Performance models of passenger aircraft and propulsion systems based on particle swarm and Spotted Hyena Optimization methods

In aviation, monitoring, and evaluation of parameters regarding the correct operation of engine systems and parts is vital for flight safety and maintenance follow-up. In this context, the thrust value of the most widely used aircraft and engine in the world has been calculated with Particle Swarm Optimization (PSO) and Spotted Hyena Optimization (SHO) methods, which have proven solution time and convergence in previous studies and applications. In both optimization methods performed in the study, the number of iterations, the spacing of the population and the search space were chosen equally for the comparison of the optimization methods. While the PSO obtained the RMSE train value as 4.4792 and the test value as 3.9289 within 125 s, the SHO method obtained the RMSE train value as 4.8684 and the test value as 4.3520 in around 35 s. The data used were taken from 50 real flights and 40 were used for training and 10 for testing purposes. It is seen that this system's data, which does not have a backup, can be obtained with the help of algorithms with different engine data taken from the sensors. Engine shaft rotation speed (N1) value, which is the flight control parameter that the thrust value is followed by the pilots in the cockpit for the safety of the flight, has been calculated with high accuracy for all flight phases from taxi to landing, without dividing into flight phases. The methods used and convergence time hold promise for flight safety.