An adaptive surface for improved modelling of rough wall bounded turbulence

用于改进粗糙壁边界湍流建模的自适应表面

• 批准号: DP240101743
• 负责人: Prof Nicholas Hutchins
• 金额: $ 32.22万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240101743
• 关键词: adaptive; surface; improved; modelling; rough

This project aims to improve the prediction of drag where fluid flows over rough surfaces. This is a significant problem, with the uncertainty in drag penalty prediction for shipping alone exceeding ten billion dollars annually. The societal importance of these flows demands action, yet novel approaches must be sought to efficiently explore the wide range of roughness types encountered in practice. An adaptive surface is proposed, where a roughness configuration can be dialled in at the press of a button, to rapidly converge on improved models. A key outcome of this project will be improved predictive models of drag for rough wall flows. Benefits will include improved efficiencies and reduced emissions across a wide range of industries.

该项目旨在改进流体在粗糙表面上流动时的阻力预测。这是一个严重的问题，单是船舶阻力罚款预测的不确定性就超过了每年100亿美元。这些流动的社会重要性要求采取行动，但必须寻求新的方法，有效地探索实践中遇到的各种粗糙类型。提出了一种自适应曲面，只需按一下按钮就可以拨入粗糙度配置，从而快速收敛到改进的模型上。该项目的一个关键成果将是改进的粗糙壁流阻力预测模型。好处将包括在广泛的行业中提高效率和减少排放。