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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：
Krishnan Suresh
金额：
$ 26.29万
依托单位：
University of Wisconsin-Madison
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：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824980&HistoricalAwards=false
关键词：
Predicting Benefits Topology Optimization

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：
Structural and Multidisciplinary Optimization
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：
J. Comput. Inf. Sci. Eng.
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：
Structural and Multidisciplinary Optimization
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：
Computer Methods in Applied Mechanics and Engineering
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：
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
影响因子：
6.4
作者：
Rankouhi, B.;Bertsch, K. M.;Suresh, K.
通讯作者：
Suresh, K.