Probabilistic Analysis and Design of HCP Nanowires:An Efficient Surrogate Based Molecular Dynamics Simulation Approach

作     者：T.Mukhopadhyay A.Mahata S.Dey S.Adhikari 

作者机构：College of EngineeringSwansea UniversitySwanseaUnited Kingdom Department of Materials Science and EngineeringMissouri University of Science and TechnologyRollaMOUSA Indian Institute of ScienceBangaloreIndia Leibniz-Institut fur Polymerforschung Dresden e.V.DresdenGermany 

出 版 物：《Journal of Materials Science & Technology》 (材料科学技术（英文版）)

年 卷 期：2016年第32卷第12期

页      面：1345-1351页

核心收录：

学科分类：07[理学] 070205[理学-凝聚态物理] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：the financial support from Swansea University through the award of Zienkiewicz Scholarship the financial support from The Royal Society of London through the Wolfson Research Merit award 

主　　题：hcp nanowire Yield strength Surrogate Monte Carlo simulation Uncertainty in nanoscale Sensitivity 

摘      要：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 *** of the major drawbacks of conventional MD simulation based studies is that the simulations are computationally very intensive and economically *** scale molecular dynamics simulation needs supercomputing access and the larger the number of atoms,the longer it takes time and computational *** this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis,uncertainty quantification and *** 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 *** the SBMD simulation approach an efficient optimum design scheme has been developed to predict optimized size of the nanowire to maximize the yield *** the effect of inevitable uncertainty associated with the controlling factors has been quantified using Monte Carlo *** 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.