权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

使用改进的交替方向强制浸没边界法模拟笛卡尔网格上的运动物体

基本信息

批准号：
2091216
负责人：
金额：
--
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2091216
关键词：
Simulation Moving Objects Cartesian Mesh

项目摘要

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.

该博士项目的主要目的是开发一种改进的交替方向强制浸入边界方法 (AFD IBM)，用于固定笛卡尔网格上运动物体的湍流解析模拟。大多数浸入式边界方法在模拟移动边界时都会遭受寄生力振荡 (SFO) 的影响。 SFO 的主要来源之一已被确定为发生在浸没边界界面处的速度不连续性。该方法基于三次样条插值来获得固体域内的人工内部流动，同时保证固体物体壁上速度场的连续性。尽管所提出的方法是针对移动物体的，但它也为静态物体提供了显着的改进。 IBM ADF 将在高阶有限差分流动求解器 Incompact3D 中实现，Incompact3D 是使用世界上最强大的超级计算机来模拟湍流的强大工具。一旦 IBM ADF 得到实施和验证，它将用于通过深度强化学习来研究多鱼的节能游泳。通过将最先进的 3D 纳维-斯托克斯方程的湍流求解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。