Short pitch corrugation has been a problem for railways worldwide over one century.In this paper,a parametric investigation of fastenings is conducted to understand the corrugation formation mechanism and gain insight...Short pitch corrugation has been a problem for railways worldwide over one century.In this paper,a parametric investigation of fastenings is conducted to understand the corrugation formation mechanism and gain insights into corrugation mitigation.A three-dimensional finite element vehicle-track dynamic interaction model is employed,which considers the coupling between the structural dynamics and the contact mechanics,while the damage mechanism is assumed to be differential wear.Various fastening models with different configurations,boundary conditions,and parameters of stiffness and damping are built up and analysed.These models may represent different service stages of fastenings in the field.Besides,the effect of train speeds on corrugation features is studied.The results indicate:(1)Fastening parameters and modelling play an important role in corrugation formation.(2)The fastening longitudinal constraint to the rail is the major factor that determines the corrugation formation.The fastening vertical and lateral constraints influence corrugation features in terms of spatial distribution and wavelength components.(3)The strengthening of fastening constraints in the longitudinal dimension helps to mitigate corrugation.Meanwhile,the inner fastening constraint in the lateral direction is necessary for corrugation alleviation.(4)The increase in fastening longitudinal stiffness and damping can reduce the vibration amplitudes of longitudinal compression modes and thus reduce the track corrugation propensity.The simulation in this work can well explain the field corrugation in terms of the occurrence possibility and major wavelength components.It can also explain the field data with respect to the small variation between the corrugation wavelength and train speed,which is caused by frequency selection and jump between rail longitudinal compression modes.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
Chemical randomness and the associated energy fluctuation are essential features of multi-principal ele-ment alloys(MPEAs).Due to these features,nanoscale stacking fault energy(SFE)fluctuation is a natural and indepen...Chemical randomness and the associated energy fluctuation are essential features of multi-principal ele-ment alloys(MPEAs).Due to these features,nanoscale stacking fault energy(SFE)fluctuation is a natural and independent contribution to strengthening MPEAs.However,existing models for conventional alloys(i.e.,alloys with one principal element)cannot be applied to MPEAs.The extreme values of SFEs required by such models are unknown for MPEAs,which need to calculate the nanoscale volume relevant to the SFE fluctuation.In the present work,we developed an analytic model to evaluate the strengthening ef-fect through the SFE fluctuation,profuse in MPEAs.The model has no adjustable parameters,and all parameters can be determined from experiments and ab initio calculations.This model explains available experimental observations and provides insightful guidance for designing new MPEAs based on the SFE fluctuation.It generally applies to MPEAs in random states and with chemical short-range order.展开更多
Bimetallic alloys could form three typical structures including solid solution,heterostructure,and intermetallic compound,depending on the interactions between identical and different atoms.Although the trend can be p...Bimetallic alloys could form three typical structures including solid solution,heterostructure,and intermetallic compound,depending on the interactions between identical and different atoms.Although the trend can be predicted by the types of binary phase diagram,different synthetic protocols will trap the system in various kinetic intermediates among the three typical structures.Herein,we studied the phase evolution and elemental segregation in the alloy nanoparticles of immiscible Pd-Ru before and after thermal annealing.By developing an analysis method of local element segregation(LES)based on the energy dispersive spectroscopy(EDS)mapping signals,we were able to quantify the mixing of Pd and Ru atoms during the gradual phase transition from face-centered cubic(fcc)to hexagonal close packed(hcp).Density functional theory was also applied to calculate the energies of all possible PdRu4 structures(93 fcc models and 267 hcp models),which helps to rationalize the phase transition and element segregation.The annealing process also leads to the change of the electronic structure,which further influences the performance in the electrocatalytic hydrogen evolution reaction.The highest activity of PdRu4-400 was largely attributed to the proper interface between the Pd-rich fcc phase and Ru-rich hcp phase,as revolved by the above methods.展开更多
以ABB 12 k V开关柜三相平行母线排为对象,利用COMSOL 5.0建立母线排有限元分析模型,计算短路稳态与暂态下母线排所受电动力大小,并且绘制出三相母线所受电动力在x-y平面内的分布和随时间变化的规律.将仿真与经典公式计算结果进行对比,...以ABB 12 k V开关柜三相平行母线排为对象,利用COMSOL 5.0建立母线排有限元分析模型,计算短路稳态与暂态下母线排所受电动力大小,并且绘制出三相母线所受电动力在x-y平面内的分布和随时间变化的规律.将仿真与经典公式计算结果进行对比,验证了该计算模型的有效性.仿真结果表明:各相所受电动力均是交变的,其频率为电流频率的两倍;暂态下,三相母线中B相所受到电动力最大,为6 989.4 N,为A相或C相所受电动力的1.07倍并且暂态分量存在了0.16 s.仿真结果为12 k V开关柜母线结构优化设计和电动稳定性分析提供参考依据.展开更多
文摘Short pitch corrugation has been a problem for railways worldwide over one century.In this paper,a parametric investigation of fastenings is conducted to understand the corrugation formation mechanism and gain insights into corrugation mitigation.A three-dimensional finite element vehicle-track dynamic interaction model is employed,which considers the coupling between the structural dynamics and the contact mechanics,while the damage mechanism is assumed to be differential wear.Various fastening models with different configurations,boundary conditions,and parameters of stiffness and damping are built up and analysed.These models may represent different service stages of fastenings in the field.Besides,the effect of train speeds on corrugation features is studied.The results indicate:(1)Fastening parameters and modelling play an important role in corrugation formation.(2)The fastening longitudinal constraint to the rail is the major factor that determines the corrugation formation.The fastening vertical and lateral constraints influence corrugation features in terms of spatial distribution and wavelength components.(3)The strengthening of fastening constraints in the longitudinal dimension helps to mitigate corrugation.Meanwhile,the inner fastening constraint in the lateral direction is necessary for corrugation alleviation.(4)The increase in fastening longitudinal stiffness and damping can reduce the vibration amplitudes of longitudinal compression modes and thus reduce the track corrugation propensity.The simulation in this work can well explain the field corrugation in terms of the occurrence possibility and major wavelength components.It can also explain the field data with respect to the small variation between the corrugation wavelength and train speed,which is caused by frequency selection and jump between rail longitudinal compression modes.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
基金sponsored by the U.S.Department of En-ergy,Office of Science,Basic Energy Sciences,Materials Science and Engineering Divisionsupported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725+2 种基金the supports from(1)the National Science Foundation(DMR-1611180 and 1809640)with program directors,Drs.J.Yang,G.Shifletthe US Army Research Office(W911NF-13-1-0438 and W911NF-19-2-0049)with program managers,Drs.M.P.Bakas,S.N.Math-audhuthe support of U.S.Na-tional Science Foundation under grant DMR-1804320.
文摘Chemical randomness and the associated energy fluctuation are essential features of multi-principal ele-ment alloys(MPEAs).Due to these features,nanoscale stacking fault energy(SFE)fluctuation is a natural and independent contribution to strengthening MPEAs.However,existing models for conventional alloys(i.e.,alloys with one principal element)cannot be applied to MPEAs.The extreme values of SFEs required by such models are unknown for MPEAs,which need to calculate the nanoscale volume relevant to the SFE fluctuation.In the present work,we developed an analytic model to evaluate the strengthening ef-fect through the SFE fluctuation,profuse in MPEAs.The model has no adjustable parameters,and all parameters can be determined from experiments and ab initio calculations.This model explains available experimental observations and provides insightful guidance for designing new MPEAs based on the SFE fluctuation.It generally applies to MPEAs in random states and with chemical short-range order.
基金This work was financially supported by the National Natural Science Foundation of Tianjin,China(No.22175127)Institute of Energy,Hefei Comprehensive National Science Center(No.19KZS207).
文摘Bimetallic alloys could form three typical structures including solid solution,heterostructure,and intermetallic compound,depending on the interactions between identical and different atoms.Although the trend can be predicted by the types of binary phase diagram,different synthetic protocols will trap the system in various kinetic intermediates among the three typical structures.Herein,we studied the phase evolution and elemental segregation in the alloy nanoparticles of immiscible Pd-Ru before and after thermal annealing.By developing an analysis method of local element segregation(LES)based on the energy dispersive spectroscopy(EDS)mapping signals,we were able to quantify the mixing of Pd and Ru atoms during the gradual phase transition from face-centered cubic(fcc)to hexagonal close packed(hcp).Density functional theory was also applied to calculate the energies of all possible PdRu4 structures(93 fcc models and 267 hcp models),which helps to rationalize the phase transition and element segregation.The annealing process also leads to the change of the electronic structure,which further influences the performance in the electrocatalytic hydrogen evolution reaction.The highest activity of PdRu4-400 was largely attributed to the proper interface between the Pd-rich fcc phase and Ru-rich hcp phase,as revolved by the above methods.
文摘以ABB 12 k V开关柜三相平行母线排为对象,利用COMSOL 5.0建立母线排有限元分析模型,计算短路稳态与暂态下母线排所受电动力大小,并且绘制出三相母线所受电动力在x-y平面内的分布和随时间变化的规律.将仿真与经典公式计算结果进行对比,验证了该计算模型的有效性.仿真结果表明:各相所受电动力均是交变的,其频率为电流频率的两倍;暂态下,三相母线中B相所受到电动力最大,为6 989.4 N,为A相或C相所受电动力的1.07倍并且暂态分量存在了0.16 s.仿真结果为12 k V开关柜母线结构优化设计和电动稳定性分析提供参考依据.
