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

该奖项的研究目标是提供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的准确估计，从而提高整个设计过程的成本效益。复杂工程系统的设计优化中的一个主要挑战，例如汽车中的那些系统（例如，crashwordiness），航空航天（例如，液体火箭喷射器），油（例如，化学驱油）和生物力学（例如，膝关节植入物）工业的问题是如何在考虑到工业环境中存在的时间限制的情况下满足改进性能、降低成本和增强安全性的冲突目标。基于代理的设计优化（SBDO），在阶段或周期进行，已被证明是一种有效的方法来解决这一挑战。它是指设计优化，依赖于使用替代或元模型（计算成本低），在每个周期收集的计算成本高/高保真度模拟的减少样本上构建/更新。 每一个周期都是昂贵的，尽管取得了进展，但目前关于SBDO的知识状况缺乏基于知识的停止标准，以评估投资于另一个优化周期与接受目前的最佳解决方案的优点。如果成功的话，研究将解决目前的工业实践的缺点，通过使用替代替代模型估计和评估信息，提供严格的停止标准SBDO。这项研究将得到验证，解决生物力学工程中最重要的问题之一：人工膝关节的设计，以最大限度地减少磨损，从而避免第二次更换的需要。人工膝关节的磨损分析是非常昂贵的计算和数量的限制，使其成为一个理想的应用SBDO。预计结果将通过计算工具传播，这些工具将通过互联网免费提供，以及会议演示，期刊出版物和工程设计优化课程。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。

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.