Simulation of Moving Objects on an Cartesian Mesh Using an Improved Alternating Direction Forcing Immersed Boundary Method

本博士项目的主要目的是开发一种改进的交替方向强迫浸入边界法（AFD IBM），用于固定笛卡尔网格上运动物体的湍流解析模拟。大多数浸入边界法在模拟移动边界时都存在伪力振荡问题。在浸入边界界面处发生的速度不连续已被确定为sfo的主要来源之一。该方法基于三次样条插值，在保证实体壁面速度场连续性的前提下，在实体域内获得人工内部流场。尽管所提出的方法用于移动对象，但它也为静态对象提供了重大改进。ADF IBM将在高阶有限差分流动求解器Incompact3D中实现，这是一个强大的工具，可以使用世界上最强大的超级计算机来模拟湍流。一旦ADF IBM实现并验证，它将通过深度强化学习用于研究多鱼的节能游泳。通过将最先进的三维Navier-Stokes方程的湍流解决模拟与强化学习相结合，该项目将证明鱼类可以通过利用湍流来减少能量消耗。

The main aim of this PhD project is to develop an improved alternating direction forcing immersed boundary method (AFD IBM) for turbulence-resolving simulations of moving objects on a fixed Cartesian mesh. Most of the immersed boundary methods suffer from spurious force oscillations (SFOs) when moving boundaries are simulated. One of the main sources of SFOs has been identified as the velocity discontinuity that occurs at the immersed boundary interface. The proposed method is based on a cubic spline interpolation to obtain an artificial internal flow within the solid domain while ensuring the continuity of the velocity field at the wall of the solid object. Even though the proposed method is intended for moving objects, it also provides significant improvements for static objects. The ADF IBM will be implemented in the high-order, finite-difference flow solver Incompact3D, which is a powerful tool to simulate turbulent flows using the most powerful supercomputers in the world. Once the ADF IBM is implemented and validated, it will be used to study energy efficient swimming by multiple fish through deep reinforcement learning. By combining state-of-the-art turbulence-resolving simulations of the 3D Navier-Stokes equations with reinforcement learning, this project will demonstrate that fish can reduce their energy expenditure by harnessing turbulence.