Predictive Modeling of Aircraft Engine Heat Rejection Using Artificial Intelligence Techniques in Python

Abstract

In the realm of aviation, understanding and accurately predicting the heat rejection from aircraft engines is pivotal for ensuring efficient operation and safety. This paper presents a novel approach utilizing artificial intelligence (AI) techniques, specifically implemented in Python programming, for predictive modelling of aircraft engine heat rejection. Unlike conventional methods that rely heavily on predefined models or assumptions, our approach empowers users to input their data, enhancing flexibility and applicability across diverse aircraft configurations and operational conditions. The methodology integrates various AI techniques, including machine learning algorithms and data preprocessing methods, to extract meaningful patterns and relationships from the provided data. Through iterative training and validation processes, the model learns to effectively capture the complex interactions influencing heat rejection from aircraft engines. Python programming facilitates the implementation of these AI techniques, providing a versatile and efficient platform for data analysis and model development. The proposed predictive model offers significant advantages in terms of accuracy and adaptability, as it can dynamically adjust to different input parameters and environmental factors. By harnessing the power of AI and Python programming, engineers and researchers can gain valuable insights into aircraft engine thermal behaviour, enabling informed decision-making and optimization of heat management strategies. Overall, this research contributes to advancing the field of aerospace engineering by offering a robust and user-friendly framework for estimating aircraft engine heat rejection, ultimately enhancing operational efficiency and safety in the aviation industry.