# Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine
## 1 Introduction
coded a simulation program, pyCycle, to analyze novel gas turbine cycles [16]. [PERSON] et al. [17] conducted a thermodynamic cycle analysis of turbofan components under off-design conditions. Different design parameters were considered to determine the performance parameters and constraint functions of a reference engine. The thermodynamic analysis of a turbofan engine with pyCycle and numerical propulsion system simulation program was compared. The pyCycle code was found to be more computationally efficient. Various machine learning algorithms were used by [PERSON] and [PERSON] to determine the dynamics of turbojet engines, particularly on the Viper 632-43 engine, using exhaust gas temperature as the fundamental variable [18]. [PERSON] et al. analyzed the exergetic sustainability of a variable cycle engine. They analogized the results with the gas turbines used in fighter aircraft. Different variables were used for performance analysis with single and double bypass modes [19]. [PERSON] et al. diagnosed the faults in gas turbine engines using convolution and recurrent neural networks and compared their accuracy. They found optimized concurrent neural networks to be more precise and stable [20]. [PERSON] et al. employed Bayesian optimization and deep learning models to accurately predict the component failure of a turbofan engine [21]. [PERSON] et al. proposed a way to extend the acceleration controller of a small bypass ratio turbofan engine based on deep reinforcement learning to tackle the multidimensional constraint optimization issue in continuous action space [22]. Advanced machine learning methods, such as Cond-LSTM, were found to give precise predictions for turbofan engine performance under specific flight conditions by [PERSON] et al. [23]. Previously, a transonic compressor instability prediction tool for the extensive modelling of axial compressor dynamics was developed by [PERSON] et al. Supervised learning was applied to estimate compressor performance at varied flow conditions relying on a dataset from CFD simulations. [PERSON] et al. analyzed the performance and stability of a low-bypass turbofan transonic axial compressor with swirl distortion. Furthermore, the non-uniform inflow swirl flow of the compressor rotor was also examined using the mathematical models based on the 1D mean-line code and Dynamic Turbine Engine Compressor Code (DYNTECC). The flow field in the transonic micro-axial flow compressor's tip clearance zone was previously studied under non-uniform flow circumstances. The research focused on the negative effects of a steady-state distorted pattern of total pressure inlet flow conditions, both under-design and off-design RPM [24; 25; 26]. This literature review indicates that, while a large body of literature studying the performance of gas turbine engines exists, it appears that a comprehensive investigation has not been considered on the F100-PW229 engine. Previously, researchers have applied machine learning algorithms on turbofan engines for their thermodynamic analysis and to predict their remaining useful life, among other topics. Professedly, this research is unprecedented as the use of supervised machine learning and deep learning for the prediction of parametric analysis of the aforementioned turbofan engine has not been researched before. Therefore, in the present study, the effects of various flight conditions on the F100-PW229 engine have been investigated. The effects of the Mach number, fan pressure ratio, altitude, turbine entry temperature, and bypass ratio on the specific thrust, propulsive efficiency, specific fuel consumption, and total fuel flow were examined. The regression learning models and deep neural networks were then programmed on this dataset to predict responses for new input data.