Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications

Abstract

Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications.