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: 2223676
• 负责人: Ping He
• 金额: $ 24.22万
• 依托单位: Iowa State University
• 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=2223676&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.

该项目将开发一种突破性的多学科设计优化(MDO)框架，该框架使用非稳态多物理计算机模拟来自动优化系统性能。这项研究的动机是缺乏有效的数值算法来缩短具有非稳定过程的大型工程系统的设计周期，如航天器、飞机和风力涡轮机。对系统性能和安全性的期望不断提高，进一步加剧了这一问题。自动化的MDO框架将显著缩短变革性系统的设计周期，这些系统有望改善国家的经济繁荣，改变人们的生活和联系方式，例如城市空中出租车和支持太空旅行的系统。此外，该项目将推进大规模工程系统中复杂机制和相互作用的知识，否则仅凭人类直觉很难获得这些知识。该项目还将为代表不足的少数族裔和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优化(MDO)能够管理非稳定过程的大型工程系统。该项目将开发一种新的混合伪谱(HPS)伴随算法，以有效地计算广泛学科范围内的非定常梯度。HPS算法的创新之处在于它有效地结合了时间精确分析的稳健性和伪谱伴随的速度，从而能够有效地计算高维非定常梯度。该项目将研究HPS算法的基本特征，并开发一种模块化体系结构，以耦合任何数量的学科来进行大规模非稳定MDO。它将通过指导城市空气流动性电动飞机和海上风力涡轮机MDO来演示该框架，该MDO考虑了流体力学、结构、热传递和动力学之间的非定常耦合。随着进一步的发展，该框架可以扩展到更多的学科，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算法是通用的，预计将有助于MDO以外的许多其他基础研究领域，包括代理建模、误差和不确定性分析以及机器学习。此外，该项目预计将在工程设计行业产生催化作用，将传统的、人工监督的设计过程转变为更自动化的过程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Duality-Preserving Adjoint Method for Segregated Navier–Stokes Solvers

分离纳维斯托克斯求解器的对偶保持伴随法

• DOI: 10.1016/j.jcp.2024.112860
• 发表时间: 2024
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Fang, Lean;He, Ping
• 通讯作者: He, Ping

Accelerating unsteady aerodynamic simulations using predictive reduced-order modeling

• DOI: 10.1016/j.ast.2023.108412
• 发表时间: 2023-05
• 期刊: Aerospace Science and Technology
• 影响因子: 5.6
• 作者: Zilong Li;Pingjing He

Low-Thrust Spacecraft Trajectory Optimization with Gravity-Assist Maneuver using Dymos

使用 Dymos 进行重力辅助机动的低推力航天器轨迹优化

• DOI: 10.2514/6.2024-0633
• 发表时间: 2024
• 期刊: AIAA SciTech Forum
• 作者: Harris, Gage W.;He, Ping
• 通讯作者: He, Ping

A Segregated Time-Accurate Adjoint Method for Field Inversion of Unsteady Flow

• DOI: 10.2514/6.2024-0158
• 发表时间: 2024-01
• 期刊: AIAA SCITECH 2024 Forum
• 作者: Lean Fang;Pingjing He

High-fidelity aerodynamic and aerostructural optimization of UAV propellers using the adjoint method

• DOI: 10.2514/6.2023-0531
• 发表时间: 2023-01
• 期刊: AIAA SCITECH 2023 Forum
• 作者: Pingjing He;Heyecan Koyuncuoglu;H. Hu;Anvesh Dhulipalla;Haiyang Hu;Hui Hu