Drag Prediction over Rough Surfaces using Hardware-Accelerated Simulations

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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.