Multi-Source energy optimization method for supersonic aircraft based on multi-objective adaptive covariance matrix and chaotic search group algorithm

Abstract

In the trend of increasing aircraft electrical power levels, future supersonic aircraft will have an urgent need for multi-source, adaptive, and high-energy-efficient approach to electrical power generation. In this paper, a multi-source energy optimization method based on multi-objective optimization algorithm MOACCGA is proposed. The method considers fuel consumption rate and thrust as the objective functions, with engine safety as a constraint. Based on the establishment of a small bypass ratio turbofan engine with multi-source energy extraction system, the influence mechanism of energy extraction on engine performance is analyzed. It is concluded that the generation of multi-source energy is optimizable, and the objective function has strong nonlinear and multi-peak distribution in the solution space. Then, the multi-objective adaptive covariance matrix and chaotic search group algorithm (MOACCGA) is proposed to solve the multi-source energy optimization problem. The algorithm utilizes the evolutionary approach with an adaptive covariance matrix to adapt to the strong nonlinearity of the system. Additionally, it overcomes the problem of getting trapped in local optima within the solution space characterized by multiple peak distributions by employing chaotic search. Finally, the multi-source energy optimization method is validated through simulations. In each condition, multi-source energy optimization can save fuel consumption by 0.7% of the total fuel consumption and reduce thrust loss by 1.5% of the total thrust.