured and calculated using DIC technology. The UHPC material used in the experiment is steel fiber ultra–high–performance concrete. The experiment utilized a 100 mm large diameter split Hopkinson compression rod as a dynamic loading device, Different strain rates were applied by adjusting the impact air pressure, with three repeated specimens are selected under different air pressures to minimize errors. Based on one-dimensional stress wave theory, the stress, strain, and strain rate of the specimen under dynamic impact splitting tensile load were calculated from the stress wave data collected by strain gauges on the incident and transmission rods. This research explores the changes in failure mode, dynamic compressive strength, dynamic elastic modulus, dynamic dissipated energy, and damage mechanism of UHPC under different strain rates, providing theoretical support for the service performance and damage assessment of UHPC.Results and Discussions The failure modes of UHPC specimens under different impact strengths show that: as the strain rate increases, the degree of fracture in the specimens becomes complete; the addition of steel fibers results in a significant difference in the impact fracture morphology between UHPC and ordinary concrete. From the fracture surface, it can be observed that there are numerous concrete fragments bridged by steel fibers and pulled-out steel fibers on the fracture surface, indicating that the sources of UHPC impact damage include the failure of the UHPC concrete matrix and the pulling out or breaking of the steel fibers. The splitting and tensile failure process of UHPC under different impact strengths was captured by a high-speed camera. Under impact pressure, the splitting crack first develops at the center of the specimen and gradually extends toward both ends of the loading end. The degree of expansion of the main crack increases with the continuous increase in impact strength. Simultaneously, some secondary cracks develop near and parallel to the main crack as the main crack expands, demonstrating the bridging effect of steel fibers in the damage process of UHPC under impact load. The strain cloud maps of specimens under different impact strengths and the curves of the main crack width of specimens under different impact strengths as they expand over time were analyzed based on DIC technology. The above DIC analysis explains that the higher the impact strength, the faster the crack development speed, and there are distinct differences in the damage characteristics of specimens under different impact strengths. The stress-strain curves of UHPC under different impact loads show that: the peak strain rate during the impact process of the specimen increases with the increase in impact load. As the peak strain rate increases, the dynamic tensile strength (i.e., stress peak) of UHPC also increases. At high strain rates, the dynamic tensile strength of the concrete specimens is significantly higher than the quasi-static tensile strength. Unlike ordinary concrete, which exhibits splitting failure after the stress reaches its peak, the rate of stress reduction in UHPC gradually slows down, and a platform segment appears. This phenomenon demonstrates the bridging effect of steel fibers in the damage process of UHPC under impact loads. By calculating the dynamic improvement factor (Dft) under different strain rates and comparing it with the concrete Dft curve provided by existing research, it is evident that the strain rate sensitivity of UHPC’s ultimate tensile strength is higher than that of ordinary concrete. Based on the law of conservation of energy, the dissipation energy and energy dissipation ratio of UHPC under different strain rates are calculated. The results showed that as the strain rate increased, the dynamic dissipation energy of UHPC also increased; the energy dissipation ratio shows a trend of first increasing and then decreasing with the increase in strain rate.Conclusions The research results indicate that: 1)The impact resistance of UHPC is significantly enhanced compared to ordinary concrete. UHPC exhibits high strength and strong deformation capability under impact loads, and its performance in dissipating impact energy is superior. There are two ways for UHPC to dissipate impact energy: one is based on the failure of the concrete matrix, and the other is the pulling out or breaking of the steel fibers. 2)As the impact strength increases, the degree of failure of UHPC gradually increases, and the extension of the main crack along the center of the specimen continues to increase until the specimen completely fractures. Except for the main crack, the development of secondary cracks is significant during the failure of UHPC with strain rates in the range of 60 to 80 s−1; when the strain rate is higher than 100 s−1, the secondary crack development of UHPC is weaker. 3)The dynamic ultimate tensile strength of UHPC increases with the increase in strain rate. The strain rate sensitivity of UHPC’s ultimate tensile strength is higher than that of ordinary concrete. As the strain rate increases, the dynamic dissipation energy of UHPC also increases, but the energy dissipation ratio experiences a trend of first increasing and then decreasing. 4)These analysis results fully demonstrate that UHPC is still a high-performance concrete material suitable for resisting impact loads and dissipating impact energy under dynamic splitting and tensile loads.关键词：high–performance concrete;strain rate;impact resistance;tensile strength
- 50
- |
- 131
- |
- 0
发布时间：2024-11-22 - “最新研究显示，钢板阻尼连接的装配式边柱减震节点具有良好承载和耗能性能，损伤可集中于连接处，为减震结构设计提供新思路。”摘要：This study conducts pseudo-static tests on two prefabricated exterior damping joints equipped with steel plate damper connection to investigate the effects of different energy dissipation mechanisms of the steel plate dampers on the mechanical behavior of prefabricated beam-column joints in steel frames. It reports the failure modes, hysteresis curves, skeleton curves, slip curves, and strain development. Additionally, it analyzes the bearing capacity, ductility, energy dissipation capacity, and moment-rotation behavior. The results demonstrated that the prefabricated exterior damping joints with steel plate dampers can develop substantial bearing capacity and energy dissipation. The beams, columns, and joint cores remained undamaged, with damage concentrated in the steel plate dampers. The prefabricated exterior damping joints with bearing energy dissipation steel plates with circular openings (BSDC) failed due to the rupture of the steel plate damper, whereas those with friction energy dissipation steel plates with long slotted openings (FSDC) exhibited pronounced sliding behavior and residual bending deformation in the steel plate dampers. The bearing capacity of FSDC was lower and decreased slightly under large displacements due to the loss of friction coefficient and bolt pretension under cyclic loading. However, the hysteresis curve was more plumped and exhibited relatively stable hysteresis behavior. The bearing capacity of BSDC decreased abruptly with the rupture of the steel plate dampers. The energy dissipation capacity and ductility of FSDC were superior to those of BSDC, with 88% of the energy dissipation consumed by friction steel plate dampers, compared to 67% by bearing steel plate dampers. These findings indicated that energy dissipation concentration is achieved, and the sliding behavior of steel plate dampers is beneficial for enhancing both energy dissipation capacity and deformation capacity.关键词：prefabricated exterior damping joint;steel plate dampers;sliding behavior;damage control;energy dissipation concentration.
- 25
- |
- 43
- |
- 0
发布时间：2024-11-22 - “在岩石稳定性分析领域，专家通过光学扫描技术和直剪试验，建立了不同岩石强度组合下结构面峰值抗剪强度计算公式，为复杂地层岩石稳定性分析提供新方法。”摘要：Natural structural faces often exhibit inconsistent lithology on either side of the rock mass. The study of the shear strength of such heterogeneous structural surfaces facilitates the analysis of rock stability in complex stratigraphy. Initially, an optical scanning technique is utilized to obtain the point cloud data of natural structural surface morphology and to construct the structural surface mesh model. Subsequently, the model is reconstructed in three dimensions to cast multiple artificial specimens with varying heterogeneous structural surfaces. Direct shear tests are then conducted under different conditions of normal stress and shear direction. Finally, numerical simulations are performed using the simulation software FLAC3D. Based on the JRC–JCS (joint roughness coefficient–joint wall compressive strength) model, the two-dimensional profile method is employed to quantify the structural surface roughness coefficient JRC, and the combined wall strength CJCS (combined joint wall compressive strength) and wall strength coefficient ratio are introduced to reflect different strength combination cases. The results demonstrate that the numerical simulations align more closely with the shear experimental results. When the compressive strength of either the higher or lower side of the structural face is determined, the lower strength side mainly controls the peak shear strength, while the higher strength side enhances it. The effect of
$\lambda $ 关键词：heterogeneous structural surfaces;direct shear tests;numerical simulations;peak shear strength;strength ratio interval- 59
- |
- 211
- |
- 0
发布时间：2024-11-22 - “在地下特殊施工领域，专家基于Peck公式推导冻隆公式，为预测冻胀变形提供理论基础和计算参考。”摘要：Objective The artificial ground freezing method is widely applied in the construction of tunnels through highly saturated soft soil layers. However, the phenomenon of frozen soil expansion can adversely effect the safety of overlying structures. As the number of projects involving this freezing method beneath such structures continues to increase, scholars are increasingly concerned about the deformation of these structures caused by frost heave. Despite this, the calculation methods and interaction laws governing the deformation of overlying structures during the freezing process remain unclear, highlighting the need to predict surface and structural deformations. This study aims to address the frost heave issue related to the macroscopic manifestation of water migration in frozen soil.Methods It derives formulas and parameter values by using Peck’s equation to calculate the deformation of overlying strata induced by frost heave. Consequently, it establishes an appropriate frost heave formula to compute the amount of frost heave generated during the freezing process. Based on construction experience, the study provides a range of reduction coefficients for frost heave in various cities. Through moisture migration experiments, the velocity of water migration during freezing is determined, and an empirical calculation method for the radius of the freezing front is derived. Additionally, a formula to calculate the increase in volume of frozen soil is developed. By comparing and analyzing the dimensions of frost heave mounds under different freezing wall diameters (3.0, 4.8, 5.2, and 6.0 m), it is discovered that the sizes of frost heave mounds produced by different diameters are nearly identical.Results and Discussions It is found that the burying depth of the freezing wall (z0) has a significantly greater impact on the shape of the frost heave mound curve than the diameter of the freezing wall. Thus, the burying depth of the freezing wall emerges as a crucial factor influencing the size of the frost heave mound in overlying strata. Drawing inspiration from Mair’s approach, a calculation formula suitable for computing the deformation of overlying strata at different depths induced by frost heave is formulated. This formula enables the calculation of the width of the frost heave mound based on the burying depth of the freezing wall (z0) and the depth of the structure’s base plate (z), and proposes empirical values for k1 and k2. Utilizing measured data from a freezing project in Zhengzhou, the volumetric increase rate of soil moisture during the freezing process is calculated, revealing that the increase in frozen soil volume caused by water migration is 42 times greater than the increase in situ moisture-frozen soil volume. The calculated values obtained using the frost heave formula closely approximate the measured values, satisfying the requirements for safety evaluation and analysis during the construction process. With the continuous expansion of urban subway tunnel construction in highly saturated soft soil layers, the number of projects involving the artificial ground freezing method beneath overlying structures is expected to increase annually.Conclusions The research findings provide calculation methods for estimating the deformation of overlying structures in freezing-undercrossing engineering projects, serving as effective references for freezing design and construction. By adopting targeted measures such as structural pre-reinforcement or frost heave control, significant risks, including damage to overlying structures during the freezing-under-crossing process, can be avoided. Furthermore, the research outcomes establish a theoretical foundation and calculation reference for the technical research and application of the artificial ground freezing method in high-risk underground engineering fields.关键词：artificial ground freezing method;volume increment of frozen soil;moisture migration;frost heave;Frost heave formula
- 42
- |
- 54
- |
- 0
发布时间：2024-11-22 -
Experimental Study on Curing Strength and Freeze-thaw Characteristics of Supersulphate Saline Soil AI导读
“在盐渍土地区渠道衬砌结构破坏问题研究中，专家通过联合固化剂提升盐渍土强度，降低环境温度敏感性，为渠道地基加固提供解决方案。”摘要：Insufficient strength and salt–frost heave failure are the primary causes of channel lining structure failure in saline soil areas. This study conducts strength and microscopic analysis of the supersulphate saline soil treated with a combined curing agent to address the problem of inadequate strength of saline soil and reduce its sensitivity to environmental temperature. The relationship between the curing agent’s dosage and the strength is established to assess the curing effect, and the microscopic pore structure is examined using a mercury injection test. The variation law of frost heave force and quantity is revealed through freeze-thaw cycle testing. The results demonstrated that the strength of solidified saline soil significantly increases, and the hydration products enhance the cementation between particles. As the curing agent’s dosage increases from 0 to 30%, the porosity decreases gradually, the pore size transitions from super-large pore to micro-small pore, and the fractal dimension of the calculation model increases incrementally. The Menger and Neimark model shows multiscale fractal characteristics. In the range of 0.1～1.0 μm, the fractal characteristics are not obvious. The fractal dimension (DT) of the thermodynamic model varies within a reasonable range of 2.824 to 2.849, accurately representing the fractal characteristics of the pore surface. After 10 freeze-thaw cycles, the salt–frost heave capacity of solidified saline soil decreased by 117% compared to uncured saline soil. The fluctuation range of salt–frost heave force in solidified saline soil gradually increased with the number of freeze-thaw cycles. The salt–frost heave force of solidified saline soil exhibited periodic attenuation. According to engineering practice, it is recommended that the proportions of cement, fly ash, silica fume, and desulfurized gypsum should be 1.00∶1.67∶0.83∶1.00 and that the dosage of the curing agent should be 30%.关键词：supersulfate saline soil;curing strength;pore fractal characteristics;salt–frost heave;salt–frost heave force- 41
- |
- 178
- |
- 0
发布时间：2024-11-22 - “在传统建筑抗震领域，研究者通过结合钢-混凝土结构和黏滞阻尼器，显著提升了双枋-柱节点的力学性能，为传统建筑抗震设计提供新思路。”摘要：Objective Chinese traditional-style architecture is an area of exploration and innovation with promising prospects for popularization and application. Although Chinese traditional-style structures resemble ancient Chinese structures in external appearance, the materials, force transmission mechanisms, and seismic behaviors differ considerably. Modern building materials and construction technologies have been adopted in traditional Chinese structures; however, their structural properties, mechanical properties, and analytical methods differ from those of modern structures. Research shows that, compared to contemporary reinforced concrete column-beam joints, the structural mechanical properties of Chinese traditional-style architecture are inferior.Methods Consequently, three specimens are designed to study the mechanical behavior of Chinese traditional-style architecture dual-lintel-column joints with a steel-concrete composite structure and viscous dampers. Two specimens with a viscous damper are classified as controlled structures, and one specimen without a viscous damper is classified as a non-controlled structure. Alternating reciprocating sine wave loading and reciprocating harmonic load tests are performed on all specimens under dynamic experimental conditions. The failure characteristics and mechanisms of the specimens are studied, and mechanical performances such as the load-displacement hysteretic curve, skeleton curve, bearing capacity, deformation performance, energy dissipation capacity, and stiffness degradation of the dual-lintel-column joints were analyzed.Results and Discussions The results show that the mechanical properties of Chinese traditional-style architecture dual-lintel-column joints can be considerably improved using a steel-concrete composite structure and properly setting viscous dampers. Based on the comparative analysis, the entire loading process for all specimens can be divided into three stages: the non-cracking stage, seam-working stage, and failure stage. For the ABS–1 specimen, in the first stage, the hysteresis curve was essentially linear with no considerable residual deformation when unloaded to zero. In the second stage, the cracks developed considerably at the connecting parts of the lintel-column, forming through cracks. The nonlinear characteristics of the hysteretic curve became obvious, with residual deformation and stiffness and strength attenuation when unloaded to zero. In the final stage, the concrete at the connecting part of the lintel column was crushed and spalled, exposing the rebar. The test was terminated because the specimen had reached a geometrically unstable state. For the ABS–2 and ABS–3 specimens, the failure modes were essentially the same. In the first stage, the loading and unloading curves of the hysteresis loop almost coincided, and the elastic characteristics of the components were notable, with no substantial residual deformation when unloaded to zero. In the second stage, some cracks appeared in the connecting parts of the lintel column, but no through cracks were observed. Residual deformation and stiffness and strength attenuation occurred when unloaded to zero. During the loading process, the specimen and the damper worked together. In the final stage, some through cracks appeared in the connecting parts of the lintel column, exposing the steel bars. The specimens remained geometrically stable when the test was terminated, as the viscous damper provided support. The results show that the failure modes were of the shear-flexural type, and the failure mechanism for all specimens was the beam hinge mechanism, which meets seismic fortification requirements. The results of the hysteresis curve analysis show that, with the increase in the controlled displacement, the area enclosed by the hysteresis loop gradually increases, and the horizontal resistance becomes nonlinear with the corresponding horizontal displacement. The strength and stiffness degradation of all specimens were notable, with small-scale concrete crushing. The load decreased for all specimens, but the degradation in the load for the controlled structure was milder compared to that of the non-controlled structure, while the degradation in the load for the joint with a damper was slower than that of the joint without a damper. This phenomenon was caused, to some extent, by the installation of a viscous damper, which improved the load-carrying capacity and delayed damage development. During the loading process, the skeleton curves can be divided into three stages: elastic, elastoplastic, and plastic stages. There was no considerable yield point in the backbone curves, reflecting the gradual development of yield from the microcosm to the macrocosm. Based on the test results, the longitudinal steel rebar yield was considered the initiation of yield. Compared to the ABS–1 specimen, the joint with a damper had a more gradual load drop in the descending section after the skeleton curve reached its peak. The bearing capacity of the joint with a damper was considerably superior to that of the joint without a damper. The bearing capacity and ductility performance were significantly increased by the attached viscous damper. Consequently, the combination of a viscous damper and Chinese traditional-style architecture can be considered a superior method for improving seismic performance and protecting the main structure. Compared to the joint without a damper, the bearing capacity and deformation properties of the joint with a damper increased significantly. The peak load improved by 55.1% for ABS–2 and 32.8% for ABS–3 under sine wave load, and by 66.4% for ABS–2 and 34.4% for ABS–3 under harmonic load. The displacement ductility coefficient improved by 13.7% for ABS–2 and 15.8% for ABS–3 under sine wave load, and by 14.4% for ABS–2 and 17.0% for ABS–3 under harmonic load. The ductility coefficient of all specimens followed the order ABS–3, ABS–2, and ABS–1, from smallest to largest. Compared to the joint bearing capacity, ABS–2 had the largest. Therefore, in actual engineering, it is essential to focus not only on the improvement of single performance parameters but also on the overall performance of the structure when selecting damper parameters. Compared to the joint without a damper under harmonic load, the equivalent damping coefficient and energy ratio for the joint with a damper increased significantly by approximately 23.0%~27.0% under the yield point, 29.1%~31.1% under the ultimate point, and 43.1%~43.8% under the failure point. Similarly, the increased range of the energy ratio was approximately 5.9%~22.1% at the failure point.Conclusions The results indicated that the viscous damper considerably improved the energy dissipation capacity, making it possible to achieve the performance objective of no damage under medium seismic events and reparability under severe earthquakes. The stiffness degradation showed an initial rapid decline, followed by a slower decline, and tended to stabilize in the later stages, which can inhibit the stiffness degradation rate of the specimens to some extent. After cyclic action under sine wave loading, the residual stiffness of the joint with a damper was higher than that of the joint without a damper. These results and proposals have reference value for the design, construction, and research of antique buildings.关键词：Chinese traditional style architecture;dual-lintel–column joint;viscous damper;mechanical performance;cyclic load
- 26
- |
- 51
- |
- 0
发布时间：2024-11-22 - “最新研究揭示土与结构物界面力学特性，构建统计损伤本构关系，为边坡桩-土界面分析提供理论支撑。”摘要：Objective Pile-soil interaction plays a critical role in slope support engineering. Since the contact surface represents the weakest link in the system, analyzing the influence of the soil shear area on the mechanical properties and the constitutive model of the contact surface between soil and structure enhances the understanding of pile-soil interactions.Methods Three sets of ring-shaped samples are initially cast to evaluate the impact of the soil shear area on the interface strength characteristics between soil and structure. Each sample has a height of 1.00 cm, an outer diameter of 6.12 cm, and inner diameters of 0 cm, 3.50 cm, and 4.98 cm, respectively. Subsequently, corresponding segments of 300-mesh sandpaper are adhered to the sample surfaces using a robust adhesive. The remolded soil is then dried, pulverized, sieved through a 2 mm mesh, and adjusted to a moisture content of 20%. It is cured for 24 hours prior to sample preparation. Different soil-structure interface samples with varying soil shear areas are then fabricated using specially designed equipment, and shear tests are conducted using the electric ZLB-1 strain-controlled direct shear apparatus manufactured by Nanjing Soil Instrument Co., Ltd. The test employs the fast shear method, with a shear rate of 0.8 mm/min and a shear displacement of 7 mm. During the tests, the soil shear area ratio (ρ) at the interface between soil and structure is controlled at 0, 0.33, 0.66, and 1.00, with normal stresses of 100, 200, 300, and 400 kPa, respectively.Results and Discussions The peak strength of the soil-structure interface increases linearly with increasing normal stress and soil shear area ratio. The shear area ratio of soil significantly influences the stress-strain curve of the sample. When the soil shear area ratio is ρ = 0 or ρ = 1.00, the shear stress-displacement curve of the sample exhibits a hardening behavior. Conversely, when the shear area ratio is 0 < ρ < 1.00, the curve demonstrates a softening behavior. This primarily occurs because the soil strength exceeds the interface strength between the soil and the structure. Under various soil shear area conditions, the strength at the soil-structure interface initially derives from the soil itself. Once the soil’s shear strength reaches its maximum, the sample’s shear stress is substantially reduced, exhibiting a stress-softening phenomenon. As the soil shear area ratio at the soil-structure interface gradually increases, the shear strength at the interface approaches that of the soil. Consequently, the interface effect leads to a decrease in shear strength compared to that of the soil shear surface. The shear strength of the soil shear surface primarily arises from the interactions between soil particles, including rotation, interlocking, and biting. In contrast, the shear strength at the soil-structure interface comprises two components: one part is generated by the friction, interlocking, and biting between the soil particles and the structure’s surface, while the other arises from the interactions among soil particles near the shear zone on the structure’s surface. During the shear process of the specimen, the proportion of shear strength is contributed by inter-particle shear resistance within the soil, and the interface shear resistance undergoes dynamic changes, exhibiting variability in the shear strength mechanism at the soil-structure interface. The total damage to the soil shear surface results from loading damage, whereas the total damage at the soil-structure interface is divided into equivalent initial damage, loading damage, and coupling damage caused by the interaction of the two. The smaller the soil shear area ratio ρ, the greater the equivalent initial damage at the soil-structure interface. By establishing a damage evolution relationship between the soil shear surface and the soil-structure interface, the latter is equated to the soil shear surface with initial damage, describing the influence of soil shear area on the strength characteristics of the interface. Based on the assumption that the strength of both the soil-structure interface and the soil shear surface follows a two-parameter Weibull probability distribution during the shear process of the sample, a damage strength model of the soil-structure interface based on equivalent damage is proposed using statistical damage theory. This model primarily consists of the fitting parameter B, which controls peak strength, and the fitting parameter C, which controls softening characteristics. Compared to the soil shear plane, the pre-set interface leads to significant changes in pores and microcracks during the shear process of the soil-structure interface. Consequently, the total damage to the soil-structure interface is considerably higher than that of the soil shear plane initially, and the macroscopic performance is that the strength of the soil-structure interface is significantly lower than that of the soil shear plane. The proposed model is validated by comparing it with experimental data, demonstrating that it accurately represents both softening and hardening stress-strain behaviors at the soil-structure interface.Conclusions This study explores how the soil shear area influences the mechanical properties of the soil-structure interface and proposes a zero-thickness interface model to predict this behavior, supported by experimental evidence. This model effectively fits the nonlinear relationship at the soil-structure interface under different soil shear area ratios, making it suitable for programmed calculations in finite element software.关键词：soil–structure interface;shear strength;equivalent damage;statistical damage theory;damage model
- 55
- |
- 54
- |
- 0
发布时间：2024-11-22 - “在桥梁工程领域，专家提出了基于深度学习的摇摆自复位桥墩设计参数优化方法，为提升桥墩抗震性能提供解决方案。”摘要：Separating the pier and platform at the base of the pier and embedding the prestressed steel bars and reinforcements into the pier body facilitates the creation of a self-centering pier. The pier’s geometric and material mechanical parameters affect its seismic performance; however, the impact on critical seismic performance indices (energy-consuming capacity, residual deformation, and load-bearing capacity) remains unclear due to the coupling of design parameters. Optimizing these parameters for the rocking self-centering pier is crucial. This study proposes a deep learning-based method for optimizing the design parameters of self-centering piers. The NSGA and MOEA/D algorithms are employed to achieve a multi-objective optimization solution. The AHP-entropy weighting method considers subjective and objective factors when determining the weight coefficients among targets. The TOPSIS method is applied to rank the optimal solutions for Pareto. A finite element analysis model is developed to optimize the critical design parameters of the self-centering pier with embedded prestressed steel bars and reinforcements, considering the three seismic performance indices (residual displacement, equivalent viscous damping coefficient, and load-bearing capacity). The results demonstrated that the method can account for the randomness in the pier’s geometric and material mechanical performance parameters, harmonize multiple design objectives, and rapidly identify design parameters with the best comprehensive performance. In addition, longer unbonded reinforcements can effectively reduce residual deformation, higher initial pretension of prestressing tendons enhances structural load-carrying capacity and mitigates residual deformation, and high-grade reinforcements are advantageous for improving load-carrying capacity and reducing residual deformation. A finite element structural agent model created through the deep learning method can incorporate random parameters related to geometric and material mechanical properties to improve the model’s robustness and the self-centering pier’s multi-objective optimization efficiency.关键词：self-centering pier;deep learning;multi-objective optimization;NSGA algorithm;MOEA/D algorithm
- 110
- |
- 106
- |
- 0
发布时间：2024-11-22 - “最新研究揭示珊瑚岛礁基础设施建设中珊瑚砂变形特性，专家建立预测模型，为工程安全提供解决方案。”摘要：With the ongoing construction of military and civilian infrastructure, the safety of infrastructure in Nansha Reef and offshore marine areas in the South China Sea has become a major concern. The effect of the fine content (Fc) and the relative density (Dr) on the deformation properties of saturated coral sand has been explored through a series of drained cyclic triaxial tests. An important finding is that Fc and Dr considerably affect the development of cumulative volumetric strain (
$ \varepsilon_{\text{vd},\text{ir}}^N $ $ \varepsilon_{\text{vd},\text{ir}}^{\mathrm{s}} $ $ \varepsilon_{\text{vd},\text{ir}}^N $ $e_{{\text{sk}}}^*$ $ \varepsilon_{\text{vd},\text{ir}}^{\mathrm{s}} $ $e_{{\text{sk}}}^*$ $e_{{\text{sk}}}^*$ $ \varepsilon_{\text{vd},\text{ir}}^N/\varepsilon_{\text{vd},\text{ir}}^{\mathrm{s}} $ $e_{{\text{sk}}}^*$ 关键词：coral sand;fine content;Density relative;cumulative volumetric strain;dynamic elastic modulus- 44
- |
- 52
- |
- 0
发布时间：2024-11-22 - “在水动力学领域，专家通过理论分析与模型计算，提出了定量描述干流水位顶托强度和范围的新方法，为支流河道水流特征研究提供新视角。”摘要：Objective This study aims to accurately analyze the backwater effect of mainstream water levels on tributary flow and to effectively characterize this impact, which is essential for flood control management and navigational channel construction in tributaries. It holds significant theoretical and scientific value. The research addresses two primary challenges: managing the scattered data of water levels and discharges in tributaries and comprehensively considering changes in water levels and flow velocities to characterize the extent of the backwater effect on tributaries. It proposes a method for describing the water level-discharge relationship in tributaries using envelopes to process scattered hydrological data and constructs a backwater intensity parameter that accounts for various hydrodynamic factors.Methods The study uses the Shiguan River, influenced by the backwater effect of the Huai River, as a case study. It begins with a theoretical analysis to examine variations in the hydrodynamic characteristics of tributaries under the influence of mainstream backwater, including increased water levels, reduced cross-sectional flow velocities, and flattened water surface slopes. The research constructs a backwater intensity parameter that integrates changes in cross-sectional water levels, water surface slopes, and flow velocities. Due to the lack of observed discharge data from the mainstream, which complicates accurately fitting the water level-discharge relationship at the confluence using a single curve, this study analyzes the randomness of flood encounters between the mainstream and tributaries during flood and non-flood seasons, as well as their mutual backwater characteristics in different periods. It proposes using the upper and lower envelopes of hydrological data (matched with topographical data) and linear fitting lines to represent the water level-discharge relationship at the confluence during different periods. Subsequently, a two-dimensional hydrodynamic numerical model of the Shiguan River is established based on the measured topographical data from March 2021. The model utilizes historical discharge data from the Jiangjiaji hydrological station, with four typical discharge scenarios (57.26, 739.00, 1 380.00, 1 758.00 m3/s) and three water level-discharge relationships (upper envelope, lower envelope, linear fitting line), generating a total of 12 combinations of Jiangjiaji discharge and Sanhejian water levels. The Jiangjiaji discharge serves as the upstream boundary condition of the model, while the water level of the Sanhejian station, located near the model outlet, is the downstream boundary condition for a series of numerical simulations. Finally, by analyzing the results of the numerical model under different scenarios, the study quantitatively assesses the variations in hydrodynamic characteristics of the Shiguan River, including changes in water levels at the Jiangjiaji station, water surface slopes along the river, and flow velocities at typical cross-sections. It also qualitatively analyzed the intensity and scope of the backwater effect on the Shiguan River. Additionally, the constructed backwater intensity parameter is utilized to quantitatively analyze the intensity and scope of the backwater effect on the Shiguan River and compare it with other characterization methods, highlighting the advantages of this parameter in representing the intensity and extent of the backwater effect.Results and Discussion This study focuses on the spatial and temporal variations of hydrodynamic characteristics and backwater properties of the Shiguan River under different water level-discharge combinations, examining the main factors affecting the degree and extent of the backwater effect on the tributary. The results and discussion are as follows: 1) Under the dynamic backwater effect of the mainstream, the backwater intensity parameter, which integrates changes in water surface slopes and cross-sectional flow velocities, can comprehensively characterize the degree of backwater effect on a particular cross-section. The use of envelopes to handle the scattered water level-discharge data at the confluence allows for coverage of various water level-discharge relationship combinations under different confluence scenarios of the mainstream and tributary. 2) Under different combinations of tributary inflow and mainstream water levels, the water level of the Shiguan River rises with an increase in tributary discharge and further elevates upstream from the confluence with the increase in mainstream water levels; the water surface slope decreases with an increase in tributary discharge and further decreases with an increase in mainstream water levels; the cross-sectional flow velocity increases with tributary discharge and decreases with an increase in mainstream water levels. 3) The degree and extent of the backwater effect on the tributary are primarily influenced by factors such as tributary inflow, mainstream water levels, and proximity to the confluence. The smaller the tributary inflow, the higher the mainstream water levels, and the closer to the confluence, the stronger the backwater effect; the greater the tributary inflow and mainstream water levels, the further the extent of the backwater effect. Near the confluence, the backwater intensity parameter for the lower reaches of the Shiguan River exceeds 0.30, indicating a long-term backwater effect from the mainstream of the Huai River. 4) The method of using a comprehensive backwater intensity parameter, compared to hydrodynamic characteristic analysis methods and other characterization methods, facilitates a more intuitive analysis of the spatial and temporal variations of the backwater properties of mainstream and tributary confluences, and demonstrates good potential for wider application.Conclusion The results indicate that the proposed method of using envelopes to describe the scattered water level-discharge relationships of tributaries affected by backwater is a reasonable representation of the hydrological characteristics of tributaries during different periods. The constructed backwater intensity parameter comprehensively captures changes in various hydrodynamic parameters, intuitively reflecting the spatial and temporal variations of backwater intensity and extent. The findings provide a valuable reference for in-depth research on the hydrodynamic characteristics of tributaries affected by mainstream backwater and their numerical simulations.关键词：water flow characteristics;backwater effect;backwater intensity parameter;numerical simulation;the Shiguan River
- 64
- |
- 124
- |
- 0
发布时间：2024-11-22
CIVIL ENGINEERING
- “在大规模运动模拟领域，专家建立了ATC-kd-tree体系，为提高近邻搜索效率提供解决方案。”摘要：Objective In large-scale motion simulation problems, the search efficiency for nearest neighbor points critically influences the overall operational efficiency. This study applies correlation analysis to establish an adaptive relationship between the maximum depth of the kd-tree (dmax) and the total number of particles (N). A novel automatic termination criterion for the kd-tree, known as ATC-kd-tree, addresses the impact of the leaf node size threshold (n0) on nearest neighbor search efficiency. This method effectively addresses the challenges of recalibrating dmax as N varies, enhancing the efficiency and applicability of the kd-tree in various scenarios.Methods The ATC-kd-tree framework is designed to dynamically adjust the kd-tree structure based on the current particle count, ensuring optimal performance for efficient searches in real-time applications. This method involves a comprehensive analysis of particle distributions, enabling the algorithm to adaptively modify dmax based on the specific characteristics of the particle set at any given time. The ATC-kd-tree effectively responds to the spatial arrangement of particles, enhancing search accuracy and speed by integrating data-driven adjustments. In addition, a two-step parameter optimization algorithm that combines grid search with coordinate descent methods (GSCD) is introduced. This hybrid approach expedites the calibration of variable parameters crucial for optimizing ATC-kd-tree performance, facilitating a more precise search process. The GSCD method accelerates the pace of parameter adjustment and increases accuracy by ensuring that the most appropriate parameters are selected based on empirical evidence. A comprehensive series of experimental trials is conducted involving various particle distribution models, such as uniform, random, and clustered configurations. These trials are designed to assess the efficiency of the ATC-kd-tree and the GSCD optimization process. Key performance metrics, including search time, cache miss rates, and overall computational efficiency, are diligently monitored and analyzed. Rigorous comparisons against baseline methods, comprising traditional kd-trees and alternative sorting algorithms, are executed to ensure a thorough evaluation of the proposed techniques.Results and Discussions The study’s findings demonstrated that the ATC-kd-tree framework significantly improves the efficiency of nearest neighbor searches, especially in large-scale motion simulations characterized by dynamically fluctuating particle distributions. Experiments involving particle distribution shapes, such as rectangular, cuboidal, notched cuboidal, spherical, and annular, are frequently employed in fluid dynamics studies to corroborate their efficiency. The ATC-kd-tree achieved an impressive average reduction in search time of up to 30.3% compared to traditional unsorted kd-tree implementations across all tested configurations. When analyzing datasets comprising 40 000 and 575 000 particles, the search times exhibit significant enhancement: the ATC-kd-tree reduces the search time from 1.75 seconds with traditional methods to 1.22 seconds for the former dataset and from 7.12 seconds to 4.97 seconds for the latter. This performance improvement is particularly marked in environments with high spatial divergence among particles, where traditional methods often falter with inconsistent search paths. In addition, an average reduction of 24.2% in cache miss rates is observed when employing the ATC-kd-tree. In scenarios with larger particle counts, the cache miss rate decreases from 35% in the traditional unsorted kd-tree to 26% with the ATC-kd-tree, which directly contributes to enhanced computational efficiency, facilitating quicker data retrieval during neighbor searches. The ATC-kd-tree demonstrated exceptional adaptability to rapidly evolving particle configurations in computational fluid dynamics (CFD) simulations. In a specific experiment involving 50 000 particles exposed to external forces causing irregular movements, the ATC-kd-tree shows a 20% reduction in cache misses and a 15% decrease in overall computational time compared to traditional methods. This capacity to effectively manage irregular particle movements underscores the ATC-kd-tree’s robustness for real-time applications that demand rapid adaptability to dynamic changes. The GSCD optimization method further augments the performance of the ATC-kd-tree, as the analysis indicates that GSCD accelerates parameter calibration by 205% relative to traditional grid search methods. In experiments, the GSCD method reduces calibration time significantly, from over 120 seconds in traditional methods to approximately 40 seconds. This enhancement enables rapid adjustments to the parameters of the kd-tree and ensures that the tree remains optimally configured to the specific characteristics of the particle distributions encountered during simulations. The adaptability of the ATC-kd-tree is evident across various particle distribution scenarios. This algorithm consistently surpasses traditional unsorted kd-trees and alternative sorting methods in clustered configurations, such as the Z-index sort. In these configurations, the search time is significantly reduced, demonstrating the ATC-kd-tree’s ability to dynamically reorganize its structure based on real-time analysis of particle distributions. This capability maximizes cache utilization and minimizes cache misses. The findings indicated that the ATC-kd-tree is particularly effective in scenarios involving non-uniform particle distributions, as its capacity to adjust dmax based on the number of particles eliminates the cumbersome recalibration processes typically required by traditional methods. Hence, this leads to a smoother and more efficient search process, even as particle distributions experience significant changes. These results highlight the critical importance of incorporating adaptive techniques into kd-tree implementations to improve search efficiency in large-scale simulations. The improvements in search time and cache utilization achieved by the ATC-kd-tree provide compelling evidence of its potential to transform how nearest neighbor searches are conducted in dynamic environments.Conclusions The introduction of the ATC-kd-tree provides a valuable approach to optimizing kd-tree-based nearest neighbor searches, particularly in dynamic and large-scale motion simulations. This research aims to enhance search efficiency and deliver a scalable solution for managing varying particle distributions by integrating an automatic termination criterion with a rapid parameter optimization method. The results showed that the ATC-kd-tree can improve operational performance, reducing computational overhead and cache misses, which is crucial for real-time applications where efficiency is paramount. In addition, the principles explored in this study can extend beyond the kd-tree framework, demonstrating new avenues for research into adaptive data access techniques that can prove beneficial across various computational domains, including machine learning, computer graphics, and robotics. Future efforts will concentrate on integrating the ATC-kd-tree with advanced cache management strategies to optimize performance and investigate methods to reduce computational complexity in high-dimensional contexts, which remains a critical area of investigation. This study aims to address these challenges and broaden the applicability of kd-tree methods in real-time environments, making them more adaptable to complex and dynamic conditions. It contributes to enhancing the functionality of kd-trees and provides insights into the broader field of data structure optimization, laying a foundation for future developments in efficient data processing techniques.关键词：kd-tree;particle nearest neighbor search;adaptive;grid search;coordinate descent
- 75
- |
- 40
- |
- 0
发布时间：2024-11-22 - “在水库群优化调度领域，专家提出了结合罚函数的嵌套DPSA-POA和智能算法的约束处理方法，有效解决了高维优化问题。”摘要：With the expansion of operational scales and the refinement of time steps in optimizing cascade reservoirs, the dimensionality of decision variables in such problems can range from hundreds to thousands. In the operational optimization of cascaded reservoirs with high-dimensional decision variables, it is often essential to consider multiple complex constraints. Traditional optimization methods struggle to effectively identify feasible regions when addressing these challenges. The intelligent optimization algorithm is a multidimensional linkage random search, which boasts a vast optimization space but suffers from low optimization efficiency. Therefore, this study introduces a constraint processing approach that integrates a penalty function with nested DPSA–POA and intelligent algorithms and applies it to the optimal flood control operation problem of cascade reservoirs in the middle reaches of the Ganjiang River, with decision variables extending to 2196 dimensions. The results of the correlation analysis indicated that: 1) the nested DPSA–POA intelligent algorithm combined with a penalty function can address the high-dimensional optimization problem under varying water inflow conditions using three constraint processing methods; 2) Of the three constraint processing methods, method 2, which involves DE optimization after securing a feasible solution through nested optimization, achieves the highest convergence accuracy, though the computation time is approximately 10 h; method 3, which involves DPSA–POA optimization after securing a feasible solution through nested optimization, achieves the second highest convergence accuracy, with a computation time of about 1～3 h; 3) Existing SF, SR, PF, and EC constraint treatment strategies fail to consistently converge to a feasible solution under different water inflow conditions, and the convergence accuracy of the results, upon obtaining a feasible solution, is significantly lower than that of the method introduced in this study. Accordingly, the nested constraint processing method presented in this research can be an effective approach for high-dimensional optimization in the operation of cascade reservoirs.关键词：High-dimensional optimization problem;Constraint processing method;DPSA–POA;Intelligent algorithm;cascade reservoirs
- 10
- |
- 0
- |
- 0
发布时间：2024-11-22 - “在Wankel泵效率研究领域，专家提出了基于分项参数换算法的效率表达与换算方法，为Wankel泵设计与研发提供指导。”摘要：The Wankel pump, a novel rotor pump based on the Reuleaux triangle, can simultaneously deliver high flow and pressure with superior conveying performance. However, the current efficiency expressions and conversion theory for the Wankel pump are insufficient and do not offer guidance for designing and producing prototype pumps through hydraulic testing of model pumps in laboratories. Therefore, this study proposes a method for expressing and converting the efficiency of the Wankel pump based on the sub-parameter conversion method. Initially, the basic composition and operating principles of the Wankel pump are analyzed, and equations for the motion trajectory, velocity, and acceleration of the rotor vertex are established based on the cylinder profile. Then, the equation for calculating the head of the Wankel pump is derived from the energy conservation, and the similar relationship between the flow rate and head of the prototype pump and the model pump under equivalent working conditions is analyzed. Then, expressions for mechanical, hydraulic, and volumetric efficiency are constructed based on the structural characteristics of the pump, and the loss coefficients of each are derived by analyzing the composition of these expressions. A similar conversion method for each loss coefficient is then analyzed based on the analogous relationship between flow and head. An expression for the total efficiency and a similar conversion formula for the Wankel pump is established based on the relationship between loss and efficiency. Lastly, a hydraulic test system for the Wankel pump is constructed, and hydraulic performance tests of different models and speeds on Wankel pumps are conducted. The MATLAB genetic algorithm fits and analyzes the total efficiency expression, and a similar conversion formula is validated based on similar operating conditions. The results indicated that the goodness of fit of the Wankel pump's total efficiency expression for various speeds is > 0.80, while the average error of the similar conversion formula is <5% under comparable operating conditions, thus confirming their accuracy. This study is intended to predict the efficiency performance of the Wankel pump at various speeds and heads, providing a fundamental basis for the design and development of the Wankel pump.关键词：Wankel pump;efficiency expression;similar conversion;similar operating conditions
- 37
- |
- 110
- |
- 0
发布时间：2024-11-22
HYDRAULIC ENGINEERING
-
Continuous Train Positioning Using Visible Light Communication Assisted with Zero Velocity Update AI导读
“在列车控制领域，研究人员提出了一种借助零速修正的可见光通信列车定位方法，有效抑制了IMU的零速偏移，满足IEEE 1474.1—2004标准对列车定位精度的要求。”摘要：Objective Accurate train location information is crucial for communications-based train control (CBTC) systems to ensure safe operation. With the current trend towards vehicle-to-vehicle communication and fully automatic operation (FAO) for subways, CBTC systems demand highly accurate and real-time train positioning. Existing research into train positioning using visible light communication (VLC) achieves train location information by calculating the positional relationship between the receiving and sending ends. This attracts attention when a train is stationary or running at a uniform speed. However, little consideration has been paid to the accumulated error from the inertial measurement unit (IMU) and the zero-velocity offset of the IMU. The zero velocity update (ZUPT) algorithm can correct IMU errors by detecting the vehicle’s motion state and attitude angle to improve the accuracy and stability of positioning. The algorithm is simple, easy to implement, and has many applications in vehicle inertial navigation, robot positioning, human motion recognition, and other fields. This study proposes a continuous train positioning method based on VLC and ZUPT to optimize the results of dynamic train positioning and correct the offset from the IMU when a train is pulled over at a station, enabling seamless train positioning with high accuracy along an entire subway line. The proposed method can improve the accuracy of train positioning in tunnel environments and provide a reference for autonomous train positioning of CBTC systems toward vehicle-to-vehicle communication.Methods This study uses the LED light sources on the tunnel wall as the sending ends of VLC, while the binocular complementary metal-oxide-semiconductor (CMOS) camera fixed on the top carrier platform of the train head serves as the receiving end. This setup enables seamless train positioning with high accuracy along the entire subway line. First, based on the principle of VLC, the binocular CMOS camera at the head of the train captures images of the LED lamps, and the train simultaneously receives the corresponding strip information of the LED lamps using the blinking frequency information. Then, combined with IMU data, mechanical vibration at the receiving end is compensated to obtain the results of dynamic train positioning. In addition, to address the errors caused by train movement and when it is stationary, the train operation process over the entire line is divided into moving and zero-velocity sections. A zero-velocity detection condition is adopted to determine whether the train is stationary, and the results of dynamic train positioning are further optimized using the Unscented Kalman Filter (UKF) algorithm. Finally, the zero-velocity offset from the IMU is corrected to compensate for the divergence in the position and attitude estimation accuracy of the receiving end on the top of the train, achieving continuous high-precision positioning in the tunnel environment. The actual data from train operations on Chengdu Metro Line 1 are utilized to verify the feasibility of the proposed method. An experimental platform for autonomous train positioning, combining binocular stereo vision and VLC, is established, and MATLAB software is employed to analyze the experimental results of continuous train positioning.Results and Discussions A test point of train positioning is set every 0.5 m along the direction of train movement, and experiments are conducted 20 times on the optimization of train positioning in each positioning unit. When the train is running at speeds of 40, 60, and 80 km/h, the maximum error of train positioning after optimization with the unscented Kalman filter (UKF) is 15.27, 16.93, and 19.57 cm, respectively, and the accuracy of train positioning improves by 53.06%, 48.46%, and 52.71%, respectively (Fig. 5). After optimization with the UKF, the maximum error of train positioning during motion with constant acceleration is 18.48 cm, and during motion with constant deceleration, it is 18.24 cm (Fig. 6). The error of train positioning does not entirely change with the increase or decrease in train speed and still maintained a shrinking trend as the train approached the LED lamps (Fig. 7). When the train enters station B during motion with constant deceleration and left station B during motion with constant acceleration, the average error in a unit of train positioning using VLC is reduced to 6.52 cm (Fig. 8), and the error of train positioning at the stopping point is decreased to 15.18 cm (Fig. 9) after optimization with ZUPT-UKF. The proposed method meets the requirements of the IEEE 1474.1-2004 standard for train positioning and ensures continuous high-precision positioning throughout the entire line.Conclusion This study provides a train positioning method adopting VLC and the ZUPT algorithm to optimize the results of train positioning in the moving section and address the IMU’s accumulated error during stopping time in a tunnel environment. The experimental results showed that the maximum error of train positioning is 15.27, 16.93, and 19.57 cm, respectively, when the train runs in a straight line at constant speeds of 40, 60, and 80 km/h after UKF optimization. When the maximum target speed is 80 km/h, the maximum errors corresponding to constant acceleration and deceleration are 18.48 and 18.24 cm, respectively. After UKF optimization in the zero-speed section, the average train positioning error decreases from 18.74 to 6.52 cm, and the IMU’s accumulated error significantly declines. After ZUPT-UKF optimization, the average train positioning error reduces from 11.35 to 6.05 cm, and the train positioning error at the stop point diminishes from 30.55 to 15.18 cm, effectively suppressing the IMU’s zero-speed displacement. The proposed method meets the requirements of CBTC systems oriented toward vehicle-to-vehicle communication for the high accuracy of train positioning and ensures continuous high-precision train positioning throughout the entire line.关键词：visible light communication;communications-based train control;train positioning;zero velocity update;unscented Kalman filter- 47
- |
- 82
- |
- 0
发布时间：2024-11-22 - “在水下3D声呐点云数据处理领域，专家提出了一种自适应简化方法，大幅减少数据量，提高探测效率，为水下作业目标探测提供解决方案。”摘要：Reducing the number of points without sacrificing the quality of the original point cloud data is an essential preprocessing step for downsizing storage space and lowering calculation intensity. This study proposes an adaptive simplification method for underwater 3D sonar point cloud data. The normal differential operator is determined to identify sudden changes in the geometric scale of the point cloud, enabling segmentation of the boundary and primary part of the original point cloud. For the boundary part of the point cloud, the moving least squares method is applied to optimize the boundary points, reduce noise influence, and maintain the geometric consistency of the surface. The octree is used for downsampling on the boundary based on the point cloud's voxel grid structure. Depending on this structure, local farthest point sampling is implemented, achieving uniform simplification while ensuring the isotropy of the boundary part of the simplified point cloud and effectively retaining the geometric feature information of the boundary part of the point cloud. For the main part of the point cloud, the voxel center sampling method is employed to reduce the data and maintain the overall isotropy of the simplified point cloud, and the point cloud surface is smoothed through Gaussian filtering. Finally, the simplified boundary and main body are integrated to obtain the final simplified results. Experiments showed that the proposed simplification method has a low computational cost and fast processing speed, improving the simplification speed of underwater point cloud data by approximately 32% while maintaining the same simplification rate as current typical algorithms. In addition, the optimization of the algorithm on the boundary points and overall distribution of the underwater 3D point cloud is demonstrated through surface density comparison and geometric distortion analysis. The method enhances the detection efficiency of underwater operational targets and produces simplified underwater mission target point clouds that retain critical geometric feature information and remain isotropic.关键词：underwater point cloud;3D sonar;point cloud simplification;normal differentiation;voxel grid
- 66
- |
- 62
- |
- 0
发布时间：2024-11-22 -
Perception Error-resistant Air Combat Maneuvering Decisions Based on Deep Reinforcement Learning AI导读
“在视距内空战机动决策领域，研究者提出了基于GRU提取态势特征的GPPO算法，有效提高了无人机面对态势感知误差时的空战机动决策能力。”摘要：In the maneuvering decision-making process of within-visual-range air combat, onboard sensing equipment, represented by photoelectric sensors and radars, is susceptible to enemy jamming or meteorological factors, resulting in situational perception errors. Although deep reinforcement learning (DRL) has made significant progress in air combat maneuvering decision-making, existing methods do not consider the influence of situational perception errors. Since the state space is continuous and high-dimensional, situational perception errors affect the precision and accuracy of state estimation, impacting DRL’s training speed and decision-making performance. In response to this issue, an algorithm based on Proximal Policy Optimization (PPO) is proposed using features extracted by a Gated Recurrent Unit (GRU). The GRU is introduced to fuse the previous state sequence and extract the hidden features in the postures, initially utilizing the PPO algorithm, and a dominant posture-solving unit is set to compress the state space dimension, reducing the training difficulty. The reward shaping (RS) method, which quantifies the advantage, is subsequently designed to guide the faster convergence of DRL training. Finally, the relative situation model of within-visual-range air combat is defined and described. An air combat simulation environment with situational awareness errors is built by designing and introducing situational perception errors, and simulation and comparison experiments are conducted under various scenarios, such as different intensities of perception error and different initial situations of the enemy or foe. The simulation results showed that the GPPO algorithm can effectively complete air combat maneuvering decisions in various within-visual-range air combat scenarios with situational perception errors. The model training convergence speed and maneuvering decision performance using the GPPO and RS method, which quantifies the advantage, are significantly better than basic reinforcement learning algorithms. Despite situational perception errors, this effectively improves the UAV’s air combat maneuvering decision-making ability.关键词：deep reinforcement learning;within-visual-range air combat;maneuvering decision;perceptual error;reward shaping;UAV- 39
- |
- 60
- |
- 0
发布时间：2024-11-22
COMPUTER SCIENCE AND TECHNOLOGY
- “在化工领域，研究人员通过数值模拟优化矩形翼涡发生器参数，显著提升了撞击流反应器的混合性能，为化工过程强化提供了新思路。”摘要：The flow field characteristics and mixing characteristics of an impinging stream reactor equipped with a rectangular wing vortex generator are analyzed using the numerical simulation method to improve the mixing performance of an impinging stream reactor. The parameters of the rectangular wing vortex generator are optimized. The effects of size and position parameters of the rectangular wing vortex generator on the mixing strength of the impinging stream reactor and the effects of position parameters on velocity distribution and turbulent kinetic energy distribution in the reactor are investigated. The mass transfer field synergy principle is applied to analyze the synergy between velocity and concentration fields in the impinging stream reactor, both with and without the rectangular wing vortex generators. The results showed that the mixing strength initially increases and decreases with rising radial spacing, r. The mixing strength fluctuation changes as the wing width, h, rotor spacing, d, and attack angle, β, increase gradually. When the wing width h = 10 mm, rotor spacing d = 8 mm, radial spacing r = 30 mm, and attack angle β = 30° for the rectangular wing vortex generator, the mixing strength in the reactor is at its highest, and the mixing effect is optimal. Under conditions of full impact from two flows, the velocity distribution in the radial jet region of the impinging flow reactor follows a "bimodal" distribution pattern. Adjusting the vortex generator position parameters improves the radial velocity, with the highest radial velocity occurring when the axial spacing d = 8 mm and the attack angle β = 30°. The turbulent kinetic energy distribution in the radial jet region of the reactor exhibits a “single-peak” distribution, although changes in the vortex generator position parameters have minimal effect on the turbulent kinetic energy distribution. From the perspective of field synergy, adding a rectangular wing vortex generator alters the magnitude and direction of the radial jet velocity in the impinging stream reactor. As a result, the synergy between the velocity and concentration fields improves, enhancing the mixing performance of the impinging stream reactor.关键词：impinging stream reactor;field synergy theory;rectangular wing vortex generator;numerical simulation;mixing intensity
- 38
- |
- 73
- |
- 0
发布时间：2024-11-22 - “在机械密封领域，专家建立了深环形槽–深螺旋槽复合式端面构型模型，揭示了其降温作用机理，为机械密封高速化提供了解决方案。”摘要：High viscous heat generation and high-temperature rise of the end face, in the end, clearance are significant and common issues in mechanical seals operating at high speeds. The grooves on the seal face substantially influence fluid heat transfer in the cross-scale clearance. Researchers have determined that the temperature rise of the seal end face can be significantly reduced by incorporating an annular groove on the inner diameter side of the seal ring. This study develops thermohydrodynamic (THD) lubrication models of a deep annular groove-deep spiral groove compound end configuration (ASG) and a classical deep spiral groove end configuration (SG) using ANSYS Fluent to further examine its heat transfer process and cooling mechanisms. In these turbulent computational models, the heat transfer process of the annular groove is compared and analyzed, its cooling mechanisms are revealed, and the impacts of its geometric parameters on the mechanical seal performance and cooling effect are explored. The results indicate that the viscous shear heat of the fluid in the annular groove area decreases considerably due to the large film thickness and minimal shear effect, which effectively reduces the temperature of the lubricating liquid film and the seal face. The cooling effect of the annular groove can be enhanced by up to 15 K with the appropriate increase in groove depth and width, resulting in a temperature rise decrease of about 33%. Furthermore, the cooling effect of the annular groove reduces the fluid viscosity loss, enhancing the bearing and tribological performance of the deep annular groove-deep spiral groove compound end configuration (ASG) compared to the classical deep spiral groove end configuration (SG). The annular groove also increases the radial pressure gradient on the inner radial side of the sealing ring, which leads to a higher seal leakage rate, and the leakage fluid removes more viscous heat per unit time. Additionally, the presence of the annular groove increases the convective heat transfer area of the high-temperature fluid on the inner diameter side and the static ring, aiding in cooling. These structural modifications and the inclusion of an annular groove effectively reduce the temperature rise of the lubrication film and sealing face, which holds significant engineering importance for high-speed mechanical seals.关键词：mechanical seal;annular groove;deep spiral groove;Turbulent flow;thermohydrodynamic;cooling mechanism;CFD
- 88
- |
- 4
- |
- 0
发布时间：2024-11-22 -
Dual-channel Potential Energy Recovery and Load Balancing Methods for Large Hydraulic Excavators 增强出版 AI导读
“在气液平衡势能回收领域，专家提出了一种新型双通道动臂势能回收系统，有效解决了蓄能器体积过大和操控性问题，为节能降耗提供了新方案。”摘要：Objective When recovering potential energy based on load balancing, the usual expectation is that the load force and the hydraulic force provided by the accumulator are fully balanced so as to reduce the energy provided by the hydraulic system. However, because the accumulator pressure is not controllable and the load force is uncertain, the two forces usually cannot be matched accurately. When the load force changes gently, the theoretical volume of the accumulator is too large, and handling problems such as over-balance or under-balance may occur. The specific manifestation of these problems is that the boom stalls or almost stops descending. To solve these problems, the research object here is a large hydraulic excavator, for which is proposed a direct/indirect dual-channel boom potential energy recovery system comprising a three-chamber balance cylinder, two accumulators, and a fan motor.Methods In this study, the traditional single accumulator recovery channel (single-channel system) is changed to a dual accumulator recovery channel (dual-channel system), where the three-chamber balance cylinder and accumulator 1 form a direct recovery channel, while the fan motor and accumulator 2 form an indirect recovery channel. The principle of the single-channel system in existing relevant literature is as follows. In the boom lifting stage, the accumulator provides lifting force through the energy storage chamber, thereby reducing the input power of the original drive system. In the boom descending stage, the gravitational potential energy of the boom is converted into hydraulic energy through the energy storage chamber and stored in the accumulator for auxiliary lifting in the next working cycle. Ideally, the auxiliary driving force provided by the accumulator fully balances the load force. However, because the pressure of the accumulator cannot adapt dynamically to the load force, handling issues or excessive accumulator volume often occur. Compared with the single-channel system, the principle of the proposed dual-channel system is as follows. 1) By controlling the displacement of the fan motor, the load force can be balanced to the maximum extent, thereby avoiding the impact of over-balance and under-balance issues. 2) Although only some of the energy is stored in the high-pressure accumulator for auxiliary lifting, the excess energy is not lost on the main valve but rather is used to drive the fan motor for useful work. 3) Because part of the load is balanced by the fan motor circuit, the maximum and minimum working pressure of the accumulator can be adjusted within a large range, making it easier to choose a smaller-volume accumulator. Note that the fan motor in the proposed dual-channel system is the original device of the hydraulic excavator, so no additional installation space is required. In addition, although the proposed dual-channel system has two accumulators, their overall volume can be much smaller than the volume of a single accumulator in the single-channel system by designing its working pressure reasonably. A mathematical model of the proposed system is established, and the control law of the fan motor is deduced. Moreover, the influence of key parameters on system characteristics is analyzed. To verify the above theoretical analysis results, a simulation model including the main pump, energy-recovery module, and mechanical structure was built for a 70-ton large hydraulic excavator, and the simulation results are compared with the experimental results for a single-channel system.Results and Discussions The results show that the proposed system reduces the accumulator volume significantly (by more than 50%), saves installation space, eliminates additional energy loss, and solves handling issues caused by the mismatch between the hydraulic pressure and load force of the accumulator. The direct recovery channel—which comprises the energy storage chamber and the high-pressure accumulator—reduces the system energy supply during the boom lifting stage, and the indirect recovery channel—which comprises the rodless chamber of the balance cylinder, fan motor, and low-pressure accumulator—can be used to drive the fan motor. By controlling the displacement of the fan motor, the pressure of both accumulators is reduced from the highest working pressure to the lowest throughout the entire boom lifting stage, thereby ensuring the control needs for the next working circle. Compared with the prototype with no system for recovering potential energy, this system has energy-saving and emission-reduction effects while ensuring its original controllability. Because the hydraulic pressure provided by the dual channels is equal to the maximum load pressure, a small amount of driving energy still must be provided by the pump during the boom lowering stage. In future research, predictive control or some detection methods can be combined with the proposed method to obtain the magnitude or variation pattern of load force. On this basis, by controlling the displacement of the fan motor, the hydraulic pressure provided by the dual channels can track the load force, thereby minimizing the supply pressure and balancing the load force to the maximum extent.Conclusion Therefore, the proposed system retains the advantages of a single-channel system while improving the impact on handling performance and saving installation space. Moreover, the proposed method can be used in other applications such as wheel loaders, cranes, and other situations requiring the recovery of gravitational potential energy, thereby giving it good promotion and application prospects. In addition, on the basis of load-force feedback, some precise closed-loop control methods can be used to achieve accurate load matching.关键词：large hydraulic excavator;potential energy recovery;dual-channel;load balance- 12
- |
- 0
- |
- 0
发布时间：2024-11-22 - “在机构学领域，专家建立了空间连杆机构尺度综合的几何流程，为解决空间机构传动任务提供解决方案。”摘要：A classic problem in mechanism design is the dimensional synthesis of mechanisms, and established here is a relatively systematic, transparent, and simple geometric process for the dimensional synthesis of spatial linkages. Starting with the rules governing the kinematic geometry of a spatial linkage, the research object is the floating link in the linkage, simplified as a fixed line on the link. This straight line is a guiding component, and its kinematic geometric characteristics are studied under the condition of finite separation positions. On this basis, the spatial kinematic geometry model of a line in the case of finite separation positions is established, which transforms the problem of space kinematics into that of spatial geometry. Next, based on the different number of spatial positions occupied by the straight line, its multi-position problem is decomposed into several combinations of multiple few-position problems. Furthermore, the geometric correlation between the extracted geometric characteristics is studied. By combining the two-plane projection system and clarifying the main projection direction of the projection transformation, the corresponding line guidance model is constructed in the plane system, which transforms geometric problems from space to plane. Finally, according to the motion task of the mechanism, the function generation task of the spatial mechanism is transformed into the problem of finding a solution for the line guidance model by using the principles of kinematic inversion, and an effective method is proposed for the dimensional synthesis of spatial linkages. Finally, the function generation synthesis problem involving a five-position RSSR mechanism is taken as an example, and by using the geometric synthesis process for spatial linkage mechanisms, a solution is obtained whereby the RSSR mechanism can realize five sets of precise function relations. Numerical results confirm the effectiveness and practicality of the synthesis method, and not limited to the dimensional synthesis of mechanisms with rotation input and rotation output, this method is also applicable to mechanisms with rotation input and slide output, rotation input and planar motion output, and rotation input and spatial motion output.关键词：spatial linkages;dimensional synthesis;geometric method;two-plane projection system;line-guidance model
- 6
- |
- 2
- |
- 0
发布时间：2024-11-22
MECHANICAL ENGINEERING
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0