Explicit Design Space Decomposition with Adaptive Sampling for Design Optiimization and Uncertainty Quantification

通过自适应采样进行显式设计空间分解，以实现设计优化和不确定性量化

• 批准号: 0800117
• 负责人: Samy Missoum
• 金额: $ 18.16万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0800117&HistoricalAwards=false
• 关键词: Explicit; Design; Space; Decomposition; Adaptive

0800117PI: MissoumThe research objective of this award is to develop a method to aid designers and engineers in optimizing complex products under uncertainty. The design of complex systems such as a vehicle or an engine often requires very long computational times due to the presence of nonlinear behaviors. Because optimization requires repeated calls to a simulation code (e.g., crash analysis) to investigate a multi-dimensional design space, it is critical to use a sound methodology to select relevant design configurations in the search for the best solutions. The proposed approach, referred to as explicit design space decomposition, partitions the design space into regions whose boundaries, which are defined explicitly in terms of the variables, separate "acceptable" and "unacceptable" designs. These boundaries are constructed using a Support Vector Machine (SVM) which is able to define multi-dimensional, disjoint, and non-convex regions. This gives insight to the designer through a direct correspondence between regions of the design space and specific behaviors of a system. In addition, accurate SVM-based boundaries can be obtained by adaptively adding relevant samples corresponding to specific designs. This approach has the potential to limit trial-and-error steps and reduce the total computational time. Finally, explicit boundaries are useful for problems with discontinuous behaviors such as buckling and provide a straightforward calculation of probabilities of failure. All in all, this approach allows the designer to reach better and more reliable designs in fewer design iterations.If successful, the results of this research will provide a general framework for design optimization and design under uncertainty of nonlinear problems characterized by, but not limited to, discontinuous responses and long simulation times (e.g., design for crashworthiness). The generality of the proposed approach will make it applicable to traditional industries (e.g., automobile and aerospace) as well as fields such as the design of biomedical devices. The reduction of trial-and-error steps will, if successful, drastically reduce design cycle times and cost. The results of this research will be included in graduate coursework and disseminated through technical publications and presentations. In addition, this approach is expected to have a significant impact on industry, resulting in streamlined design processes and better products.

0800117 PI：该奖项的研究目标是开发一种方法，以帮助设计师和工程师在不确定性条件下优化复杂产品。由于存在非线性行为，车辆或发动机等复杂系统的设计通常需要非常长的计算时间。由于优化需要重复调用模拟代码（例如，碰撞分析）为了研究多维设计空间，在寻找最佳解决方案的过程中，使用合理的方法来选择相关的设计配置是至关重要的。所提出的方法，被称为显式设计空间分解，分区的设计空间到区域的边界，这是明确定义的变量，分离的“可接受的”和“不可接受的”设计。这些边界是使用支持向量机（SVM），这是能够定义多维，不相交，非凸区域。这通过设计空间的区域与系统的特定行为之间的直接对应关系为设计人员提供了洞察力。此外，精确的基于SVM的边界可以通过自适应地添加对应于特定设计的相关样本来获得。这种方法有可能限制试错步骤，减少总的计算时间。最后，显式边界对于具有不连续行为（如屈曲）的问题很有用，并提供了一个简单的失效概率计算。总之，这种方法可以使设计人员在更少的设计迭代中达到更好和更可靠的设计。如果成功，本研究的结果将为设计优化和非线性问题的不确定性下的设计提供一个通用框架，这些问题的特征是但不限于不连续响应和长仿真时间（例如，耐撞性设计）。所提出的方法的一般性将使其适用于传统行业（例如，汽车和航空航天）以及诸如生物医学设备的设计等领域。减少试错步骤，如果成功的话，将大大减少设计周期和成本。这项研究的结果将列入研究生课程，并通过技术出版物和演示文稿传播。此外，这种方法预计将对工业产生重大影响，从而简化设计流程和改进产品。