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 by direct simulations. Due to this reason, the development of efficient metamodelling techniques represents nowadays an imperative need for the designers. In fact, surrogate models has been demonstrated to significantly reduce the number of high-fidelity evaluations, thus alleviating the computing effort. Over the last years, the aeronautical designers' community has switched from a design approach predominantly based on direct simulations to an extensive use of metamodels. Recently, to further improve the efficiency, several dynamic approaches based on self-tuning parameters have been developed to support the metamodel construction. This work deals with the use of surrogate models based on Artificial Neural Network for the noise shielding of unconventional aircraft configurations. Here, the insertion loss field of a 2D airfoil is reproduced through advanced machine learning techniques. The relevant framework is the calculation of the noise emitted by innovative aircraft configurations by means of suitable corrections of existing well-assessed noise prediction tools. The self-tuning algorithm has demonstrated to be accurate and efficient, and the observed performance discloses the possibility to implement numerical strategies for the reliable and robust unconventional aircraft optimal design.