Sonic boom minimization using inverse design and probabilistic acoustic propagation

near-field signature is more accurately represented. The problem of F-function parameters’ estimation is reformulated as a gradient-based optimization problem and solved. Sonic boom propagation is carried out in a probabilistic fashion using parametric atmospheric models and statistical techniques. A bilevel pseudoinverse optimization is performed using coarse-grained parallel genetic algorithms to design aircraft that meet low sonic boom requirements under atmospheric uncertainty. The optimization analysis is split into two cycles with multiple conflicting objectives. Results are presented and discussed.