"Prediction and control of extreme fluid dynamics with artificial intelligence"

“利用人工智能预测和控制极端流体动力学”

• 批准号: 2275537
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2275537
• 关键词: Prediction; control; extreme; fluid; dynamics

This cross-disciplinary project falls in the following five EPSRC research areas: Artificial intelligence technologies, Combustion engineering, Fluid Dynamics and Aerodynamics, Complexity Science, Numerical Analysis.High-fidelity simulations of turbulent flows have increasingly been adopted by academia to understand the physics of turbulence (direct numerical simulation, DNS), and by industry (large-eddy simulation, LES) as design tools to minimize costly testing. Whereas the calculation of the statistics of turbulent flows can be accurate, the time and space accurate prediction of extreme events, such as violent turbulent bursts and auto-ignited spots of mixture, has not been achieved yet. No matter how accurate the simulation code is, the time and space prediction of extreme events is limited by the chaotic nature of reacting and non-reacting turbulence. This project will enable the prediction and physical understanding of extreme events by leveraging artificial intelligence and machine learning algorithms with dynamical systems' theory. An optimisation algorithm will be designed and trained to control extreme fluid dynamics, in particular in multi-physical problems that are relevant to energy harvesting and aeronautical propulsion.

这个跨学科的项目福尔斯属于以下五个EPSRC研究领域：人工智能技术、燃烧工程、流体动力学和空气动力学、复杂性科学、数值分析。学术界越来越多地采用湍流的高保真模拟来理解湍流的物理特性（直接数值模拟，DNS）和工业（大涡模拟，LES）作为设计工具，以最大限度地减少昂贵的测试。虽然湍流统计量的计算是准确的，但对极端事件，如剧烈的湍流爆发和混合物自燃点的时间和空间精确预测尚未实现。无论模拟代码多么精确，极端事件的时间和空间预测都受到反应和非反应湍流的混沌性质的限制。该项目将通过利用人工智能和机器学习算法与动力系统理论，实现对极端事件的预测和物理理解。将设计和训练优化算法，以控制极端流体动力学，特别是在与能量收集和航空推进相关的多物理问题中。