Estimation of Engine Inlet Air Temperature in Fighter Aircraft

Abstract: An accurate estimate of the gasturbine inlet air temperature is essential to the stability of the engine since its control depends on it. Most supersonic military aircrafts have a design with the engine integrated in the fuselage which requires a rather long inlet duct from the inlet opening to the engine face. Such duct can affect the temperature measurement because of the heat flow between the inlet air and the duct skin. This is especially true when the temperature sensor is mounted close to the duct skin, which is the case for most engines. This master thesis project therefore revolved around developing a method to better estimate the engine inlet temperature and to compensate for the disturbances which a temperature sensor near the duct skin can be exposed to. A grey box model of the system was developed based on heat transfer equations between different components in the inlet, as well as predictions of temperature changes based on a temperature model of the atmosphere and thermodynamic laws. The unknown parameters of the grey box model were estimated using flight data and tuned to minimize the mean square of the prediction error. The numerical optimization of the parameters was performed using the Matlab implementations of the BFGS and SQP algorithms. An extended Kalman filter based on the model was also implemented. The two models were then evaluated in terms of how much the mean squared error was reduced compared to just using the sensor measurement to estimate the inlet air temperature. It was also analyzed how much the models reduced the prediction errors. A cross-correlation analysis was also done to see how well the model utilized the input signals. The results show that the engine inlet temperature can be estimated with good accuracy. The two models were shown to reduce the mean square of the prediction error by between 84 % and 89 % if you compare with just using the temperature sensor to estimate the temperature. The model which utilized the Kalman filtering was shown to perform slightly better than the other model. The relevance of different subcomponents of the model were investigated in order to see if the model could be simplified and maintain similar accuracy. Some investigations were also done with the relationship between different temperatures of the inlet to further understand the flow patterns of the inlet and to perhaps improve the model even more in the future.