Surrogate Based Shape Optimization of a Low Boom Axisymmetric Body

The minimization of the intensity of the sonic boom is a key enabler for a future supersonic transport aircraft with a high appeal to potential operators as well as broad acceptance by the general public. In this paper a robust surrogate based optimization method including high-fidelity near-field Euler simulations with the DLR TAU code, the TRAPS propagation algorithm and the evaluation of the level of perceived loudness is developed. By optimizing a low boom body of revolution geometry provided by the Second AIAA Sonic Boom Prediction Workshop, an approach for the parametrization of the geometry to minimize the generation of irregular geometries is presented and the required number of design variables to obtain shaped pressure signatures is described. It is shown that the setup of the surrogate model is very important for fast convergence and good optimization results. The best and fastest convergence of the optimization was achieved with a polynomial order of the trend function of the surrogate model larger than two and the Differential Evolutionary algorithm for the tuning of the hyperparameters of the surrogate model. Multi-objective low-boom, low-drag optimizations are compared with a single-objective low-boom optimization. Although both optimizations yield an improvement of the perceived loudness and the drag, the improvement in loudness is larger for the singleobjective optimization