# Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine
## 1 Introduction
They attempted to demonstrate the difference in performance measures under ideal and practical conditions. The effects of efficiencies and pressure ratios of different components on engine output parameters were also investigated under various flight conditions. Various machine learning algorithms were used by [PERSON] and [PERSON] to determine the dynamics of turbojet engines, particularly the Viper 632-43 engine, using the exhaust gas temperature as the fundamental variable [2]. A neural network was developed that recognizes the provided information as a continuous sequence and anticipates the output in the following run. The one-step-ahead strategy, which determines the exhaust gas temperature in the following run, was assigned the prediction model. An artificial neural network (ANN) model for a micro-gas turbine was developed by [PERSON] et al. [3] that could accurately predict its characteristic performance. Subsequently, they employed the ANN to analyze the functionality and emissions of a recuperative micro-gas turbine that burned natural gas and biogas mixes. [PERSON] et al. [4] analyzed a micro-gas turbine with an ANN to determine the effect of ambient conditions on its performance and estimate the pollution caused by it. A two-intercooler, two-reheater gas turbine was studied by [PERSON] [5] for the parametric analysis of design variables and their effect on energy, energetic, and ecological performance using an ANN. ANNs have also been used in other gas turbine-based applications. Deep learning was used by [PERSON] et al. [6] to estimate the energy and exergy performance of the F135 PW100 turbofan engine. An intelligent model was implemented in their study to estimate output characteristics. [PERSON] et al. [7] devised a technique for detecting and isolating gas turbine malfunctions using modular convolutional neural networks. They analyzed the results of a single convolutional neural network architecture-based fault classification approach and a deep long short-term memory-assisted fault detection and isolation method. [PERSON] et al. [8] suggested an artificial intelligence algorithm to estimate the degradation of power plant gas turbines for primary engine components using deep learning. The degradation data from the thermodynamic model was used to train the neural network model. The most effective framework was achieved through reducing the mean square error. In addition, thermodynamic analysis was used to assess the performance of the neural network using dissimilar degradation data as its testing data. [PERSON] et al. [9] predicted the functional parameters of an industrial gas turbine with an ANN. This model accurately predicted the performance parameters with real-time data as its input. [PERSON] et al. [10] used reinforcement learning to estimate the performance variables of a turbofan engine with minimum error. [PERSON] et al. [11] presented an artificial neural network-based framework for the detection of externally fueled gas turbines to identify intrinsic flaws, as well as the influence of fuel. [PERSON] and [PERSON] [12] devised a prediction model with supervised learning for gas turbine performance. The turbine and compressor's functional features were determined using an artificial neural network. [PERSON] et al. [13] 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. [PERSON] and [PERSON] [14] applied machine learning to develop a methodology for predicting an aircraft's remaining useful life employing complete life cycle data and degradation parameter data. A deep layer recurrent neural network model was developed to estimate a turbofan engine's durability. [PERSON] et al. studied the turbojet engine's parametric cycle using two simulation programs and differentiated their results. The optimum design characteristics that enhanced the engine's performance were concluded through their research [15]. [PERSON] et al.