Aerodynamic design optimization is a key aspect in aircraft design. The further evolution of advanced derivatives requires powerful toolbox. Reinforcement learning (RL) tool but has rarely been utilized the aerodynamic It can potentially obtain results similar to those human designer, by accumulating experience from training. In this work, popular RL method called proximal policy (PPO) proposed...

In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...

Adjoint method is widely used in aerodynamic design because only once solution of flow field required for it to obtain the gradients all variables. However, computational cost adjoint vector approximately equal that computation. In order accelerate and improve efficiency adjoint-based optimization, machine learning modeling presented. Deep neural network (DNN) employed construct mapping between...