The noise levels produced by high-performance supersonic military aircraft engines significantly exceed those of civilian aircraft, highlighting the critical importance of predicting military aircraft noise levels. This paper shows the modeling and assessment of the correlation between sound frequency and sound pressure level (SPL) using particle awarm optimisation (PSO) and the cuckoo search algorithm (CSA) for the high-performance fighter aircraft F-22 Raptor. The developed model aims to predict noise with high precision at various microphone angles from 60° to 150°. As a result of the analysis, the MAPE value for CSA was found to be below 1% for 10 different inlet angles, while the maximum mean absolute percentage error (MAPE) for PSO was 1.7863%. The large dataset ranging from 238 to 762 data points are used and the minimal error values confirm the high accuracy of the model. Additionally, the PSO and CSA algorithms were compared indirectly. The lower error values for CSA, along with its correlation coefficient (R) values closer to 1 indicate that the CSA method gives better results than PSO.

This paper describes a reverse engineering methodology so as to accomplish an aero-propulsive modelling (APM) through implementing a drag polar estimation for a case study jet aircraft in case of the absence of the thrust data of the aircraft’s engine. Since the available thrust force can be replaced by the required thrust force for the sustained turn, this approach allows the elimination for the need of the thrust parameter in deriving an aero-propulsive model utilising equations of motion. Two different modelling approaches have been adopted: (i) implementing the 6-DOF model data for sustained turn and climb flight to achieve induced drag model; and then incorporating the glide data to obtain the total drag polar model; (ii) using the 6-DOF model data together with introducing the effect of CL-α dependency. The error assessments showed that the derived CSA models were able to predict the drag polar values accurately, providing linear correlation coefficient (R) values equal to 0.9982 and 0.9998 for the small α assumption and CL-α dependency, respectively. A direct comparison between the trimmed CD values of 6-DOF model and the values predicted by the CSA model was accomplished, which yielded highly satisfactory results within high subsonic and transonic CL values.