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Finite element verification on constitutive law of AZ31 magnesium alloy at 400℃

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Transactions of Nonferrous Metals Society of China 2013年第11期23卷 3372-3382页

作者：曾世聪胡宣德成功大学土木工程学系

A constitutive law is offered for an AZ31B-H24 Mg alloy within a strain rate range of 10-5-10-2 s-1 at a temperature of 400 ℃ The constitutive law, which is developed by curve fitting the tensile tests data, is expressed as a flow stress function of strain and strain rate. Furthermore, the constitutive law is embedded into a proper FE model to simulate the tensile experiments for the purpose of verifying reliability, where the incremental stress-strain relationships are calculated by an elastic-plastic theory in the finite element analysis （FEA）. The results show that the stress-strain characteristics and the final deformed shapes in the FEA agree well with the experiments. In addition, the predicting analysis of constant-velocity stretch conditions and the verification of a free bulge forming experiment show that the proposed FE model is practicable for mechanical analysis on superplastic forming problems. A selective numerical method is offered for advanced superplastic analysis on AZ31 Mg alloys.

关键词： AZ31 magnesium alloy constitutive law finite element verification

An efficient hyper-elastic model for the preforming simulation of Carbon-Kevlar hybrid woven reinforcement

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Chinese Journal of Aeronautics 2022年第12期35卷 321-335页

作者： Sasa GAO Zhengtao QU Yunjie ZHANG Zizhao ZHAO Biao LIANG College of Mechanical&Electrical Engineering Shaanxi University of Science&TechnologyXi’an 710021China State Key Laboratory of Applied Optics Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China

An efficient hyper-elastic model that can reflect the primary mechanical behaviors of Carbon-Kevlar hybrid woven reinforcement was developed and implemented with VUMAT constitutive code for preforming *** model parameters were accurately determined through the uniaxial and bias-extension *** calibrate the simulation code,preforming experiments of hybrid woven reinforcement over the hemisphere mold and tetrahedron mold were respectively conducted to validate the proposed hyper-elastic *** comparison between the simulations and experiments shows that the model can not only accurately capture shear angle distribution and geometry shape after deformation,but also accurately predict the force–displacement curve and potential fiber tensile failure during the preforming *** result indicates that the proposed model can be used to predict the preforming behavior of Carbon-Kevlar hybrid woven reinforcement,and simulate its manufacturing process of complicated geometry.

关键词： constitutive law Finite element analysis Hybrid woven reinforcement Hyper-elastic Preforming

Data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

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Defence Technology（防务技术） 2024年第3期33卷 55-65页

作者： Zhiyuan Tang Yu Wang Khalil I.Elkhodary Zefeng Yu Shan Tang Dan Peng State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of Technology116023DalianPR China The Department of Mechanical Engineering The American University in CairoNew Cairo11835Egypt Department of Neurology The Second Hospital of Dalian Medical UniversityDalian116023PR China

Brain tissue is one of the softest parts of the human body,composed of white matter and grey *** mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain ***,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical *** white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical *** such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional *** a constitutive law for multiscale brain tissue using traditional function-based approaches can be very ***,this paper proposes a data-driven approach to establish the desired mechanical model of brain *** focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our *** matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading *** generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal ***,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body *** with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling *** predictions made by our trained model demonstrate sufficient ***,we find that the level of internal blood pressur

关键词： Data driven constitutive law Anisotropy Brain tissue Internal pressure

Compressible closure models for turbulent multifluid mixing

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Applied Mathematics and Mechanics(English Edition) 2016年第1期37卷 97-106页

作者： H.JIN Department of Mathematics Jeju National University

This paper studies governing equations describing the turbulent fluid mixing behavior effectively. The goal is to propose a closure for compressible multiphase flow models with transport and surface tension, which satisfy the boundary conditions at the mixing zone edges, the conservation requirements, and an entropy inequality constraint. Implicitness of positivity for the entropy of averaging requires entropy inequality as op- posed to conservation of entropy for microphysically adiabatic processes.

关键词： multiphase flow closure constitutive law averaged equation

Machine learning-based constitutive models for cement-grouted coal specimens under shearing

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International Journal of Mining Science and Technology 2021年第5期31卷 813-823页

作者： Guichen Li Yuantian Sun Chongchong Qi School of Mines China University of Mining and TechnologyXuzhou 221116China State Key Laboratory of Coal Resources and Safe Mining China University of Mining and TechnologyXuzhou 221116China School of Resources and Safety Engineering Central South UniversityChangsha 410083China

Cement-based grouting has been widely used in mining engineering;its constitutive law has not been comprehensively *** this study,a novel constitutive law of cement-grouted coal specimens(CGCS)was developed using hybrid machine learning(ML)*** tests were performed on CGCS for the analysis of stress-strain curves and the preparation of the *** maintain the interpretation of the trained ML models,regression tree(RT)was used as the main *** effect of maximum RT depth(Maxdepth)on its performance was studied,and the hyperparameters of RT were tuned using the genetic algorithm(GA).The RT performance was also compared with ensemble learning *** optimum correlation coefficient on the training set was determined as 0.835,0.946,0.981,and 0.985 for RT models with Maxdepth=3,5,7,and 9,*** overall correlation coefficient was over 0.9 when the Maxdepth≥5,indicating that the constitutive law of CGCS can be well ***,the failure type of CGCS could not be captured using the trained RT *** forest was found to be the optimum algorithm for the constitutive modeling of CGCS,while RT with the Maxdepth=3 performed the worst.

关键词： constitutive law Cement-grouted coal specimens Machine learning Regression tree Ensemble learning

Derivation of the Orthotropic Nonlinear Elastic Material law Driven by Low-Cost Data(DDONE)

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Acta Mechanica Solida Sinica 2022年第5期35卷 800-812页

作者： Qian Xiang Hang Yang K.I.Elkhodary Zhi Sun Shan Tang Xu Guo State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian116023China International Research Center for Computational Mechanics Dalian University of TechnologyDalian116023China The Department of Mechanical Engineering The American University in CairoNew Cairo11835Egypt

Orthotropic nonlinear elastic materials are common in nature and widely used by various ***,there are only limited constitutive models available in today's commercial software(e.g.,ABAQUS,ANSYS,etc.)that adequately describe their mechanical ***,the material parameters in these constitutive models are also difficult to calibrate through low-cost,widely available experimental ***,it is paramount to develop new ways to model orthotropic nonlinear elastic *** this work,a data-driven orthotropic nonlinear elastic(DDONE)approach is proposed,which builds the constitutive response from stress–strain data sets obtained from three designed uniaxial tensile *** DDONE approach is then embedded into a finite element(FE)analysis framework to solve boundary-value problems(BVPs).Illustrative examples(e.g.,structures with an orthotropic nonlinear elastic material)are presented,which agree well with the simulation results based on the reference material *** DDONE approach generally makes accurate predictions,but it may lose accuracy when certain stress–strain states that appear in the engineering structure depart significantly from those covered in the data *** DDONE approach is thus further strengthened by a mapping function,which is verified by additional numerical examples that demonstrate the effectiveness of our modified ***,artificial neural networks(ANNs)are employed to further improve the computational efficiency and stability of the proposed DDONE approach.

关键词： Data-driven Orthotropic nonlinear elastic materials constitutive law Finite element analysis Artificial neural network

Prediction of single cell mechanical properties in microchannels based on deep learning

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Applied Mathematics and Mechanics(English Edition) 2024年第11期45卷 1857-1874页

作者： Jiajie GONG Xinyue LIU Yancong ZHANG Fengping ZHU Guohui HU Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering ScienceShanghai Frontier Science Center of MechanoinformaticsShanghai Key Laboratory of Mechanics in Energy EngineeringShanghai UniversityShanghai200072China Shanghai Institute of Aircraft Mechanics and Control Shanghai200092China Department of Neurosurgery Huashan HospitalShanghai Medical CollegeFudan UniversityShanghai200040China

Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a requirement for advanced technical *** overcome these challenges,a novel machine learning(ML)approach is implemented based on the convolutional neural networks(CNNs),aiming at predicting cells'elastic modulus and constitutive equations from their deformations while passing through micro-constriction *** the present study,the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus,incorporating three widely-used constitutive models that characterize the cellular mechanical behavior,i.e.,the Mooney-Rivlin(M-R),Neo-Hookean(N-H),and Kelvin-Voigt(K-V)*** this dataset,a multi-input convolutional neural network(MI-CNN)algorithm is developed by incorporating cellular deformation data as well as the time and positional *** approach accurately predicts the cell elastic modulus,with a coefficient of determination R^(2)of 0.999,a root mean square error of 0.218,and a mean absolute percentage error of 1.089%.The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R^(2)of 0.99,even when the stochastic noise is added to the simulated *** significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we *** model accurately and reliably predicts single-cell mechanical properties,showcasing a robust ability to *** demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and *** algorithm presents a possibility for high-throughput,highly automated,real-time,and precise characterization of single-cell mechanical properties.

关键词： cell deformation single-cell mechanics machine learning(ML) constitutive law convolutional neural network(CNN)

Exact Solution of a Cylinder Tube Made of Metallic Foam Under Inner Pressure

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Journal of Beijing Institute of Technology 2008年第3期17卷 355-360页

作者：祝爱玉范天佑 School of Science Beijing Institute of Technology

Exact solution of the stress and velocity fields of a cylinder tube of metallic foams under inner pressure is given in which the Triantafillou and Gibson constitutive law （TG model） for the material is taken as a basis of the calculation. The nonlinear equation is turned linear equation by introducing a kinematics parameter. The differences between the full condensed materials and the effect of the relative density are also discussed.

关键词： metal foams constitutive law TG model the relative density kinematics parameter

THE EXISTENCE OF SOLUTIONS IN NONLINEAR ELASTODYNAMICS

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Applied Mathematics and Mechanics(English Edition) 1997年第3期18卷 243-249页

作者：郭兴明 Shanghai Institute of Applied Mathematics and Mechanics Shanghai University Shanghai PR China

Under the small deformation assumption this paper shows the existence of solution for the system of elastic dynamics with the general nonlinear constitutive laws, and the existence of classical solution can be found u... 详细信息

关键词： nonlinear constitutive law elastodynamics existence