Noise shielding surrogate models using dynamic artificial neural networks

The optimal design methodologies in aeronautics are known to be constrained by the computational burden required direct simulations. Due this reason, development of efficient metamodelling techniques represents nowadays an imperative need for designers. In fact, surrogate models has been demonstrated significantly reduce number high-fidelity evaluations, thus alleviating computing effort. Over last years, aeronautical designers community switched from a approach predominantly based on simulations extensive use metamodels. Recently, further improve efficiency, several dynamic approaches parameters self-tuning have developed support metamodel construction. This work deals with Artificial Neural Network noise shielding unconventional aircraft configurations. Here, insertion loss field Blended Wing Body is reproduced means advanced machine learning techniques. relevant framework calculation emitted innovative configurations suitable corrections existing well-assessed prediction tools. algorithm accurate and efficient, observed performance discloses possibility implement numerical strategies reliable robust