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

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

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.