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

高性能计算（HPC）的进步使我们能够在复杂耦合的多场、多域和多相系统中对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、声学），多域意味着这些场在分离的几何域上的解。在过去的六年里，在我的研究小组，高保真模拟使用第一原理物理定律（即，连续体方程）为航空航天、近海和海洋工程领域的新设计和新装置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，多领域（例如，流体-结构相互作用）模拟在某种程度上是低效的，因此对于设计优化、参数空间探索以及工程系统的控制和监测策略的开发而言吸引力较小。另一方面，目前用于流动动力学和流固耦合的参数研究和控制的最先进的方法主要是基于半经验方法，并且通常丢弃非线性效应，例如涡脱落、湍流尾流动力学和尾流干扰、由于流固耦合引起的大变形。拟议的研究计划将专注于解决我们内部基于HPC的高保真解决方案与新兴的数据科学和机器学习领域相结合的基础和应用挑战，同时促进UBC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，在我们最近对规范体的非定常流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，在非定常力预测的性能增益方面实现了4-5个数量级的改进。这种对学术问题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。我们将采用我们的新框架扑翼动力学提取海洋流体动力学（MHK）的能量在洋流建模。使用我们的高保真解算器，倒置的柔性箔沉浸在流体流动中，最近发现表现出大幅挥舞，这可以转换为电力使用压电器件。我们的目标是探索我们的多保真度框架的广泛的物理参数和配置的MHK设备。关于瞬态混沌和/或多尺度现象以及非线性动力学的离线-在线分解存在许多挑战。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.