Research on a component characteristic adaptive correction method for variable cycle engines

Abstract

There is inevitably a performance deviation between an engine model and an actual engine that is influenced by unpredictable factors such as the unsuspected environmental conditions and the natural performance degradation in the process of use. Because the engine model precision largely depends on the accuracies of the component maps, it is possible to revise the engine model to determine a better trend for the engine performance from recorded measurements by adjusting the maps. This paper presents a new method for updating the variable geometry component maps of a variable cycle engine (VCE) by using a set of scaling factors estimated with the cubature Kalman filter (CKF). A mapping function is created between the scaling factors and the component characteristic scaling coefficients for the adjustments of the maps. The proposed method is applied to a VCE model according to the VCE benchmark steady-state performance data. The results show that the maximum simulation error of the engine steady-state model decreases from 5.33 to 0.93%, and the CKF-based adaptation method provides a much faster computing rate than the particle swarm optimization (PSO) based adaptation method, which verifies the effectiveness and engineering applicability of the variable geometry characteristic adaptive correction method.

Developing and correction methods for a one-dimensional axial flow compressor model

“…In this paper, the PSO algorithm [12] is employed to correct the model. PSO is a simulation of cooperative behavior in birds' foraging and movement, serving as an iterative optimization tool and a populationbased multi-objective stochastic optimization technique.…”