Publication

Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

2019
Student Projects

Abstract

Aerodynamic shape optimization has become of primary importance for the aerospace industry over the last years. Most of the method developed so far have been shown to be either computationally very expensive, or to have low dimensional search space. In this work we present how Geodesic Convolutional Neural Networks can be used as a surrogate model for a computational fluid dynamics solver. The trained model is shown to be capable of efficiently exploring a high dimensional shape space. We apply this method to the optimization of a fixed wing drone, the eBee Classic.

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2019
Student Projects

Abstract

Official source

Related concepts (32)

Surrogate model

A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the airflow around the wing for different shape variables (length, curvature, material, .

Scientific modelling

Scientific modelling is a scientific activity, the aim of which is to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate by referencing it to existing and usually commonly accepted knowledge. It requires selecting and identifying relevant aspects of a situation in the real world and then developing a model to replicate a system with those features.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

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Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

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