This paper proposes a physics-informed neural network(PINN)framework to analyze the nonlinear buckling behavior of a three-dimensional(3D)FG porous,slender beam resting on a Winkler-Pasternak foundation.PINNs need muc...This paper proposes a physics-informed neural network(PINN)framework to analyze the nonlinear buckling behavior of a three-dimensional(3D)FG porous,slender beam resting on a Winkler-Pasternak foundation.PINNs need much less training data to obtain high accuracy using a straightforward network.The powerful tool used in this work can handle any class of PDEs.We use the deep learning platform TensorFlow and DeepXDE library to design our network.In this study,the PINNs framework takes information from the governing differential equations of the beam system and the data from boundary conditions and outputs the critical nonlinear buckling load.The mathematical model is developed using Hamilton’s principle,considering geometry’s nonlinearity.The accuracy of the modeling framework is carefully examined by applying it to various boundary condition cases as well as the physical parameters such as 3D FG indexes on the nonlinear mechanical behaviors.Finally,the PINNs results are validated with those extracted from the generalized differential quadrature method(GDQM).It is found that the proposed PINN framework can characterize the nonlinear buckling behavior of 3D FG porous,slender beams with satisfactory accuracy.Furthermore,PINN is presented to accurately predict the nonlinear buckling behavior of the beam up to 71 times faster than the numerical method.展开更多
Ceramic reinforced metal matrix nanocomposites are widely used in aerospace and auto industries due to their enhanced mechanical and physical properties.In this research,we investigate the mechanical properties of alu...Ceramic reinforced metal matrix nanocomposites are widely used in aerospace and auto industries due to their enhanced mechanical and physical properties.In this research,we investigate the mechanical properties of aluminum/Nano-silica composites through experiments and simulations.Aluminum/Nanosilica composite samples with different weight percentages of silica nanoparticles are prepared via powder metallurgy.In this method,Nano-silica and aluminum powders are mixed and compressed in a mold,followed by sintering at high temperatures.Uniaxial tensile testing of the nanocomposite samples shows that adding one percent of Nano-silica causes a considerable increase in mechanical properties of nanocomposite compared to pure aluminum.A computational micromechanical model,based on a representative volume element of aluminum/silica nanocomposite,is developed in a commercial finite element software.The model employs an elastoplastic material model along with a ductile damage model for aluminum matrix and linear elastic model for nano-silica particles.Via careful determination of model parameters from the experimental results of pure aluminum samples prepared by powder metallurgy,the proposed computational model has shown satisfactory agreement with experiments.The validated computational model can be used to perform a parametric study to optimize the microstructure of nanocomposite for enhanced mechanical properties.展开更多
In nanocomposites,the interphase thickness may be comparable to the size of nano-particles,and hence,the effect of interphase layers on the mechanical properties of nanocomposites may be substantial.The interphase thi...In nanocomposites,the interphase thickness may be comparable to the size of nano-particles,and hence,the effect of interphase layers on the mechanical properties of nanocomposites may be substantial.The interphase thickness to the nano-particle size ratio and properties variability across the interphase thickness are the most important affecting parameters on the overall behavior of nanocomposites.In this study,the effect of properties variability across the interphase thickness on the overall elastic and elastoplastic properties of a polymeric clay nanocomposite(PCN)using a functionally graded(FG)interphase is investigated in detail.The results of the computational homogenization on the mesoscopic level show that Young’s modulus variation of the interphase has a significant effect on the overall elastic response of nanocomposites in a higher clay weight ratio(Wt).Moreover,strength variation through the interphase has a notable effect on the elasto-plastic properties of PCNs.Also,the increase or decrease in stiffness of interphase from clay to matrix and vice versa have a similar effect in the overall behavior of nanocomposites.展开更多
文摘This paper proposes a physics-informed neural network(PINN)framework to analyze the nonlinear buckling behavior of a three-dimensional(3D)FG porous,slender beam resting on a Winkler-Pasternak foundation.PINNs need much less training data to obtain high accuracy using a straightforward network.The powerful tool used in this work can handle any class of PDEs.We use the deep learning platform TensorFlow and DeepXDE library to design our network.In this study,the PINNs framework takes information from the governing differential equations of the beam system and the data from boundary conditions and outputs the critical nonlinear buckling load.The mathematical model is developed using Hamilton’s principle,considering geometry’s nonlinearity.The accuracy of the modeling framework is carefully examined by applying it to various boundary condition cases as well as the physical parameters such as 3D FG indexes on the nonlinear mechanical behaviors.Finally,the PINNs results are validated with those extracted from the generalized differential quadrature method(GDQM).It is found that the proposed PINN framework can characterize the nonlinear buckling behavior of 3D FG porous,slender beams with satisfactory accuracy.Furthermore,PINN is presented to accurately predict the nonlinear buckling behavior of the beam up to 71 times faster than the numerical method.
文摘Ceramic reinforced metal matrix nanocomposites are widely used in aerospace and auto industries due to their enhanced mechanical and physical properties.In this research,we investigate the mechanical properties of aluminum/Nano-silica composites through experiments and simulations.Aluminum/Nanosilica composite samples with different weight percentages of silica nanoparticles are prepared via powder metallurgy.In this method,Nano-silica and aluminum powders are mixed and compressed in a mold,followed by sintering at high temperatures.Uniaxial tensile testing of the nanocomposite samples shows that adding one percent of Nano-silica causes a considerable increase in mechanical properties of nanocomposite compared to pure aluminum.A computational micromechanical model,based on a representative volume element of aluminum/silica nanocomposite,is developed in a commercial finite element software.The model employs an elastoplastic material model along with a ductile damage model for aluminum matrix and linear elastic model for nano-silica particles.Via careful determination of model parameters from the experimental results of pure aluminum samples prepared by powder metallurgy,the proposed computational model has shown satisfactory agreement with experiments.The validated computational model can be used to perform a parametric study to optimize the microstructure of nanocomposite for enhanced mechanical properties.
文摘In nanocomposites,the interphase thickness may be comparable to the size of nano-particles,and hence,the effect of interphase layers on the mechanical properties of nanocomposites may be substantial.The interphase thickness to the nano-particle size ratio and properties variability across the interphase thickness are the most important affecting parameters on the overall behavior of nanocomposites.In this study,the effect of properties variability across the interphase thickness on the overall elastic and elastoplastic properties of a polymeric clay nanocomposite(PCN)using a functionally graded(FG)interphase is investigated in detail.The results of the computational homogenization on the mesoscopic level show that Young’s modulus variation of the interphase has a significant effect on the overall elastic response of nanocomposites in a higher clay weight ratio(Wt).Moreover,strength variation through the interphase has a notable effect on the elasto-plastic properties of PCNs.Also,the increase or decrease in stiffness of interphase from clay to matrix and vice versa have a similar effect in the overall behavior of nanocomposites.
