Advances in Explicit Design Space Decomposition for Computational Design

计算设计显式设计空间分解的进展

• 批准号: 1029257
• 负责人: Samy Missoum
• 金额: $ 26.16万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029257&HistoricalAwards=false
• 关键词: Advances; Explicit; Design; Space; Decomposition

The research objective of this award is to develop a new paradigm for the computational design of complex systems. This research will aid engineers to find optimal design solutions that will minimize performance objectives while satisfying requirements under the presence of uncertainties. This is made possible by defining explicitly the boundaries of the regions of the design space corresponding to specific system behaviors. This Explicit Design Space Decomposition (EDSD), which is achieved using support vector machines, has been shown to be promising and particularly useful for problems that are discontinuous or binary and involve costly computer simulations. The research extends the EDSD approach following three main avenues. First, a multifidelity scheme will be developed that will enable the designers to exploit the wealth of information coming from various sources (analytical models, engineer experience, simulations, and physical experiments). Through adaptive sampling, the multifidelity approach can lead to a drastic reduction in the number of calls to expensive computer simulations. Second, the new approach will quantify the inaccuracy of the explicit boundaries and incorporate this information in the assessment of probabilities of failure. Finally, this work proposes to unify the EDSD approach with existing approaches based on response approximations such as Kriging.If successful, these methods will lead to a more flexible computational design framework, particularly for complex systems. In fact, EDSD and the proposed advances are able to handle problems with non-smooth behaviors, reduce computational time, propagate uncertainties, and combine vastly different sources of information. The associated benefits such as cost minimization, design cycle time reduction, and improved reliability, will provide a competitive advantage to companies. The techniques are applicable to many engineering disciplines and are particularly useful in the biomedical field where both clinical data and computational models are used (e.g., for hip fracture prediction). The methods will be disseminated to the engineering and research community through their implementation in the free DAKOTA software package from Sandia National Laboratory.

该奖项的研究目标是为复杂系统的计算设计开发一种新的范式。这项研究将有助于工程师找到最佳的设计方案，将最大限度地减少性能目标，同时满足要求下的不确定性。通过明确定义与特定系统行为相对应的设计空间区域的边界，可以实现这一点。这种显式设计空间分解（EDSD），这是使用支持向量机实现的，已被证明是有前途的，特别是对于不连续或二进制的问题，并涉及昂贵的计算机模拟有用。该研究扩展了EDSD方法以下三个主要途径。首先，将开发一个多保真度方案，使设计人员能够利用来自各种来源（分析模型，工程师经验，模拟和物理实验）的丰富信息。通过自适应采样，多保真度方法可以大大减少昂贵的计算机模拟的调用次数。其次，新方法将量化显式边界的不准确性，并将此信息纳入故障概率的评估。最后，这项工作提出了统一的EDSD方法与现有的方法的基础上响应近似，如Kriging.If成功，这些方法将导致一个更灵活的计算设计框架，特别是对复杂的系统。事实上，EDSD和所提出的改进能够处理具有非光滑行为的问题，减少计算时间，传播不确定性，并且联合收割机组合了非常不同的信息源。相关的好处，如成本最小化，设计周期缩短，提高可靠性，将提供一个竞争优势的公司。所述技术适用于许多工程学科，并且在使用临床数据和计算模型两者的生物医学领域中特别有用（例如，用于髋部骨折预测）。 这些方法将通过在桑迪亚国家实验室的免费DAKOTA软件包中实施而传播给工程和研究界。