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The on-wing engine performance is difficult to track for thermodynamic modelsbecause of its inaccurate component maps, and also difficult for data drivenmethods for their over-fitting to measurement errors. So, we propose athermodynamic based and data driven hybrid network for gas turbine modeling.Different from thermodynamic models, our network reconstructs the componentcharacteristics in a data-driven way to take component degeneration andindividual difference into consideration. Moreover, different from data drivenmethods, in the training phase, physical based equations and the analyticalmathematical description are used to ensure that the optimization converges tothe gas turbine's dynamics. A huge number of relaxed quasi steady state flightdata to 26970 is used to train and test our hybrid network. The result showsthat the accuracy of our hybrid network can reach about 7% measured by max T6relative error, 5% better than map fitting based thermodynamic model and 8%better than pure data driven method with similar model volume.

A Thermodynamic based and Data Driven Hybrid Network for Gas Turbine Modeling