Deep learning for reduced order modelling of wall bounded, turbulent flows

用于壁面湍流降阶建模的深度学习

• 批准号: 2753788
• 金额: --
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2753788
• 关键词: Deep; learning; reduced; order; modelling

Simulation (and detailed experiments) of turbulent fluid may be definedas a "data-rich, knowledge-poor" activity: the complexity of underlyingphysics is so high that it is almost impossible to learn and extract newknowledge from a large amount of the observed data directly.This thesis will focus on data accumulated with high fidelity simulationsto build effective reduced order models set up using a feed forward neural network.Once a reduced order model is available, one can have rapid estimates of flow field changes as the parameters are changed.This can be used as an environment for reinforcement learning algorithms.

仿真（和详细的实验）湍流流体可以被定义为“数据丰富，知识贫乏”的活动：由于物理现象的复杂性，直接从大量的观测数据中学习和提取新知识几乎是不可能的.本文将利用高保真模拟积累的数据，利用前馈神经网络建立有效的降阶模型.降阶后，模型可用，可以随着参数的变化快速估计流场的变化。这可以用作强化学习算法的环境。