A Novel Framework for Model Reduction and Data-Driven Modeling of Fluid-Structure System: Application to Flapping Dynamics

高性能计算(HPC)的进步使我们能够对复杂耦合的多场、多域和多相系统中的数十亿个变量进行大规模模拟。虽然多场表示几个相互作用的物理场(例如，流体、固体、声场)，但多域意味着这些场在不同的几何域上的解。在我的研究小组的过去六年里，使用第一原理物理定律(即连续介质方程)的高保真模拟为航空航天、海上和海洋工程中新设计和设备的开发提供了宝贵的见解。尽管有高效的算法和强大的超级计算机，但多场(如流固耦合)模拟的效率较低，因此在工程系统的设计优化、参数空间探索以及控制和监测策略的开发方面缺乏吸引力。另一方面，目前流动动力学和流固耦合的参数研究和控制方法主要是基于半经验方法，而涡流脱落、湍流尾迹动力学和尾迹干扰等非线性效应通常被摒弃，流固耦合引起的大变形通常被摒弃。拟议的研究计划将重点解决我们内部基于HPC的高保真解算器与新兴数据科学和机器学习领域整合过程中的基本和应用挑战，同时促进不列颠哥伦比亚省的跨学科研究和教育。我相信，基于物理模型和数据驱动计算的新框架将彻底改变下一代系统的工程预测和设计。例如，在我们最近对正则物体非定常流动动力学预测的研究中，我们通过使用深度学习的数据驱动建模，在预测非恒定力的性能增益方面取得了超过4-5个数量级的改进。这些在理论问题上的改进对于大规模动力系统优化的迫切需要是非常有希望的。我们将使用我们的新框架来模拟扑翼动力学，以提取洋流中的海洋流体动力(MHK)能量。使用我们的高保真解算器，最近发现浸没在流体中的倒置柔性薄片表现出大幅度的拍打，这种拍打可以通过压电装置转换为电能。我们的目标是探索我们的多保真框架，用于MHK设备的各种物理参数和配置。在非线性动力学的暂态、混沌和/或多尺度现象以及离线-在线分解方面存在许多挑战。最后，该研究计划将提供有效的工具、物理洞察力和实践指导，并将为研究生和博士后研究员培养一个培训环境。

Advances in high-performance computing (HPC) have empowered us to perform large-scale simulations for billions of variables in complex coupled multifield, multidomain and multiphase systems. While the multifield represents several interacting physical fields (e.g., fluid, solid, acoustic), the multidomain implies the solution of these fields over separate geometric domains. Over the past six years in my research group, the high-fidelity simulations using the first-principle physical laws (i.e., continuum equations) have been providing invaluable insight for the development of new design and devices in aerospace, offshore and marine engineering. Despite efficient algorithms and powerful supercomputers, the multifield (e.g., fluid-structure interaction) simulations are somewhat inefficient hence less attractive with regard to design optimization, parameter space exploration and the development of control and monitoring strategies for engineering systems. On the other hand, current state-of-the-art methods for parametric investigation and control of flow dynamics and fluid-structure interactions are primarily based on semi-empirical methods and nonlinear effects such as vortex shedding, turbulent wake dynamics and wake interference, large deformation due to fluid-structure coupling are typically discarded. The proposed research program will focus on addressing fundamental and applied challenges during the integration of our in-house HPC-based high-fidelity solver with the emerging field of data science and machine learning while promoting interdisciplinary research and education in UBC. I believe that the new framework based on the physical-model and data-driven computing will revolutionize engineering predictions and design of next-generation systems. For example, in our recent studies for the unsteady flow dynamical predictions of canonical bodies, we have achieved over 4-5 orders of magnitudes improvements in the performance gain for the prediction of unsteady forces via data-driven modeling using deep learning. Such improvements on academic problems are very promising for the pressing needs for optimization of large-scale dynamical systems. We will employ our novel framework for modeling of flapping foil dynamics for the extraction of marine hydrokinetic (MHK) energy in ocean currents. Using our high-fidelity solver, inverted flexible foils immersed in fluid flow are recently found to exhibit large-amplitude flapping, which can be converted to electricity using piezoelectric devices. We aim to explore our multifidelity framework for a broad range of physical parameters and configurations of MHK devices. There are numerous challenges with regard to transient chaotic and/or multi-scale phenomenon and offline-online decompositions of the nonlinear dynamics. Finally, the research program will provide efficient tools, physical insight, and practical guidance and will foster a training environment for graduate students and postdoctoral fellows.