Predicting the Benefits of Topology Optimization
预测拓扑优化的好处
基本信息
- 批准号:1824980
- 负责人:
- 金额:$ 26.29万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-09-01 至 2021-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
One of the primary tasks of engineers is to optimize designs, i.e., improve the performance of designs, through computer simulation. Since this can be time-consuming and expensive, engineers are faced with the following set of tough questions: Is it worth optimizing a given design? How much can the performance be improved through computer simulation? Can one predict the benefits of design optimization, without investing several days of computing? This project will address these questions through fundamental research on predictive simulation techniques. New algorithms will be established that will permit engineers to rapidly predict the benefits of design optimization, prior to investing time and effort into a detailed investigation. This has the potential to improve industrial competitiveness significantly by allowing designers to focus their limited resources on only the most promising optimization problems. The research team will publish the findings in journals and conference proceedings; the team will also release the resulting software to the scientific community. The team will conduct engineering workshops for high-school students, including underrepresented minority students, on engineering simulation and optimization. Case-studies from local industries and student-car projects will be used as design challenges. The broader impact of the industrial interaction is on budding engineers who will be exposed to a unique blend of research, industrial experience, and formal education.In the project, the concept of Pareto-optimal distances will be used as a metric for predicting the benefits of design optimization. The Pareto-optimal distances, for a given design, is a set of axis-aligned distances to the Pareto-optimal manifold; these distances will be estimated via the topological sensitivity field. Preliminary results indicate that these Pareto-optimal distances can indeed be used to estimate the benefits of design optimization, i.e., greater the distance, greater the benefit. Using this simple concept, designers can make rational decisions on design optimization. The following questions will be addressed in this project: (1) Can the Pareto-optimality concept be applied to problems beyond the structural mechanics problems considered thus far? (2) Can this concept be used to rank-order parts within an assembly for design optimization? (3) How can these concepts be generalized to include constraints, multi-load scenarios, and material design? (4) Can second-order and error-correction strategies be developed to improve the efficacy of these predictive methods?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
工程师的主要任务之一是通过计算机模拟来优化设计,即提高设计的性能。由于这可能既耗时又昂贵,工程师们面临着以下一组棘手的问题:是否值得优化给定的设计?通过计算机模拟,性能可以提高多少?人们能在不投入几天计算的情况下预测设计优化的好处吗?本项目将通过对预测模拟技术的基础研究来解决这些问题。将建立新的算法,允许工程师在投入时间和精力进行详细调查之前,快速预测设计优化的好处。这使得设计者可以将有限的资源集中在最有希望的优化问题上,从而有可能显著提高工业竞争力。研究团队将在期刊和会议记录上发表研究结果;该团队还将向科学界发布结果软件。该团队将为高中生,包括代表不足的少数族裔学生,举办关于工程模拟和优化的工程研讨会。来自当地行业和学生汽车项目的案例研究将被用作设计挑战。工业互动的更广泛影响是对初出茅庐的工程师的影响,他们将接触到研究、工业经验和正规教育的独特结合。在该项目中,帕累托最优距离的概念将被用作预测设计优化好处的指标。对于给定的设计,帕累托最优距离是到帕累托最优流形的一组轴向距离;这些距离将通过拓扑敏感场来估计。初步结果表明,这些帕累托最优距离确实可以用来估计设计优化的效益,即距离越大,效益越大。使用这个简单的概念,设计师可以做出合理的设计优化决策。本项目将解决以下问题:(1)帕累托最优概念能否应用于目前所考虑的结构力学问题以外的问题?(2)该概念能否用于对装配中的部件进行排序以进行设计优化?(3)如何推广这些概念以包括约束、多载荷情景和材料设计?(4)能否开发二阶和纠错策略以提高这些预测方法的效率?该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
AuTO: a framework for Automatic differentiation in Topology Optimization
- DOI:10.1007/s00158-021-03025-8
- 发表时间:2021-04
- 期刊:
- 影响因子:3.9
- 作者:A. Chandrasekhar;S. Sridhara;K. Suresh
- 通讯作者:A. Chandrasekhar;S. Sridhara;K. Suresh
A Review of Methods for the Geometric Post-Processing of Topology Optimized Models
- DOI:10.1115/1.4047429
- 发表时间:2020-06
- 期刊:
- 影响因子:0
- 作者:Subodh C. Subedi;C. Verma;K. Suresh
- 通讯作者:Subodh C. Subedi;C. Verma;K. Suresh
A density-and-strain-based K-clustering approach to microstructural topology optimization
- DOI:10.1007/s00158-019-02422-4
- 发表时间:2019-11
- 期刊:
- 影响因子:3.9
- 作者:T. Kumar;K. Suresh
- 通讯作者:T. Kumar;K. Suresh
Spectral decomposition for graded multi-scale topology optimization
- DOI:10.1016/j.cma.2021.113670
- 发表时间:2021-04
- 期刊:
- 影响因子:7.2
- 作者:T. Kumar;S. Sridhara;B. Prabhune;K. Suresh
- 通讯作者:T. Kumar;S. Sridhara;B. Prabhune;K. Suresh
Experimental validation and microstructure characterization of topology optimized, additively manufactured SS316L components
- DOI:10.1016/j.msea.2020.139050
- 发表时间:2020-03-03
- 期刊:
- 影响因子:6.4
- 作者:Rankouhi, B.;Bertsch, K. M.;Suresh, K.
- 通讯作者:Suresh, K.
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Krishnan Suresh其他文献
TCTAP C-043 Is It Dissection or Plaque Migration Proximally Following Left Anterior Descending Artery Stenting?
- DOI:
10.1016/j.jacc.2015.03.315 - 发表时间:
2015-05-05 - 期刊:
- 影响因子:
- 作者:
Krishnan Suresh;Arshad Mushahafil - 通讯作者:
Arshad Mushahafil
Treetop: topology optimization using constructive solid geometry trees
- DOI:
10.1007/s00158-025-03980-6 - 发表时间:
2025-02-27 - 期刊:
- 影响因子:4.000
- 作者:
Rahul Kumar Padhy;Pramod Thombre;Krishnan Suresh;Aaditya Chandrasekhar - 通讯作者:
Aaditya Chandrasekhar
Deformation constrained support-structure optimization for laser powder bed fusion
- DOI:
10.1016/j.addma.2024.104294 - 发表时间:
2024-06-05 - 期刊:
- 影响因子:
- 作者:
Subodh C. Subedi;Dan J. Thoma;Krishnan Suresh - 通讯作者:
Krishnan Suresh
CAD-integrated analysis of 3-D beams: a surface-integration approach
- DOI:
10.1007/s00366-010-0191-9 - 发表时间:
2010-07-30 - 期刊:
- 影响因子:4.900
- 作者:
Wa’el Abdel Samad;Krishnan Suresh - 通讯作者:
Krishnan Suresh
Optimal Box Contraction for Solving Linear Systems via Simulated and Quantum Annealing
通过模拟和量子退火求解线性系统的最佳盒子收缩
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Sanjay Suresh;Krishnan Suresh - 通讯作者:
Krishnan Suresh
Krishnan Suresh的其他文献
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{{ truncateString('Krishnan Suresh', 18)}}的其他基金
AF: Small: Collaborative Research: A Robust Framework for Overcoming the Tangled Mesh Problem
AF:小型:协作研究:克服网格缠结问题的稳健框架
- 批准号:
1715970 - 财政年份:2017
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
Generalization of Non-Uniform Rational Bezier Splines: Theory and Applications
非均匀有理贝塞尔样条的推广:理论与应用
- 批准号:
1661597 - 财政年份:2017
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
Using Topology Optimization to Reduce Support Structures in Additive Manufacturing
使用拓扑优化来减少增材制造中的支撑结构
- 批准号:
1561899 - 财政年份:2016
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
PFI:AIR - TT: Design Optimization on the Cloud
PFI:AIR - TT:云端设计优化
- 批准号:
1500205 - 财政年份:2015
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
Collaborative Research: Computational Foundations for Learning, Verifying, and Applying Model Simplification Rules
协作研究:学习、验证和应用模型简化规则的计算基础
- 批准号:
1161474 - 财政年份:2012
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
GOALI: A Novel Strategy for Large-Scale Industrial Topology Optimization
GOALI:大规模工业拓扑优化的新策略
- 批准号:
1232508 - 财政年份:2012
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
CAREER: Next-Generation Shape Optimization of Geometrically Complex Artifacts
职业:几何复杂工件的下一代形状优化
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0745398 - 财政年份:2008
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
Collaborative Research: Automatic Generation of Context-Dependent Simplified Models to Support Interactive Virtual Assembly
协作研究:自动生成上下文相关的简化模型以支持交互式虚拟装配
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0726635 - 财政年份:2007
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
Skeletal Reduction of Thin Mechanical Components
薄型机械部件的骨架减少
- 批准号:
0322134 - 财政年份:2003
- 资助金额:
$ 26.29万 - 项目类别:
Standard Grant
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