Transient system simulation for an aircraft engine using a data-driven model

Sangjo Kim, Kuisoon Kim and Changmin Son

Energy, 2020, vol. 196, issue C

Abstract: In this paper, a simulation approach using a data-driven model to predict the performance of a gas turbine for aircraft engines during transient operations is proposed. The low bypass ratio and mixed-flow turbofan engine is considered in the simulation. The input parameters for the training of the data-driven model are fuel mass flow rate, altitude, flight Mach number, and the required power due to the moments of inertia of the rotating parts. The output parameters in the data-driven model are engine net thrust, low-pressure shaft rotating speed, pressure and temperature at each station, propulsion efficiency, efficiency of energy conversion and the overall efficiency. The data-driven model is trained using the data set obtained from a validated first principle model. In the simulation, using the data-driven model, the final engine performance is calculated using an iterative calculation for converging the power balance equation that considers the required power due to the moments of inertia at each time step. The transient performance simulation is tested during a throttle frequency sweep maneuver. In a comparison between the first principle model and the proposed simulation approach, the R-squared values of the output parameters are higher than 0.98, except for the efficiency of energy conversion and the overall efficiency, which register 0.9715 and 0.9183, respectively. It has been confirmed that the proposed simulation approach using the data-driven model can be applied to the transient simulation.

Keywords: Transient system simulation; Aircraft engine; Data-driven model; Artificial neural network; Radial basis function (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301535

DOI: 10.1016/j.energy.2020.117046

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