Multi-fidelity aerodynamic modelling of competition cyclists.
Multi-fidelity aerodynamic modelling of competition cyclists.
批准号:
2856850
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --
中文摘要
该项目的重点是推进计算空气动力学在竞争自行车空气动力学的挑战性领域的艺术状态,与英国自行车队(BCT)的空气动力学家密切合作。我们将提供运动员在不同位置的3D扫描,以及在一定速度范围内的风洞数据(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的方法,以降低计算成本提供准确的预测;由稳态(RANS)建模在中等网格分辨率,湍流尺度分辨率(LES)在高网格分辨率和实验数据的组合。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。结果数据集将用于改善低阶模型的预测,以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-例如过渡流区域-我们将开发一个框架,使用数据同化将流维斯图像结合起来,以确定过渡位置。除了推动多保真度建模的最新技术外,该项目还通过与英国自行车队的密切合作,在巴黎和洛杉矶奥运会期间产生很大的影响。这亦会为大学提供令人振奋的宣传,有助促进我们在空气动力学方面的工程研究和教学。
英文摘要
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.
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