检索结果-内蒙古大学图书馆

MAISE: Construction of neural network interatomic models and evolutionary structure optimization

COMPUTER PHYSICS COMMUNICATIONS 2021年 259卷 107679-107679页

作者： Hajinazar, Samad Thorn, Aidan Sandoval, Ernesto D. Kharabadze, Saba Kolmogorov, Aleksey N. SUNY Binghamton Dept Phys Appl Phys & Astron POB 6000 Binghamton NY 13902 USA

Module for ab initio structure evolution (MAISE) is an open-source package for materials modeling and prediction. The code's main feature is an automated generation of neural network (NN) interatomic potentials for use in global structure searches. The systematic construction of Behler-Parrinello-type NN models approximating ab initio energy and forces relies on two approaches introduced in our recent studies. An evolutionary sampling scheme for generating reference structures improves the NNs' mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the applicability of the stratified scheme for an arbitrary number of elements. The full workflow in the NN development is managed with a customizable 'MAISE-NET' wrapper written in Python. The global structure optimization capability in MAISE is based on an evolutionary algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe extension of the algorithm allows for an efficient simultaneous optimization of nanoparticles in a given size range. Implemented structure analysis functions include fingerprinting with radial distribution functions and finding space groups with the SPGLIB tool. This work overviews MAISE's available features, constructed models, and confirmed predictions. Program summary Program Title: MAISE CPC Library link to program files: https://***/10.17632/vfzgt2gnsh.1 Licensing provisions: GNU General Public License v3.0 Programming language: C Nature of problem: Construction of NN interatomic potentials suitable for evolutionary structure searches, molecular dynamics, phonon calculations, and other applications presents a host of challenges ranging from sampling relevant parts of vast configuration spaces to tuning multitudes of NN parameters. Solution method: evolutionary data generation and modular NN training algorithms f

关键词： Neural network interatomic potentials evolutionary structure optimization Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Optimal structural topology of materials with micro-scale tension-compression asymmetry simulated using granular micromechanics

引用

MATERIALS & DESIGN 2017年 115卷 422-432页

作者： Jia, Haipeng Misra, Anil Poorsolhjouy, Payam Liu, Congyi Hebei Univ Technol Tianjin Dept Mech Engn Tianjin 300130 Peoples R China Univ Kansas Civil Environm & Architectural Engn Dept Bioengn Res Ctr BERC 1530 W 15th StLearned Hall Lawrence KS 66045 USA Shanghai Jiao Tong Univ State Key Lab Ocean Engn Dept Civil Engn Dongchuan Rd Shanghai 200240 Peoples R China

Material systems derived from particulate precursor exhibit significant micro-scale tension-compression asymmetry under loading which leads to macro-scale material anisotropy. Topology optimization of structures composed of such materials could suggest new unique topologies as well as material anisotropy distribution. This paper utilizes the granular micromechanics method that relates the micro-scale interactions to the macroscale for defining material constitutive relationship. The resultant constitutive relationship is implemented in the evolutionary structure optimization algorithm based upon finite element analysis. Benchmark problems are simulated to investigate the influences of degrees of asymmetry on structure topologies and to illustrate how material anisotropy evolves under given boundary conditions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Tension-compression asymmetry Granular micromechanics structure topology optimization evolutionary structure optimization Finite element analysis

来源：评论

学校读者我要写书评

暂无评论

Topology optimization of structure with Asymmetry Cementitious Granular Material

Topology Optimization of Structure with Asymmetry Cementitio...

引用

2016 International Conference on Modeling, Simulation and optimization Technologies and Applications（MSOTA 2016）

作者： Haipeng Jia Qunying Men Anil Misra Congyi Liu Department of Mechanical Engineering Hebei University of Technology Civil Environmental and Architectural Engineering DepartmentBioengineering Research Center (BERC)University of Kansas

ISBN: (纸本)9781510835702

Topology optimization of structure of cementitious granular material with different tension and compression properties is one of challenging and important branches in engineering application for the complex stress and strain relationship of cohesive material. Applying evolutionary structure optimization technique, this paper proposes the mathematical formulation and computational algorithm of structure topology optimization of cohesive granular material based on the tension-compression asymmetry constitutive equation. The objective function is to minimize the mean compliance of structure. Numerical example illustrated the effectiveness of the proposed algorithm.

关键词： granular cementitious material tension-compression asymmetry structure topology optimization evolutionary structure optimization

来源：评论

学校读者我要写书评

暂无评论

evolutionary level set method for structural topology optimization

引用

COMPUTERS & structureS 2011年第5-6期89卷 445-454页

作者： Jia, Haipeng Beom, H. G. Wang, Yuxin Lin, Song Liu, Bo Inha Univ Dept Mech Engn Inchon 402751 South Korea Hebei Univ Technol Dept Mech Engn Tianjin 300130 Peoples R China

This paper proposes an evolutionary accelerated computational level set algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization (ESO) and level set method (LSM). Traditional LSM algorithm is largely dependent on the initial guess topology. The proposed method combines the merits of ESO techniques with those of LSM algorithm, while allowing new holes to be automatically generated in low strain energy within the nodal neighboring region during optimization. The validity and robustness of the new algorithm are supported by some widely used benchmark examples in topology optimization. Numerical computations show that optimization convergence is accelerated effectively. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.

关键词： evolutionary structure optimization structure topology optimization Intelligent computation Level set method

来源：评论

学校读者我要写书评

暂无评论

Nodal evolutionary Computation Enhanced Level set Algorithm for Structural Topology optimization

Nodal Evolutionary Computation Enhanced Level set Algorithm ...

引用

International Conference on Applied Mechanics and Mechanical Engineering

作者： Jia, Haipeng Jiang, Chundong Liu, Bo Cao, Dongxing Jiang, Chunbo Heibei Univ Technol Tianjin 300130 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China

ISBN: (纸本)9780878492459

This paper proposes an improved computational algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization and Level Set Method (LSM) for structure topology optimization. Traditional LSM algorithm has some drawbacks, for instance, its optimal topology configuration is largely dependent on the structural topology initialization. Additionally, new holes cannot be evolved within the updated topology during the optimization iteration. The method proposed in this paper combines the merits of ESO techniques with the LSM scheme, allowing new holes to be automatically inserted in regions with low deformation energy at prescribed iterations of the optimization. The nodal neighboring region is a good selection. For complex structures in which holes cannot be properly inserted in advance, the proposed method considerably improves the ability of LSM to search the optimal topology. In addition to achieving more accurate results, the proposed method also yields higher efficiency during optimization. Benchmark problems are presented to show the effectiveness and robustness of the new proposed algorithm.

关键词： structure optimization Topology optimization evolutionary Computation evolutionary structure optimization Level Set Method

来源：评论

学校读者我要写书评

暂无评论

evolutionary Algorithm and its Application in Structural Topology optimization

Evolutionary Algorithm and its Application in Structural Top...

引用

27th Chinese Control Conference

作者： Jiang Chundong Jia Haipeng Du Taihang Zhang Lei Chunbo Jiang Hebei Univ Technol Tianjin 300130 Peoples R China China Coal Zhangjiakou Coal Mining Machinery Coop Zhangjiakou 075025 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China

ISBN: (纸本)9787900719706

This paper proposes a bio-inspired evolutionary computational algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization and level set method for structure topology optimization. Traditional Level Set Method algorithm depends on the initialil topology to some extent. New holes cannot be evolved within the updated topology during the optimization iteration. The algorithm proposed in this paper combines the merits of ESO techniques with that of LSM scheme, allowing new holes to be automatically inserted in regions with low deformation energy at prescribed iterations of the optimization. The nodal neighboring region is adopted in the proposed algorithm. Numerical example shows that good result can be gotten through less optimization iteration. The approach can solve complex structures easily, in which holes cannot be properly layed out in advance. The proposed method considerably improves the ability of LSM to find the optimal topology. The validity and efficiency of the proposed algorithm are supported by some benchmark examples in this paper.

关键词： Intelligent Computation evolutionary structure optimization structure Topology optimization Level Set Method Numerical Computation

来源：评论

学校读者我要写书评

暂无评论

Intelligent Computation Algorithm in Structural Design optimization

Intelligent Computation Algorithm in Structural Design Optim...

引用

20th Chinese Control and Decision Conference

作者： Jiang Chundong Jia Haipeng Du Taihang Zhang Ping Zheng Yi Jiang Chunbo Hebei Univ Technol Tianjin 300130 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China

ISBN: (纸本)9781424417339

An intelligent computational algorithm is proposed for structure topology optimization in this paper. It integrates the merits of evolutionary structure optimization (ESO) and Level Set Method (LSM), and overcome some drawbacks in traditional LSM algorithm. The established algorithm allows new holes to be automatically inserted in regions with low deformation energy during optimization and thus the final optimal topology configuration is independent of initial structural topology. Compared with evolutionary structure optimization for structure optimization, the final topology possesses smooth boundary. The new algorithm is efficient for structures that have complex boundaries. The validity and robustness are supported by some benchmark problems in topology optimization.

关键词： Intelligent Computation evolutionary structure optimization Topology optimization Level Set Method

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：