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Knowledge based approach to ground refuelling optimization of commercial airplanes
Author(s) -
Plaza Elías,
Santos Matilde
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12631
Subject(s) - airplane , computer science , heuristic , range (aeronautics) , genetic algorithm , process (computing) , stability (learning theory) , function (biology) , position (finance) , work (physics) , set (abstract data type) , operations research , aerospace engineering , engineering , mechanical engineering , artificial intelligence , finance , machine learning , evolutionary biology , economics , biology , programming language , operating system
This work aims to establish a general and optimized procedure for the initial refuelling of commercial airplanes, as this loading process is strongly related to safety and energy saving issues. The on‐ground refuelling is addressed as an optimization problem whose cost function involves expert knowledge about constraints and factors that influence the aircraft stability and performance. Several heterogeneous criteria (fuelling time, structural load, flow transfers, etc.) have been considered and weighted accordance to its importance in terms of stability. This allows us to adapt the strategy to any type and planned trip of the airplane. The priority is the positioning of the centre of mass of the civil aircraft within safety and manoeuvrability margins, and near the optimal position. Evolutive algorithms are applied, keeping feasible solutions by modifying genetic operators. As a case of study, the initial refuelling of a long range type commercial aircraft, the Airbus A330‐200, is analysed. Simulation results have proved this methodology to be efficient and optimal. Even more, this heuristic and general approach improves the traditional solution that follows a set of pre‐defined rules that are specific for each type of aircraft.