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仿真来优化设计,即提高设计的性能。由于这可能既耗时又昂贵,工程师面临着以下一系列棘手问题:是否值得优化给定的设计?通过计算机模拟可以将性能提高多少?无需投入几天的计算就能预测设计优化的好处吗?该项目将通过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,使工程师能够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。通过允许设计者将有限的资源集中在最有希望的优化问题上,这有可能显着提高工业竞争力。研究小组将在期刊和会议记录中发表研究结果;该团队还将向科学界发布最终的软件。该团队将为高中生(包括少数族裔学生)举办有关工程模拟和优化的工程研讨会。当地行业和学生汽车项目的案例研究将用作设计挑战。工业互动对崭露头角的工程师产生了更广泛的影响,他们将接触到研究、工业经验和正规教育的独特融合。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效益的指标。对于给定的设计,帕累托最优距离是一组到帕累托最优流形的轴对齐距离;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好处,即距离越大,好处越大。利用这个简单的概念,设计人员可以对设计优化做出合理的决策。该项目将解决以下问题:(1)帕累托最优概念是否可以应用于迄今为止考虑的结构力学问题之外的问题? (2) 这个概念可以用于对装配中的零件进行排序以优化设计吗? (3) 如何将这些概念推广到包括约束、多负载场景和材料设计? (4) 能否开发二阶和纠错策略来提高这些预测方法的有效性?该奖项反映了 NSF 的法定使命,并通过使用基金会的智力价值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
- 批准号:
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
- 批准号:
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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