The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials,such as hyperelastic and functionally graded materials(FGMs),is critical for ensuring their safety and optimiz...The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials,such as hyperelastic and functionally graded materials(FGMs),is critical for ensuring their safety and optimizing their design.However,conventional numerical methods can face challenges with the non-linearities inherent in hyperelasticity and the complex spatial variations in FGMs.This paper presents a novel hybrid numerical approach combining Physics-Informed Neural Networks(PINNs)with Finite Element Method(FEM)derived data for the robust analysis of thick-walled,axisymmetric,heterogeneous,hyperelastic pressure vessels with elliptical geometries.A PINN framework incorporating neo-Hookean constitutive relations is developed in MATLAB.To enhance training efficiency and accuracy,the PINN’s loss function is augmented with displacement data obtained from high-fidelity FEM simulations performed in ANSYS.The methodology is rigorously validated by comparing PINN-predicted displacement and von Mises stress fields against ANSYS benchmarks for various scenarios of FGMconfigurations(with material properties varying according to a power law)subjected to internal and external pressurization.The results demonstrate excellent agreement between the proposed hybrid PINN-FEMapproach and conventional FEMsolutions across all test cases,accurately capturing complex deformation patterns and stress concentrations.This study highlights the potential of data-augmented PINNs as an effective and accurate computational tool for tackling complex solid mechanics problems involving non-linearmaterials and significant heterogeneity,offering a promising avenue for future research in engineering design and analysis.展开更多
Fatigue crack growth is a critical phenomenon in engineering structures,accounting for a significant percentage of structural failures across various industries.Accurate prediction of crack initiation,propagation path...Fatigue crack growth is a critical phenomenon in engineering structures,accounting for a significant percentage of structural failures across various industries.Accurate prediction of crack initiation,propagation paths,and fatigue life is essential for ensuring structural integrity and optimizing maintenance schedules.This paper presents a comprehensive finite element approach for simulating two-dimensional fatigue crack growth under linear elastic conditionswith adaptivemesh generation.The source code for the programwas developed in Fortran 95 and compiled with Visual Fortran.To achieve high-fidelity simulations,the methodology integrates several key features:it employs an automatic,adaptive meshing technique that selectively refines the element density near the crack front and areas of significant stress concentration.Specialized singular elements are used at the crack tip to ensure precise stress field representation.The direction of crack advancement is predicted using the maximum tangential stress criterion,while stress intensity factors are determined through either the displacement extrapolation technique or the J-integral method.The simulation models crack growth as a series of linear increments,with solution stability maintained by a consistent transfer algorithm and a crack relaxation method.The framework’s effectiveness is demonstrated across various geometries and loading scenarios.Through rigorous validation against both experimental data and established numerical benchmarks,the approach is proven to accurately forecast crack trajectories and fatigue life.Furthermore,the detailed description of the program’s architecture offers a foundational blueprint,serving as a valuable guide for researchers aiming to develop their specialized software for fracture mechanics analysis.展开更多
为研究声屏障降噪的主要影响因素及规律,基于边界元理论,结合高速列车实测声源识别结果,建立了高速铁路声屏障降噪效果预测模型,研究了包括高速列车不同位置声源、声屏障高度、声屏障截面形状和吸声边界条件对插入损失的影响,并在此基...为研究声屏障降噪的主要影响因素及规律,基于边界元理论,结合高速列车实测声源识别结果,建立了高速铁路声屏障降噪效果预测模型,研究了包括高速列车不同位置声源、声屏障高度、声屏障截面形状和吸声边界条件对插入损失的影响,并在此基础上提出了对现役声屏障结构的改进方案.研究结果表明,列车声源高度对声屏障插入损失有重要影响,现有2.15 m高声屏障只对车体下方噪声有降噪效果;随着声屏障高度增加,插入损失逐渐增大,声屏障高于6.15 m时,插入损失达到25 d B（A）以上;对于不同截面形式的声屏障,降噪效果从优到劣依次为Y型、倾斜型、T型、外折型、直立型和内折型,其中Y型比直立型插入损失高0.7～1.5 d B（A）;对于任一类型声屏障,吸声引起的具体降噪效果与声屏障形式有关,有吸声边界条件的降噪效果要优于＂刚性光滑＂边界条件,前者与后者相比,其插入损失可提高0.3～6.4 dB（A）。展开更多
文摘The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials,such as hyperelastic and functionally graded materials(FGMs),is critical for ensuring their safety and optimizing their design.However,conventional numerical methods can face challenges with the non-linearities inherent in hyperelasticity and the complex spatial variations in FGMs.This paper presents a novel hybrid numerical approach combining Physics-Informed Neural Networks(PINNs)with Finite Element Method(FEM)derived data for the robust analysis of thick-walled,axisymmetric,heterogeneous,hyperelastic pressure vessels with elliptical geometries.A PINN framework incorporating neo-Hookean constitutive relations is developed in MATLAB.To enhance training efficiency and accuracy,the PINN’s loss function is augmented with displacement data obtained from high-fidelity FEM simulations performed in ANSYS.The methodology is rigorously validated by comparing PINN-predicted displacement and von Mises stress fields against ANSYS benchmarks for various scenarios of FGMconfigurations(with material properties varying according to a power law)subjected to internal and external pressurization.The results demonstrate excellent agreement between the proposed hybrid PINN-FEMapproach and conventional FEMsolutions across all test cases,accurately capturing complex deformation patterns and stress concentrations.This study highlights the potential of data-augmented PINNs as an effective and accurate computational tool for tackling complex solid mechanics problems involving non-linearmaterials and significant heterogeneity,offering a promising avenue for future research in engineering design and analysis.
基金funding of the Deanship of Graduate Studies and Scientific Research,Jazan University,Saudi Arabia,through Project number:JU-20250230-DGSSR-RP-2025.
文摘Fatigue crack growth is a critical phenomenon in engineering structures,accounting for a significant percentage of structural failures across various industries.Accurate prediction of crack initiation,propagation paths,and fatigue life is essential for ensuring structural integrity and optimizing maintenance schedules.This paper presents a comprehensive finite element approach for simulating two-dimensional fatigue crack growth under linear elastic conditionswith adaptivemesh generation.The source code for the programwas developed in Fortran 95 and compiled with Visual Fortran.To achieve high-fidelity simulations,the methodology integrates several key features:it employs an automatic,adaptive meshing technique that selectively refines the element density near the crack front and areas of significant stress concentration.Specialized singular elements are used at the crack tip to ensure precise stress field representation.The direction of crack advancement is predicted using the maximum tangential stress criterion,while stress intensity factors are determined through either the displacement extrapolation technique or the J-integral method.The simulation models crack growth as a series of linear increments,with solution stability maintained by a consistent transfer algorithm and a crack relaxation method.The framework’s effectiveness is demonstrated across various geometries and loading scenarios.Through rigorous validation against both experimental data and established numerical benchmarks,the approach is proven to accurately forecast crack trajectories and fatigue life.Furthermore,the detailed description of the program’s architecture offers a foundational blueprint,serving as a valuable guide for researchers aiming to develop their specialized software for fracture mechanics analysis.
文摘为研究声屏障降噪的主要影响因素及规律,基于边界元理论,结合高速列车实测声源识别结果,建立了高速铁路声屏障降噪效果预测模型,研究了包括高速列车不同位置声源、声屏障高度、声屏障截面形状和吸声边界条件对插入损失的影响,并在此基础上提出了对现役声屏障结构的改进方案.研究结果表明,列车声源高度对声屏障插入损失有重要影响,现有2.15 m高声屏障只对车体下方噪声有降噪效果;随着声屏障高度增加,插入损失逐渐增大,声屏障高于6.15 m时,插入损失达到25 d B（A）以上;对于不同截面形式的声屏障,降噪效果从优到劣依次为Y型、倾斜型、T型、外折型、直立型和内折型,其中Y型比直立型插入损失高0.7～1.5 d B（A）;对于任一类型声屏障,吸声引起的具体降噪效果与声屏障形式有关,有吸声边界条件的降噪效果要优于＂刚性光滑＂边界条件,前者与后者相比,其插入损失可提高0.3～6.4 dB（A）。
