A key challenge in the gas turbine community is to adapt the engine model by matching measured data with simulation data. This study presents a novel procedure aiming to calibrate the gas turbine component maps and predict unmeasured parameters simultaneously. To reproduce degradation, disturbance is injected into the healthy components maps at different times. Subsequently, fifteen correction coefficients along with measured data and unmeasured parameters are coupled together using cooperative working equations and optimized based on primal-dual interior point method. When performing the adaptive procedure, Jacobian and hessian matrices are calculated using finite difference since the component maps have external, mapped, functions implemented as lookup-tables, and mode-switching statements. To improve the accuracy of first-order and second-order partial derivatives, the finite difference is enhanced by Richardson extrapolation method. The search scope of correction coefficients and unmeasured parameters are determined by the whole working conditions. Meanwhile, an adaptive update method of initial solution is proposed to make sure the convergence of the optimization procedure as quickly as possible. Finally, the proposed method is further applied to the on-line adaptation of component maps in case of performance degradation. It is demonstrated that the method is capable of refining the component maps progressively during long-term run phase, which is significant for the model-based gas path diagnostics and prognostics.


On-Line Component Map Adaptive Procedure Based on Sensor Data

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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