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Predicting and simulating aerodynamic fields for civil aircraft over wideflight envelopes represent a real challenge mainly due to significant numericalcosts and complex flows. Surrogate models and reduced-order models help toestimate aerodynamic fields from a few well-selected simulations. However,their accuracy dramatically decreases when different physical regimes areinvolved. Therefore, a method of local non-intrusive reduced-order models usingmachine learning, called Local Decomposition Method, has been developed tomitigate this issue. This paper introduces several enhancements to this methodand presents a complex application to an industrial-like three-dimensionalaircraft configuration over a full flight envelope. The enhancements of themethod cover several aspects: choosing the best number of models, estimatingapriori errors, improving the adaptive sampling for parallel issues, and betterhandling the borders between local models. The application is supported by ananalysis of the model behavior, with a focus on the machine learning methodsand the local properties. The model achieves strong levels of accuracy, inparticular with two sub-models: one for the subsonic regime and one for thetransonic regime. These results highlight that local models and machinelearning represent very promising solutions to deal with surrogate models foraerodynamics.

Improved Surrogate Modeling using Machine Learning for Industrial Civil Aircraft Aerodynamics