Neural Network‐based Method to Compute Recommended Cruise Level and Optimal Climb Location

Abstract

AbstractSystem engineering towards utilizing machine learning solutions in the aviation domain is a fast‐progressing discipline. This paper tries to bring out the system engineering and implementation aspects of ML solution as an improvement in a sub‐system of an existing aviation system. The flight management systems (FMSs) compute the recommended cruise level (RCL) considering the aircraft's gross weight, its speed schedule, and the forecast wind. The RCL increases with distance in the no wind conditions as the fuel get consumed, reducing the gross aircraft weight. The RCL profile may have frequent climbs and descents in the presence of winds, which is not desired for flight operations.The document proposes a neural network‐based method for computing an operationally acceptable cruise profile considering the wind and temperature weather forecast. The method recommends step climbs and step descents (optimal level and optimal step location) in such a way that they are separated by a large enough cruise phase. From system engineering point of view two guiding documents for such an implementation to look forward are “EASA Concept Paper: First usable guidance for Level 1 machine learning applications” [12] and “Process Standard for Development and Certification/Approval of Aeronautical Safety‐Related Products Implementing AI” [13].Two neural networks have been created to estimate wind compensated ECON cruise speeds and required time‐fuel to cover the next 500NM distance. The RCL profiles are generated for various test wind scenarios for Airbus A220 aircraft. It has been recorded the proposed neural network‐based method is better at recommending a cruise altitude and it is 9X faster than the existing method to calculate the RCL and optimal climb location. The neural network‐based design also helped in reducing the memory footprint by more than 100 times as there is no requirement of storing the performance database files required by the existing system. Only the parameters of the trained neural networks are required to be stored. Most importantly, the RCL computed by the proposed neural network‐based method yields a smaller operational cost than that determined by the existing method. The proposed method is designed to replace the existing traditional subsystem to give an overall improved performance while the functionalities of other subsystems remain unaffected.