Machine Learning and Shape Optimisation of Fluid-Structure Interactions

流固耦合的机器学习和形状优化

• 批准号: DP210100990
• 负责人: Prof Kerry Hourigan
• 金额: $ 25.32万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-12-30 至 2024-12-29
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210100990
• 关键词: Machine; Learning; Shape; Optimisation; Fluid

This project aims to address vibrations of solid structures by utilising a combination of advanced experimental and computational methods. This project expects to generate new knowledge in the area of flow-induced vibrations utilising the new techniques of machine learning and evolutionary shape optimisation. Expected outcomes of this project include greatly accelerated discovery of mechanisms leading to structural vibrations and optimising structure geometries to either enhance or suppress the vibrations. This should provide significant benefits, such as the design strategies for improved energy harvesters, such as current oscillators, or more stable structures, such as platforms for offshore wind turbines.

该项目旨在通过利用先进的实验和计算方法的结合来解决固体结构的振动问题。该项目预计将利用机器学习和进化形状优化的新技术在流致振动领域产生新的知识。该项目的预期成果包括大大加快发现导致结构振动的机制，并优化结构几何形状以增强或抑制振动。这将带来显著的好处，例如改进能量采集器的设计策略，例如电流振荡器，或更稳定的结构，例如海上风力涡轮机平台。