Using Machine Learngin for Early-Stage Aircraft Wing Design

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Early-stage aircraft structural design must explore a large design space to ensure optimal aerodynamic and load bearing performance. This is especially important as we enter an age of novel aerostructures with enhanced capabilities such as morphing wings. At this stage (as opposed to the subsequent detailed design stage), designers must also cope with uncertainties that exist due to lack of design maturity, knowledge about aerodynamic loads and model predictions. The PhD project aims to use machine learning to develop a data-driven robust aircraft wing design toolbox in collaboration with Airbus. Machine learning is seeing a rapid uptake in manufacture and design due to its ability to predict optimal design/operational conditions given countless possibilities and covariates. Machine learning will be a key enabler for the project to properly trade optimum performance against the risks of achieving the performance and meeting the constraints. A Bayesian machine learning approach will be used within a data-driven framework for robust design and optimization of aircraft structures under a set of stringent design constraints imposed by considerations of weight penalty, flexibility in flight conditions, flight envelopes and risk minimization