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Mechanistic Data-Driven Modeling on the Damage of Hyperelastic Materials Under Cyclic Loading 认领 引用
1
作者 Min Tang Gang Zhang +3 位作者 Zherui Liu Lijie Liu Zefeng Yu Shan Tang 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第5期681-691,共11页
This study proposes a data-driven computational mechanics framework for predicting the damage behavior of hyperelastic materials under complex cyclic loading.Leveraging prior knowledge of the underlying physical mecha... This study proposes a data-driven computational mechanics framework for predicting the damage behavior of hyperelastic materials under complex cyclic loading.Leveraging prior knowledge of the underlying physical mechanisms and an established modeling framework,the 3D stress-strain data required for constitutive modeling are reduced to 1D datasets.A recurrent neural network(RNN)is trained on uniaxial cyclic loading data to capture the stress-strain response,and the trained model is subsequently embedded into a finite element solver.This approach allows 3D structural simulations under complex cyclic loads to be driven by 400 uniaxial test data samples.The predictive accuracy of the proposed approach is validated against the classical Mullins damage model,demonstrating its effectiveness.Finally,the limitations of the present method and potential directions for future improvement are discussed. 展开更多
关键词 Data-driven Constitutive law Hyperelastic Damage Mullins effect Recurrent neural network
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A data-driven modeling approach for predictive control of mixed traffic flow 认领 引用
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作者 Yue Zuo Xudong Qi +2 位作者 Huifeng Hu Tao Zou Ping Wang 《Control Theory and Technology》 EI CSCD 2026年第2期293-306,共14页
Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions.This paper proposes a data-driven control strategy for connected and autonomous vehicles(CAVs)in... Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions.This paper proposes a data-driven control strategy for connected and autonomous vehicles(CAVs)in mixed traffic flow,implemented through variable speed limits and lane-changing guidance.First,a cellular automata model of mixed traffic flow is developed,with adjustable CAVs'maximum speed limit and lane-changing probability,thereby linking microscopic CAV operating rules to macroscopic traffic flow dynamics.Second,a recurrent neural network(RNN)is employed to capture the temporal dynamics of the traffic system and predict the evolution of traffic flow states.The RNN is then linearized via the Koopman operator,transforming the complex nonlinear model into a linear representation for the design of a computationally efficient model predictive controller.Finally,simulation results demonstrate that the strategy increases the average traffic speed by 14.2% across 12 traffic scenarios.Specifically,under the challenging conditions of high traffic density with low CAV penetration,it achieves a 5.22% improvement and promotes a more uniform vehicle distribution.These findings highlight the potential of the proposed approach for mixed traffic flow regulation. 展开更多
关键词 Mixed traffic flow Model predictive control Cellular automata model Koopman operator
Balanced Optimization of Dimensional Accuracy and Printing Efficiency in FDM Based on Data-Driven Modeling 认领 引用 被引量：3
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作者 Liu Changhui Li Hao +5 位作者 Yu Chunlong Liao Xueru Liu Xiaojia Sun Jianzhi Tang Qirong Yu Min 《Additive Manufacturing Frontiers》 CSCD 2025年第2期97-110,共14页
Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring... Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM. 展开更多
关键词 Fused deposition modeling Dimensional accuracy Process parameters Printing efficiency Balanced optimization Sustainable manufacturing
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Data-driven modeling of a four-dimensional stochastic projectile system 认领 引用
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作者 Yong Huang Yang Li 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第7期157-162,共6页
The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations.With the rapid development of data science and scientific tools of m... The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations.With the rapid development of data science and scientific tools of measurement recently,there are numerous data-driven methods devoted to discovering governing laws from data.In this work,a data-driven method is employed to perform the modeling of the projectile based on the Kramers–Moyal formulas.More specifically,the four-dimensional projectile system is assumed as an It?stochastic differential equation.Then the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data,which agree well with the real system.The effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile. 展开更多
关键词 data-driven modeling machine learning projectile systems Kramers–Moyal formulas
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Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality 认领 引用 被引量：8
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作者 Xiaoyu Jiang Xiangyin Kong Zhiqiang Ge 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第6期1445-1461,共17页
The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased si... The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications. 展开更多
关键词 Index Terms—Curse of dimensionality data augmentation data-driven modeling industrial processes machine learning
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Data-driven modeling of power system dynamics:Challenges,state of the art,and future work 认领 引用 被引量：1
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作者 Heqing Huang Yuzhang Lin +3 位作者 Yifan Zhou Yue Zhao Peng Zhang Lingling Fan 《iEnergy》 2023年第3期200-221,共22页
With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditi... With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented challenges.Data-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating conditions.This paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and applications.It also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future. 展开更多
关键词 Data-driven modeling machine learning model construction parameter identification power system dynamics system identification
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Data-Driven Modeling for Wind Turbine Blade Loads Based on Deep Neural Network 认领 引用 被引量：1
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作者 Jianyong Ao Yanping Li +2 位作者 Shengqing Hu Songyu Gao Qi Yao 《Energy Engineering》 EI 2024年第12期3825-3841,共17页
Blades are essential components of wind turbines.Reducing their fatigue loads during operation helps to extend their lifespan,but it is difficult to quickly and accurately calculate the fatigue loads of blades.To solv... Blades are essential components of wind turbines.Reducing their fatigue loads during operation helps to extend their lifespan,but it is difficult to quickly and accurately calculate the fatigue loads of blades.To solve this problem,this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis,feature selection,and model construction.In the mechanism analysis part,the generation mechanism of blade loads and the load theoretical calculationmethod based on material damage theory are analyzed,and four measurable operating state parameters related to blade loads are screened;in the feature extraction part,15 characteristic indicators of each screened parameter are extracted in the time and frequency domain,and feature selection is completed through correlation analysis with blade loads to determine the input parameters of data-driven modeling;in the model construction part,a deep neural network based on feedforward and feedback propagation is designed to construct the nonlinear coupling relationship between the unit operating parameter characteristics and blade loads.The results show that the proposed method mines the wind turbine operating state characteristics highly correlated with the blade load,such as the standard deviation of wind speed.The model built using these characteristics has reasonable calculation and fitting capabilities for the blade load and shows a better fitting level for untrained out-of-sample data than the traditional scheme.Based on the mean absolute percentage error calculation,the modeling accuracy of the two blade loads can reach more than 90%and 80%,respectively,providing a good foundation for the subsequent optimization control to suppress the blade load. 展开更多
关键词 Wind turbine blade fatigue load modeling deep neural network
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用 被引量：1
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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Data-Driven Materials Science Using Machine Learning and Computational Modeling 认领 引用
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作者 Manjodh Kaur Princy Randhawa +4 位作者 Jitendra Jaiswal Deepak Dubal Ravindra N.Bulakhe Deepanraj Balakrishnan Nithesh Naik 《Computers, Materials & Continua》 SCIE EI 2026年第8期90-124,共35页
This review emphasizes the growing role of artificial intelligence(AI)in transforming the materials discovery process into a data-driven and autonomous approach.It systematically traces the evolution of scientific par... This review emphasizes the growing role of artificial intelligence(AI)in transforming the materials discovery process into a data-driven and autonomous approach.It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning,generative models,and AI agents are revolutionizing the design,screening,and optimization of materials.A key contribution is a detailed,step-by-step machine learning framework that guides researchers through data collection,preprocessing,feature engineering,model development,and validation,utilizing publicly available materials databases and computational tools.Additionally,the review discusses the latest advances in generative AI and autonomous research systems,highlighting their potential to enable inverse design and closed-loop experiments.It includes a tutorial case study on sodium-ion battery materials to demonstrate practical application in formation energy prediction via machine learning,along with comparisons to high-throughput screening accuracy using density functional theory(DFT).The article also addresses current challenges such as data limitations,model interpretability,and physics-based approaches.Overall,this publication serves as both a conceptual and practical guide for integrating AI into materials research,aiming to accelerate the discovery process and improve efficiency. 展开更多
关键词 Artificial intelligence materials science materials discovery energy materials computational modeling
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Data-driven modeling for evaluating deformation of a deep excavation near existing tunnels 认领 引用 被引量：4
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作者 Fengwen Lai Songyu Liu +3 位作者 Jim Shiau Mingpeng Liu Guojun Cai Ming Huang 《Underground Space》 SCIE EI CSCD 2025年第5期162-179,共18页
This study explores an integrated framework combining in-situ test-based numerical and data-driven modeling to assess the performance of a deep excavation-tunnel system.To achieve the goal,a case history of deep excav... This study explores an integrated framework combining in-situ test-based numerical and data-driven modeling to assess the performance of a deep excavation-tunnel system.To achieve the goal,a case history of deep excavations adjacent to existing tunnels in silt/sand-dominated sediments is introduced to establish a base three-dimensional finite element(3D-FE)model.In-situ tests such as cone penetration test(CPT/CPTU)and seismic dilatometer test(DMT/SDMT),as an alternative to laboratory testing,are used to determine a set of advanced constitutive model parameters.The established excavation-tunnel numerical model is then validated against filed monitoring data.A dataset from numerical simulation is created for training and testing four machine learning models(i.e.,artificial neural network(ANN),support vector machines(SVM),random forest(RF),and light gradient boosting machine(LightGBM)),which predict the maximum wall deflection,ground surface settlement,horizontal and vertical displacements of the tunnel.Results show that the ANN model outperforms other models in prediction capacity.Its generalization ability in practice is further enhanced by comparing field measurement data and empirical equations.The findings suggest that,with the integrated in-situ tests,FE and ANN modeling could be used to predict deformation responses of deep excavations close to existing tunnels in soft soil.The present study is useful and valuable for practical risk assessment and mitigation decisions. 展开更多
关键词 Numerical modeling Data-driven modeling In-situ test Deep excavation Tunnel Soft soil Deformation response
Data-Driven and Physics-Informed Surrogate Modeling for Heat Conduction in the Pressurizer Wall of Pressurized Water Reactors under Severe Accident Scenarios 认领 引用
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作者 Fabiano Thulu Zeyun Wu 《Energy Engineering》 EI 2026年第5期1-26,共26页
Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors(PWRs)during severe accidents,such as Station Blackout(SBO)and Loss-of-Coolant Accident(LOCA)is vital for structur... Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors(PWRs)during severe accidents,such as Station Blackout(SBO)and Loss-of-Coolant Accident(LOCA)is vital for structural integrity assessment.However,conventional thermal-hydraulic simulations used for such predictions are computationally intensive,limiting their applicability for real-time analysis.This study develops and compares three surrogate models:Polynomial Regression,Deep Neural Network(DNN),and a Physics-Informed Neural Network(PINN).Thermal-hydraulic simulation data generated by RELAP5-3D are integrated with physics-constrained learning techniques to model transient heat conduction in the pressurizer wall.The internal wall temperature evolution is reconstructed using a one-dimensional transient heat conduction mode solved via the Finite Difference Method.The Polynomial Regression model,while achieving a relative high coefficient of determination(R2=0.9780),exhibited an average Root Mean Squared Error(RMSE)of 7.50 K and a Maximum Absolute Error(MaxAE)of 193.6 K on unseen test data,indicating limited capability in capturing localized thermal stresses.In contrast,the purely data-drive DNN model demonstrated superior performance,achieving an overall test R2of 0.9996,an RMSE of 1.04 K,and a significantly reduced MaxAE of 24.8 K.Finally,the PINN model yielded an overall physics-based test R2of 0.9874,with an RMSE of 5.66 K,and a MaxAE of 89.7 K.Although the DNN achieves the highest statistical accuracy,the PINN offers a key advantage by enforcing adherence to the governing heat conduction equations.The embedded physical consistency makes PINN a more reliable and trustworthy surrogate for nuclear safety analysis,where maintaining physical fidelity is as critical as numerical accuracy. 展开更多
关键词 Heat conduction surrogate modeling polynomial regression deep neural networks physics-informed neural networks
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Data-driven modeling and sensitivity analysis of pressure differential deviations in pharmaceutical cleanrooms under static conditions and dynamic disturbances 认领 引用 被引量：1
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作者 Yufan Chang Jie Zong +5 位作者 Yan Hu Zhengtao Ai Weichao Zhang Wenxuan Zhao Zhongbing Liu Rongpeng Zhang 《Building Simulation》 SCIE EI CSCD 2025年第8期2019-2038,共20页
Pressure differential deviations under static conditions and pressure convergence fluctuations under dynamic disturbances are widely reported problems with pressure differential control in pharmaceutical cleanrooms,ye... Pressure differential deviations under static conditions and pressure convergence fluctuations under dynamic disturbances are widely reported problems with pressure differential control in pharmaceutical cleanrooms,yet their underlying mechanisms and key reasons remain insufficiently explored.This study performed a field survey and model-based simulations to identify the major influencing parameters and quantify their influence on pressure differentials.Twelve pharmaceutical cleanrooms with varying environmental control parameters were included in the field survey,all of which were served by a variable air volume(VAV)ventilation system.Large deviations between actual and design pressure differentials were found,ranging from 10%to 42.5%,and a total of 24 uncertain parameters and their respective uncertainty ranges were identified.Based on the field survey,a data-driven pressure differential response model was developed using MATLAB/Simulink platform.The model fully took into account the system dynamics and facilitated real-time monitoring and control of the pressure differential.Sobol-based sensitivity analysis was then conducted to identify key influencing parameters of pressure differential deviations.The simulated results revealed that static pressure differential deviations were predominantly influenced by pressure sensing accuracy,exhaust airflow accuracy,and duct impedance,while dynamic disturbances were mainly driven by room envelope airtightness and supply airflow accuracy.The interactions between connected zones were pronounced.Rooms with higher branch duct impedance experienced smaller pressure differential deviations due to natural buffering characteristics,while the parameter uncertainties in these rooms significantly affected pressure differential in other rooms.These findings offer practical guidance for the design and operation of precise pressure differential control in pharmaceutical cleanrooms. 展开更多
关键词 pharmaceutical cleanroom pressure differential field survey data-driven modeling sensitivity analysis
An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：2
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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Dynamic modeling of spatial variable stator vane mechanism using modified Lagrange multiplier method 认领 引用 被引量：1
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作者 Ke HE Kaiyi HUANG +2 位作者 Zhen LI Shuhui HU Zhinan ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期251-271,共21页
The Variable Stator Vanes(VSV)system ensures the smooth operation of the highpressure compressor by adjusting the vane angles to prevent surge,and the dynamic behavior of its multistage vanes directly affects its serv... The Variable Stator Vanes(VSV)system ensures the smooth operation of the highpressure compressor by adjusting the vane angles to prevent surge,and the dynamic behavior of its multistage vanes directly affects its service performance.To investigate the dynamic behavior of the spatial VSV multi-vane mechanism,a positional constraint equation for the VSV mechanism was established,and the numerical expressions of the Jacobian matrices for different kinematic pair constraint equations were derived.The Lagrange multiplier method was modified for spatial rotation,and an ideal dynamic model of the spatial VSV multi-vane mechanism was developed.The computational results indicate that the dynamic behavior of different vanes within the same stage is similar,and the constraint moment experienced by vanes at different positions has a linear relationship with their centroid coordinates.This study expands the dynamic modeling methods for spatial mechanisms and provides a foundation for researching the frictional dynamic behavior of VSV mechanisms with clearance. 展开更多
关键词 Dynamic characteristics Dynamic models Lagrange multipliers Spatial dynamic modeling:VSV
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A peridynamics modeling approach for pre-cracked rock cracking processes under impact by integrating Drucker-Prager plasticity model and efficient contact model 认领 引用 被引量：2
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作者 Jingzhi Tu Nengxiong Xu Gang Mei 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期179-195,共17页
In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical propert... In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks. 展开更多
关键词 Pre-cracked rocks Cracking processes Non-ordinary state-based peridynamics (NOSBPD) Drucker-Prager plasticity model Efficient contact model
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Data-driven computing ligament loading mechanisms:integration of the computational ligament mechanics models with deep learning 认领 引用 被引量：1
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作者 Datao Xu Huiyu Zhou +7 位作者 Yi Yuan Zanni Zhang Tianle Jie Zhifeng Zhou Zixiang Gao Liangliang Xiang Meizi Wang Yaodong Gu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期628-672,共45页
Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This st... Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This study combines medical imaging data to construct the subject-specific ankle musculoskeletal model,which considers the subject's individualized characteristics and ligamentous attributes.Furthermore,we developed the structural constitutive model to restore the nonlinear short-term viscoelastic properties of the ligament-dense connective tissue,which can more realistically revert the LLM and reveal the mechanical properties of ankle injury.Based on the computational ligament mechanics(CLM)model,we developed a deep learning-based prediction model to predict LLM by CLM data-driven modeling.The modeling simulation results are highly consistent with the calculation results from the dual fluoroscopic imaging system,which demonstrated that the CLM model has high accuracy.The data-driven modeling performs exceptionally well in predicting ligament loading forces.The findings indicate that the constructed CLM data-driven model has the potential to enhance the accuracy and safety of ankle rehabilitation robots,while also providing personalized,dynamically adjusted rehabilitation training programs.The proposed comprehensive solutions would bring benefits to more patients with sports injuries and the general rehabilitation population,and promote the development and advancement of the research field of CLM and biomechanical variable prediction. 展开更多
关键词 Computational ligament mechanics Subject-specific musculoskeletal model Structural constitutive model Ankle ligament injury mechanisms Biomechanical variable prediction
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Time-dependent behavior of deep roadway surrounding rock considering damage induced by excavation and mining disturbances:Experiments,modeling,and simulation 认领 引用 被引量：1
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作者 Qingzhe Cui Rongbin Hou +4 位作者 Zhenhua Li Feng Du Xu Chen Boyang Zhang Lielie Li 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第2期439-456,共18页
In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicti... In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicting long-term roadway stability,necessitating the development of a reliable constitutive creep model and numerical simulation approach.In this study,creep experiments were conducted on pre-damaged rock with varying initial damage levels to investigate the time-dependent mechanical properties.Based on the experimental results,an accelerated-creep criterion was proposed,and an elastic-viscoplastic creep damage model(EVPCD)was established that simultaneously considers the effects of time-dependent damage and instantaneous damage caused by stress disturbances on rock creep behavior.Subsequently,the effectiveness of the proposed creep model was verified using experimental data,and the secondary development of the EVPCD model was completed based on the FLAC3D platform.Following this,a long-term stability analysis method of deep surrounding rock that accounts for excavation-and mining-induced disturbances was proposed.Using the main roadway of Xutuan Coal Mine as a case study,numerical simulations were carried out to investigate the time-dependent deformation and failure characteristics of the surrounding rock following excavation and mining disturbance.Combined with on-site monitoring of the surrounding rock damage areas,the results indicate that the EVPCD outperforms the CVISC and Nishihara models in predicting the time-dependent behavior of deep surrounding rock. 展开更多
关键词 Initial damage Time-dependent damage Creep model Numerical implementation Damage evolution
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Non-landslide sample for landslide susceptibility prediction modeling:A review of selection strategies and their influence rules 认领 引用 被引量：1
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作者 Zhuo Jia Zhijin Cheng +3 位作者 Zhilu Chang Qin Li Faming Huang Yuhao Peng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期2859-2880,共22页
A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and m... A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and machine learning models,as well as in the extent of LSP performance enhancement after their coupling.To overcome these uncertainties,this study takes Wuning county of China as a case area,collecting 24 conditioning factors and 379 landslides data.Four non-landslide sample selection strategies,namely random selection,low-slope,buffer zone,and semi-supervised strategies,are then combined with landslide samples in a 1:1 ratio to serve as input variables for constructing LSP models using support vector machine(SVM),logistic regression(LR),random forest(RF)and extreme gradient boosting(XGBoost).Finally,the uncertainty of semi-supervised machine learning coupled models with a 1:2 ratio of landslide to non-landslide samples is analyzed and compared.The results show that:(1)The semi-supervised and low-slope strategies demonstrate higher prediction accuracy compared to the buffer zone and random selection strategies.Moreover,the RF coupled models are the most reliable,followed by the XGBoost,SVM,and LR coupled models;(2)Compared to a 1:1 ratio,a 1:2 ratio of landslide to non-landslide samples significantly improves prediction accuracy,suggesting that appropriately increasing the proportion of non-landslide samples helps to mitigate overfitting and enhance the identification of landslide samples;and(3)LSP is more sensitive to non-landslide sample selection strategies than to the choice of machine learning models.In conclusion,prioritizing reliable non-landslide samples is crucial for improving accuracy of LSP. 展开更多
关键词 Landslide susceptibility prediction Non-landslide sample selection Machine learning models Uncertainty analysis
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Automatic gating and riser system design and defect control for K4169 superalloy guide blade casting based on parametric 3D modeling-simulation integrated system 认领 引用 被引量：1
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作者 Le-chuan Li Ya-jun Yin +4 位作者 Bing-zheng Fan Guo-yan Shui Xiao-yuan Ji Jian-xin Zhou Lei Jin 《China Foundry》 SCIE EI CAS CSCD 2026年第1期20-30,共11页
Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical si... Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%. 展开更多
关键词 numerical simulation automatic design investment casting parametric 3D modeling gating and riser system
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Multi-scale modeling of ultra-thin commercially pure titanium sheet for fuel cell bipolar plates:Plastic anisotropy and distortional strain hardening 认领 引用 被引量：1
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作者 Kyung Mun Min Seonghwan Choi +2 位作者 Xiaohua Hu Jinwoo Lee Hyuk Jong Bong 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第5期1637-1651,共15页
This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for c... This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for characterizing ultra-thin sheets under complex stress states are lacking,a virtual modeling approach was employed.At the grain scale,a crystal plasticity finite element(CPFE)model was constructed to incorporate the relevant slip and twinning systems,enabling prediction of responses under diverse loading conditions.Extending to the continuum scale,the CPFE results,combined with tensile data,were used to calibrate an advanced constitutive model based on the evolutionary Yld2000-2d yield function,capable of capturing anisotropic behavior.Validation against independent limiting dome height tests confirmed the predictive accuracy of the framework.The proposed approach provides a basis for simulating the forming behavior of ultra-thin CP-Ti sheets and supports precise manufacturing of bipolar plates in fuel cell systems. 展开更多
关键词 commercially pure titanium sheet crystal plasticity plastic anisotropy distortional strain hardening multi-scale modeling
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