Cruise range modeling of a transport aircraft using machine learning

Abstract Accurate estimation of an aircraft cruise range is a critical challenge in aeronautical engineering that directly influences fuel efficiency, operational costs and environmental sustainability. Traditional analytical models, such as the Bréguet range equation, provide simplified range estimates but often fail to capture the nonlinear dependencies between flight parameters, fuel consumption and aerodynamic efficiency. In this study, machine learning-based regression methods have been employed to model the aircraft cruise range using the cruise flight data of Boeing 737–400, which is one of the most widely used commercial aircraft. Based on the calculated performance metrics, it is determined that the Gaussian process regression model exhibits superior validation and test performance compared to the other models. The developed models provide deeper insight into range variations under various flight conditions by performing accurate and robust predictions.