Gas Path Parameter Identification of Turbofan Engine for Carrier Aircraft via Hybrid Mutated Pigeon-Inspired Optimization

Abstract

Carrier aircraft is a commonly concerned issue in scientific research due to its extensive military use. Turbofan engine has equipped nearly every carrier aircraft to provide propulsion and gas flow. Gas path parameter identification is performed to establish a mathematical component model for dynamic in-loop simulation. In this paper, the identification is transformed into a two-stage optimization problem, solving by bionic intelligent computation and adaptive Newton Raphson (NR) Iteration. Adaptive step-size adjustment is applied in NR and dynamic scale coefficient in cost function brings convergence to the steady state equation of component model. To reduce the difficulty of deciding the initial status, typical mutation mechanism is utilized to enhance the exploitation characteristic of Pigeon-Inspired Optimization, which is effective in searching for the suitable initial value of NR method. Finally, comparative simulation is put forward to prove the satisfactory performance of the novel optimization method towards other typical swarm intelligence algorithm.