Multi-fidelity aerodynamic modelling of competition cyclists.

竞赛自行车运动员的多保真度空气动力学建模。

• 批准号: 2856850
• 金额: --
• 依托单位: University of Manchester
• 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-2856850
• 关键词: Multi; fidelity; aerodynamic; modelling; competition

This project focuses on advancing the state of the art of computational aerodynamics in the challenging domain of competition cycling aerodynamics, in close collaboration with aerodynamicists in the British Cycling Team (BCT). We will be provided with 3D scans of the athlete in different positions, as well as wind tunnel data at a range of speeds (NDA signed). Such flows are characterised by complex 3D moving geometries and transitional Reynolds numbers, both of which present significant challenge to turbulence modelling. We will develop a multi-fidelity approach to provide accurate prediction at a reduced computational cost; consisting of the combination of steady state (RANS) modelling at moderate mesh resolution, turbulence scale resolution (LES) at high mesh resolution and experimental data. High-fidelity simulations will be combined with experimental data via field inversion, to fill gaps and ensure key features are correctly captured. Resulting datasets will be used to improve prediction of lower-order models in order to efficiently explore parameters such as rider position. Where modelling is insufficient or prohibitively expensive - such as with regions of transitional flow - we will develop a framework to incorporate flow vis images using data-assimilation to fix transition location. Aside from pushing the state of the art in multifidelity modelling, this project has potential for very high impact via close collaboration with the British Cycling Team in the run up to Olympic Games in Paris and LA. It would also provide exciting publicity for the University to help promote our engineering research and teaching in aerodynamics.

该项目专注于与英国自行车队（BCT）的空气动力学家密切合作，在具有挑战性的竞技自行车空气动力学领域推进计算空气动力学的最新技术。我们将获得运动员在不同位置的3D扫描，以及在不同速度下的风洞数据（NDA签署）。这种流动的特点是复杂的3D运动几何形状和过渡雷诺数，这两者都对湍流建模提出了重大挑战。我们将开发一种多保真度方法，以降低计算成本提供准确的预测；由中等网格分辨率的稳态（RANS）模型、高网格分辨率的湍流尺度分辨率（LES）模型和实验数据相结合组成。高保真模拟将通过场反演与实验数据相结合，以填补空白并确保正确捕获关键特征。由此产生的数据集将用于改进低阶模型的预测，以便有效地探索骑手位置等参数。在建模不足或过于昂贵的地方-例如过渡流的区域-我们将开发一个框架，使用数据同化来固定过渡位置，将流可视化图像合并在一起。除了推动多保真度建模技术的发展之外，该项目还将通过与英国自行车队在巴黎和洛杉矶奥运会前的密切合作，产生非常大的影响。此外，这亦有助宣传理大的空气动力学工程研究及教学。