Davide Esposto
A deep learning approach to predicting unsteady airfoil performance from tiny datasets.
Rel. Stefano Berrone, Gaetano Iuso. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Aerospaziale, 2024
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Abstract
The interaction between fluid and wing surface for the generation of aerodynamic forces is a complex nonlinear phenomenon with multiple variables. Obtaining the aerodynamic performance of a body is, therefore, a complex, time-consuming and costly endeavour. Wind tunnel testing, especially for high-speed flows, requires special facilities and custom-built, high-precision body models. On the other hand, high-precision computational fluid dynamics is expensive, and the possibility of real-life mirroring Direct Numerical Simulation for flows of engineering interest remains impractical for the foreseeable future. Especially during the preliminary design phase, exploring the effect of freestream conditions on the aerodynamic performance of a profile requires multiple trials with high time and labour input.
In recent years, deep learning has undergone rapid advances in various applications, from computer vision to natural language processing to time series prediction
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