Drag Prediction over Rough Surfaces using Hardware-Accelerated Simulations

该项目旨在通过利用高性能和高效的硬件加速来揭示粗糙地形和阻力之间的关系。该项目希望通过使用最先进的硬件加速高保真模拟和机器学习技术来识别重要的粗糙度参数，从而在粗糙壁湍流边界层领域产生新的知识。该项目的预期成果包括开发一种新的，更准确，更强大的模型来预测阻力。这将导致更好的数据驱动政策，以实现更可持续和盈利的航空和海运业。

This project aims to uncover the relationship between roughness topography and drag by utilising high-performance and efficient hardware acceleration. This project expects to generate new knowledge in the area of rough-wall turbulent boundary layer by using state-of-the-art hardware accelerated high fidelity simulations and machine learning techniques to identify important roughness parameters. Expected outcomes of this project include the development of a novel, more accurate, and robust model to predict drag. This would lead to improved data-driven policies for more sustainable and profitable airline and maritime industries.