Experimental Evaluation of Different Microturbojet EGT Modeling Approaches

Microturbojets are one of the main thrust sources in several high-performance unmanned aerial vehicles (UAVs). In many applications these vehicles operate in conditions that may demand performance at the limits of microturbojets' capabilities. These conditions can eventually result in component degradation and engine malfunction. Safety and reliability demand to monitor and diagnose the health of the motor during the operation of the vehicle. One of the main microturbojet health indicators is the exhaust gas temperature (EGT). The scope of this article is to evaluate the estimation accuracy and computational cost of several EGT models through extensive experimental data. The evaluated models include classical, data-based, and novel approaches: an adiabatic nozzle representation, a nozzle model with a lineal correction, a nozzle model including heat transfer, a genetic programming model, a feed-forward neural network, a polynomic approximation and a multiphysics model. This evaluation provides information regarding the advantages and disadvantages of each model. The results show that the best-suited model depends mainly on the application and available identification data.