Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The...Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.展开更多
Modeling the elastic behavior of solids in energy conversion and storage devices such as fuel cells and lithium-ion batteries is usually difficult because of the nonlinear characteristics and the coupled chemo-mechani...Modeling the elastic behavior of solids in energy conversion and storage devices such as fuel cells and lithium-ion batteries is usually difficult because of the nonlinear characteristics and the coupled chemo-mechanical behavior of these solids.In this work,a perturbation finite element(FE)formulation is developed to analyze chemo-elastic boundary value problems(BVPs)under chemical equilibrium.The perturbation method is applied to the FE equations because of the nonlinearity in the chemical potential expression as a function of solute concentration.The compositional expansion coefficient is used as the perturbation parameter.After the perturbation expansion,a system of partial differential equations for the displacement and dimensionless solute concentration functions is obtained and solved in consecutive steps.The presence of a numerical solution enables modeling 3D chemo-elastic solids,such as battery electrodes or ionic gels,of any geometric shape with defects of different shapes.The proposed method is tested in several numerical examples such as plates with circular or elliptical holes,and cracks.The numerical examples showed how the shape of the defect can change the distribution of solute concentration around the defect.Cracks in chemo-elastic solids create sharp peaks in solute concentration around crack tips,and the intensity of these peaks increases as the far field solute concentration decreases.展开更多
文摘Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.
文摘Modeling the elastic behavior of solids in energy conversion and storage devices such as fuel cells and lithium-ion batteries is usually difficult because of the nonlinear characteristics and the coupled chemo-mechanical behavior of these solids.In this work,a perturbation finite element(FE)formulation is developed to analyze chemo-elastic boundary value problems(BVPs)under chemical equilibrium.The perturbation method is applied to the FE equations because of the nonlinearity in the chemical potential expression as a function of solute concentration.The compositional expansion coefficient is used as the perturbation parameter.After the perturbation expansion,a system of partial differential equations for the displacement and dimensionless solute concentration functions is obtained and solved in consecutive steps.The presence of a numerical solution enables modeling 3D chemo-elastic solids,such as battery electrodes or ionic gels,of any geometric shape with defects of different shapes.The proposed method is tested in several numerical examples such as plates with circular or elliptical holes,and cracks.The numerical examples showed how the shape of the defect can change the distribution of solute concentration around the defect.Cracks in chemo-elastic solids create sharp peaks in solute concentration around crack tips,and the intensity of these peaks increases as the far field solute concentration decreases.
