Estimating Probability of Improvement in Next Cycle of Surrogate Based Design Optimization

估计基于替代的设计优化的下一个周期的改进概率

• 批准号: 0856431
• 负责人: Raphael Haftka
• 金额: $ 25万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0856431&HistoricalAwards=false
• 关键词: Estimating; Probability; Improvement; Next; Cycle

The research objective of this award is to provide accurate estimates of the probability of achieving a target level of improvement in the next surrogate based design optimization cycle, hence increasing the cost-effectiveness of the overall design process. A major challenge in the design optimization of complex engineering systems such as those found in the automotive (e.g., crashwordiness), aerospace (e.g., liquid rocket injector), oil (e.g., chemical flooding) and biomechanical (e.g., knee implants) industries is how to meet the conflicting objectives of improved performance, reduced costs and enhanced safety considering the time constraints present in industrial environments. Surrogate-based design optimization (SBDO), which proceeds in stages or cycles, has shown to be an effective approach to address this challenge. It refers to design optimization that relies on the use of surrogates or metamodels (computationally inexpensive) built/updated on reduced samples of computationally expensive/high fidelity simulations collected at each of the cycles. Each cycle is costly, and in spite of the progress, the current state-of-knowledge regarding SBDO lacks statistics-based stopping criteria for assessing the merit of investing in another cycle of optimization versus accepting the present best solution. If successful, the research will address the shortcomings of present industrial practice through the use of alternative surrogate model estimation and appraisal information to provide a rigorous stopping criterion for SBDO. The research will be validated addressing one of the most significant problems in biomechanical engineering: the design of artificial knees for minimizing wear and hence avoiding the need for a second replacement. Wear analysis of artificial knees is computationally very costly and the limit on number of simulations makes it an ideal application of SBDO. The results are expected to be disseminated through computational tools which will be freely available via the internet, and conference presentations, journal publications and engineering design optimization courses. The research will benefit from an ongoing collaboration with Brazilian, Venezuelan, and French researchers.

该奖项的研究目标是准确估计在下一个基于替代的设计优化周期中实现目标改进水平的概率，从而提高整个设计过程的成本效益。复杂工程系统（例如汽车（例如，碰撞）、航空航天（例如，液体火箭喷射器）、石油（例如，化学驱）和生物力学（例如，膝关节植入物）行业中的复杂工程系统的设计优化的一个主要挑战是，考虑到工业环境中存在的时间限制，如何满足提高性能、降低成本和增强安全性的相互冲突的目标。基于替代的设计优化（SBDO）分阶段或循环进行，已被证明是应对这一挑战的有效方法。它指的是依赖于使用在每个周期收集的计算昂贵/高保真模拟的减少样本上构建/更新的代理或元模型（计算便宜）的设计优化。 每个周期的成本都很高，并且尽管取得了进展，但当前有关 SBDO 的知识水平缺乏基于统计的停止标准来评估投资另一个优化周期与接受当前最佳解决方案的优点。如果成功，该研究将通过使用替代替代模型估计和评估信息来解决当前工业实践的缺点，为 SBDO 提供严格的停止标准。该研究将得到验证，解决生物力学工程中最重要的问题之一：设计人造膝盖以最大限度地减少磨损，从而避免第二次更换的需要。人工膝关节磨损分析的计算成本非常高，而且模拟次数的限制使其成为 SBDO 的理想应用。研究结果预计将通过可通过互联网免费获取的计算工具、会议演讲、期刊出版物和工程设计优化课程进行传播。该研究将受益于与巴西、委内瑞拉和法国研究人员的持续合作。