Collaborative Research: Enabling Large-scale Multidisciplinary Design Optimization with Unsteady Simulations: A Hybrid Pseudo-spectral Approach

协作研究：通过非定常模拟实现大规模多学科设计优化：混合伪谱方法

• 批准号: 2223670
• 负责人: Joaquim Martins
• 金额: $ 33.28万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223670&HistoricalAwards=false
• 关键词: Collaborative; Research; Enabling; Large; scale

This project will develop a breakthrough multidisciplinary design optimization (MDO) framework that uses unsteady multiphysics computer simulations to optimize system performance automatically. The research is motivated by the lack of effective numerical algorithms to shorten the design period for large-scale engineered systems with unsteady processes, such as spacecraft, aircraft, and wind turbines. This issue is further exacerbated by ever-increasing expectations for system performance and safety. The automated MDO framework will significantly reduce the design cycle time for transformative systems that are poised to improve the nation’s economic prosperity and change how people live and connect, such as urban air taxis and systems supporting space travel. Furthermore, this project will advance the knowledge of complex mechanisms and interactions in large-scale engineered systems, which would otherwise be hard to obtain solely by human intuition. This project will also conduct educational and outreach activities for underrepresented minority and K-12 students to encourage STEM engagement, promote diversity and inclusion, and stimulate students' interest in engineering design and optimization.The research objective of this project is to enable the gradient-based multidisciplinary design optimization (MDO) of large-scale engineered systems governed by unsteady processes. The project will develop a new hybrid pseudo-spectral (HPS) adjoint algorithm to compute unsteady gradients for a broad range of disciplines efficiently. The originality of the HPS algorithm is that it effectively combines the robustness of time-accurate analysis and the speed of pseudo-spectral adjoint to enable efficient computation of high-dimensional unsteady gradients. The project will investigate the fundamental characteristics of the HPS algorithm and develop a modular architecture to couple any number of disciplines for large-scale unsteady MDO. It will demonstrate the framework by conducting urban air mobility electric aircraft and offshore wind turbine MDO that considers the unsteady coupling between fluid mechanics, structures, heat transfer, and dynamics. With further development, the framework can be extended to more disciplines, such as control and multiphase flow. The unsteady MDO framework will be open to the public to promote collaborations in the engineering design community. The HPS algorithm is general and expected to benefit many other fundamental research areas beyond MDO, including surrogate modeling, error and uncertainty analyses, and machine learning. Moreover, this project is anticipated to create a catalytic effect in the engineering design industry to transform the traditional, human-supervised design process into a more automated one.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.

该项目将开发一个突破性的多学科设计优化（MDO）框架，该框架使用不稳定的多物理计算机模拟自动优化系统性能。这项研究是由于缺乏有效的数值算法无法缩短具有不稳定过程的大型工程系统的设计期，例如航天器，飞机和风力涡轮机。人们对系统性能和安全性的期望不断增加，进一步加剧了这个问题。自动化的MDO框架将大大减少被毒害以改善国家经济繁荣并改变人们生活和联系的变革系统的设计周期时间，例如城市航空出租车和支持太空旅行的系统。此外，该项目将促进大规模工程系统中复杂机制和相互作用的知识，否则这仅仅是通过人类直觉而获得的。该项目还将为代表性不足的少数群体和K-12学生开展教育和外展活动，以鼓励STEM参与，促进多样性和包容性，并激发学生对工程设计和优化的兴趣。该项目的研究目标是使基于梯度的多学科设计（MDO）能够对不稳定流程管理的大型工程系统进行基于梯度的多学科设计（MDO）。该项目将开发一种新的混合伪谱（HPS）伴随算法，以有效地计算不稳定的梯度，以有效地计算各种学科。 HPS算法的独创性在于，它有效地结合了时间准确分析的鲁棒性和伪谱伴随的速度，以有效地计算高维无稳定梯度。该项目将调查HPS算法的基本特征，并开发模块化体系结构，以使大规模Unsteady MDO的任何数量的学科结合在一起。它将通过进行城市空气移动飞机和海上风力涡轮机MDO来展示框架，这些飞机和海上风力涡轮机MDO考虑了流体机构，结构，传热和动态之间的不稳定耦合。随着进一步的发展，该框架可以扩展到更多学科，例如控制和多相流。不稳定的MDO框架将向公众开放，以促进工程设计社区的合作。 HPS算法是一般的，预计将使MDO以外的许多其他基本研究领域受益，包括替代建模，错误和不确定性分析以及机器学习。此外，预计该项目将在工程设计行业中产生催化效应，以将传统的，人类监督的设计过程转变为更加自动化的效果。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准通过评估来评估的。