Reliability Based Design Optimization of a Supersonic Engine Inlet

This paper presents risk-based design optimization of a supersonic axisymmetric outwardturning engine inlet with geometric uncertainties by exploiting a convolutional neural network. Due to the exorbitant computational burden, a convolutional neural network-based surrogate model is implemented to be used in the prediction of engine performance parameters which are total pressure recovery and mass flow ratio. To generate a dataset, 256 unique configurations for the inlet are parametrically designed in Engineering Sketch Pad while the SU2 Suite is used to obtain a solution of supersonic flow domains. For sought-after optimum reliable design, gradient-free particle swarm optimization is incorporated with a first-order reliability method. Inlet buzz is considered as the critical phenomenon in computations of the reliability of the engine while the maximum probability of failure is limited with 10⁻⁷ in the optimum inlet configuration.