Izvorni znanstveni članak

https://doi.org/10.17559/TV-20221023193735

Application of Cargo Distribution Computation in Airbus A330 Cargo Aircraft with Optimization Algorithms

Yalçin Albayrak ; Akdeniz University, Department of Electric and Electronics Engineering, Engineering Faculty Dumlupinar Boulevard 07058 Campus Antalya Turkey
Bünyamin Atak ; Akdeniz University, Department of Electric and Electronics Engineering, Engineering Faculty Dumlupinar Boulevard 07058 Campus Antalya Turkey
Okan Oral orcid.org/0000-0002-6302-4574 ; Department of Mechatronics Engineering, Engineering Faculty Dumlupinar Boulevard 07058 Campus Antalya Turkey

Sažetak

Weight and balance problems are one of the main reasons for cargo aircraft accidents including around 30% of accidents that are due to Center of Gravity (CG). Because the pilots often calculate CG index using Load & Trim Sheets manually or use a set of simple formulas, in these calculations, it is only checked whether CG index is within the safe zone instead of determining the ideal value. In order for the safety and fuel economy to be maximized in an aircraft, CG index should be calculated at the ideal value given in the Aircraft Handling Manual. Due to safety and cost concerns, airline companies prefer non-commercial optimization solutions. Therefore, we proposed new heuristic approaches that have been motivated by a real-world application for a major airline company. First, we applied standard GA, WSA, PSO algorithms to obtain a solution that is as close as possible to the ideal CG index in an Airbus A330 cargo plan. Then, we modified standard WSA and PSO algorithms to decrease the error value and to better achieve the ideal CG index. These proposed heuristic solutions have the potential to help the pilots flying cargo aircraft with maximum safety and minimum fuel consumption.

Ključne riječi

center of gravity optimization algorithm; optimization; weight and balance calculation

Hrčak ID:

305450

URI

https://hrcak.srce.hr/305450

Datum izdavanja:

28.6.2023.

Posjeta: 1.300 *