| Departments |
Zienkiewicz Institute for Modelling, Data and AI
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| Description | This project will involve developing a suite of in-house software capabilities that allows the optimisation of aerofoils (2D wing sections) using computational fluid dynamics (CFD) to determine aerodynamic performance where there are multiple, competing optimisation objectives. Deep neural network learning will be employed to help accelerate the process of determining the aerofoil aerodynamic performance compared to the otherwise computationally expensive purely CFD-driven approach. |
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| Preparation | To prepare for this project, students should familiarise themselves with basic principles of RANS-based computational fluid dynamics (including mesh generation, Navier Stokes equations, equation discretisation techniques) and also eveolutionary optimisation methods. |
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Level of Studies
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| Level 6 (Undergraduate Year 3) |
yes
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| Level 7 (Masters) |
yes
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| Level 8 (PhD) |
yes
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