Current multiscale topology optimization restricts the solution space by enforcing the use of a few repetitive microstructures that are predetermined,and thus lack the ability for structural concerns like buckling str...Current multiscale topology optimization restricts the solution space by enforcing the use of a few repetitive microstructures that are predetermined,and thus lack the ability for structural concerns like buckling strength,robustness,and multi-functionality.Therefore,in this paper,a new multiscale concurrent topology optimization design,referred to as the self-consistent analysis-based moving morphable component(SMMC)method,is proposed.Compared with the conventional moving morphable component method,the proposed method seeks to optimize both material and structure simultaneously by explicitly designing both macrostructure and representative volume element(RVE)-level microstructures.Numerical examples with transducer design requirements are provided to demonstrate the superiority of the SMMC method in comparison to traditional methods.The proposed method has broad impact in areas of integrated industrial manufacturing design:to solve for the optimized macro and microstructures under the objective function and constraints,to calculate the structural response efficiently using a reduced-order model:self-consistent analysis,and to link the SMMC method to manufacturing(industrial manufacturing or additive manufacturing)based on the design requirements and application areas.展开更多
This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways.It incorporates a multi-objective genetic algorithm,training and ...This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways.It incorporates a multi-objective genetic algorithm,training and screening property sets,and correlation and principal component analyses.The framework enables iterative definition of properties in the training and screening sets,guided by correlation relationships between properties,aiming to achieve optimal parametrizations for properties of interest.Specifically,the performance of increasingly complex potentials,Buckingham,Stillinger-Weber,Tersoff,and modified reactive empirical bond-order potentials are compared.Using MoSe_(2)as a case study,we demonstrate good reproducibility of training/screening properties and superior transferability.For MoSe_(2),the best performance is achieved using the Tersoff potential,which is ascribed to its apparent higher flexibility embedded in its functional form.These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.展开更多
文摘Current multiscale topology optimization restricts the solution space by enforcing the use of a few repetitive microstructures that are predetermined,and thus lack the ability for structural concerns like buckling strength,robustness,and multi-functionality.Therefore,in this paper,a new multiscale concurrent topology optimization design,referred to as the self-consistent analysis-based moving morphable component(SMMC)method,is proposed.Compared with the conventional moving morphable component method,the proposed method seeks to optimize both material and structure simultaneously by explicitly designing both macrostructure and representative volume element(RVE)-level microstructures.Numerical examples with transducer design requirements are provided to demonstrate the superiority of the SMMC method in comparison to traditional methods.The proposed method has broad impact in areas of integrated industrial manufacturing design:to solve for the optimized macro and microstructures under the objective function and constraints,to calculate the structural response efficiently using a reduced-order model:self-consistent analysis,and to link the SMMC method to manufacturing(industrial manufacturing or additive manufacturing)based on the design requirements and application areas.
基金The authors acknowledge the support of the National Science Foundation,through award CMMI 1953806computational resources provided by the Center of Nanoscale Materials at Argonne National Laboratory,as well as the Quest High Performance Computing Cluster at Northwestern University.Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357The authors acknowledge Dr.Henry Chan for the helpful discussions and suggestions.
文摘This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways.It incorporates a multi-objective genetic algorithm,training and screening property sets,and correlation and principal component analyses.The framework enables iterative definition of properties in the training and screening sets,guided by correlation relationships between properties,aiming to achieve optimal parametrizations for properties of interest.Specifically,the performance of increasingly complex potentials,Buckingham,Stillinger-Weber,Tersoff,and modified reactive empirical bond-order potentials are compared.Using MoSe_(2)as a case study,we demonstrate good reproducibility of training/screening properties and superior transferability.For MoSe_(2),the best performance is achieved using the Tersoff potential,which is ascribed to its apparent higher flexibility embedded in its functional form.These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.
