We investigate the dependency of strain rate,temperature and size on yield strength of hexagonal close packed（HCP） nanowires based on large-scale molecular dynamics（MD） simulation.A variance-based analysis has bee...We investigate the dependency of strain rate,temperature and size on yield strength of hexagonal close packed（HCP） nanowires based on large-scale molecular dynamics（MD） simulation.A variance-based analysis has been proposed to quantify relative sensitivity of the three controlling factors on the yield strength of the material.One of the major drawbacks of conventional MD simulation based studies is that the simulations are computationally very intensive and economically expensive.Large scale molecular dynamics simulation needs supercomputing access and the larger the number of atoms,the longer it takes time and computational resources.For this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis,uncertainty quantification and optimization.We propose a novel surrogate based molecular dynamics（SBMD）simulation approach that enables us to carry out thousands of virtual simulations for different combinations of the controlling factors in a computationally efficient way by performing only few MD simulations.Following the SBMD simulation approach an efficient optimum design scheme has been developed to predict optimized size of the nanowire to maximize the yield strength.Subsequently the effect of inevitable uncertainty associated with the controlling factors has been quantified using Monte Carlo simulation.Though we have confined our analyses in this article for Magnesium nanowires only,the proposed approach can be extended to other materials for computationally intensive nano-scale investigation involving multiple factors of influence.展开更多
Solidification phenomenon has been an integral part of the manufacturing processes of metals,where the quantification of stochastic variations and manufacturing uncertainties is critically important.Accurate molecular...Solidification phenomenon has been an integral part of the manufacturing processes of metals,where the quantification of stochastic variations and manufacturing uncertainties is critically important.Accurate molecular dynamics(MD)simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary.The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization,causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo(MC)simulations.In this work,we present a machine learning(ML)approach,as a data-driven surrogate to MD simulations,which only needs a few MD simulations.This efficient yet high-fidelity ML approach enables MC simulations for fullscale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate.Unlike conventional ML models,the proposed hybrid polynomial correlated function expansion here,being a Bayesian ML approach,is data efficient.Further,it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations,which in principle addresses the issue of repeatability in stochastic simulations with low variance.Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus,yield strength and ultimate strength,illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.展开更多
基金the financial support from Swansea University through the award of Zienkiewicz Scholarshipthe financial support from The Royal Society of London through the Wolfson Research Merit award
文摘We investigate the dependency of strain rate,temperature and size on yield strength of hexagonal close packed（HCP） nanowires based on large-scale molecular dynamics（MD） simulation.A variance-based analysis has been proposed to quantify relative sensitivity of the three controlling factors on the yield strength of the material.One of the major drawbacks of conventional MD simulation based studies is that the simulations are computationally very intensive and economically expensive.Large scale molecular dynamics simulation needs supercomputing access and the larger the number of atoms,the longer it takes time and computational resources.For this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis,uncertainty quantification and optimization.We propose a novel surrogate based molecular dynamics（SBMD）simulation approach that enables us to carry out thousands of virtual simulations for different combinations of the controlling factors in a computationally efficient way by performing only few MD simulations.Following the SBMD simulation approach an efficient optimum design scheme has been developed to predict optimized size of the nanowire to maximize the yield strength.Subsequently the effect of inevitable uncertainty associated with the controlling factors has been quantified using Monte Carlo simulation.Though we have confined our analyses in this article for Magnesium nanowires only,the proposed approach can be extended to other materials for computationally intensive nano-scale investigation involving multiple factors of influence.
基金supported by the National Science Foundation,CMMI 2031800The authors are grateful for the supercomputing time allocation provided by the NSF’s ACCESS(Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support),Award No.DMR140008 and MAT210018.
文摘Solidification phenomenon has been an integral part of the manufacturing processes of metals,where the quantification of stochastic variations and manufacturing uncertainties is critically important.Accurate molecular dynamics(MD)simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary.The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization,causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo(MC)simulations.In this work,we present a machine learning(ML)approach,as a data-driven surrogate to MD simulations,which only needs a few MD simulations.This efficient yet high-fidelity ML approach enables MC simulations for fullscale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate.Unlike conventional ML models,the proposed hybrid polynomial correlated function expansion here,being a Bayesian ML approach,is data efficient.Further,it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations,which in principle addresses the issue of repeatability in stochastic simulations with low variance.Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus,yield strength and ultimate strength,illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.
