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.

可以将湍流的模拟（和详细的实验）定义为“富含数据，知识贫困”的活动：基础物理学的复杂性是如此之高，以至于几乎不可能直接从大量观察到的数据中学习和提取新知识，直接从大量论文中，本论文将重点放在数据累积的数据上，使用高富达级别的模型，使用有效的订单模型。随着参数的更改，流场变化的估计值可以用作增强学习算法的环境。