Data-Driven Multi-Fidelity Modelling of Large Aspect Ratio Wings with Distributed Propellers

Strut-braced large aspect ratio wings (LARW) with distributed hybrid-electric propulsion (DHEP) show promise in reducing noise and emissions from aircraft. The adoption of these technologies will further aviation sustainability and will improve the quality of life of communities near to airports. Existing low-fidelity models used for optimizing these configurations often neglect crucial flow physics typical of such complex and unconventional configurations, limiting their accuracy. This work explores two methods to enhance prediction capabilities suitable for early design phases: correcting the prediction of an existing low-fidelity model using an error-based approach and developing a stand-alone reduced order model based on high-fidelity Reynolds-Averaged Navier-Stokes (RANS) simulations. An error-based model aims to correct outputs from the low-fidelity surrogate, while a pure high-fidelity surrogate is constructed using the same samples. This work aims at understanding if the behaviour of the error between low- and high-fidelity predictions will have less nonlinearity than the flow itself, allowing for a more efficient and more effective building of the reduced order model. Both methods are evaluated by predicting the performance of an untrained configuration within the training space to determine which approach more effectively predicts lift and drag coefficients.