Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm
A time-series flight trajectory technique was developed for use in a civil aircraft during descent. The three-degree-of-freedom (3-DoF) equations of motion were solved via time-series prediction of aerodynamic forces. In the present evaluation, the microburst effect during the descent was considered...
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Jurnal Mekanikal
2018
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| author | Othman, Norazila Mansor, Shuhaimi Ab. Wahid, Mastura Abdul Latif, Ainullotfi Wan Omar, Wan Zaidi Mohd. Jaafar, Mohammad Nazri Wan Ali, Wan Khairuddin Dahalan, Md. Nizam Mohd. Nasir, Mohd. Nazri Kanazaki, Masahiro |
| author_facet | Othman, Norazila Mansor, Shuhaimi Ab. Wahid, Mastura Abdul Latif, Ainullotfi Wan Omar, Wan Zaidi Mohd. Jaafar, Mohammad Nazri Wan Ali, Wan Khairuddin Dahalan, Md. Nizam Mohd. Nasir, Mohd. Nazri Kanazaki, Masahiro |
| author_sort | Othman, Norazila |
| collection | ePrints |
| description | A time-series flight trajectory technique was developed for use in a civil aircraft during descent. The three-degree-of-freedom (3-DoF) equations of motion were solved via time-series prediction of aerodynamic forces. In the present evaluation, the microburst effect during the descent was considered. The single-objective optimization problem, in which the cost function indicating the trajectory efficiency was minimized, was solved by means of a Kriging model based genetic algorithm (GA) which produces an efficient global optimization process. The optimal trajectory results were compared with those without the microburst condition during the descent. The minimization solution converged well in each case for both conditions plus the differences in flight profiles based on the trajectory history were smaller than those of the solutions before optimization. An analysis of variance and parallel coordinate plot were applied to acquire the quantitative information for the initial condition of descend. The results revealed that the aerodynamic control factors, such as elevators angle and angles of attack, were effective for the minimization of the cost function when microburst is in effective range. According to the visualization results, it was found that a higher airspeed and a larger aerodynamic control by initial elevator were effective for minimizing the cost function when microburst has appeared. It shows that the developed model produced efficient global optimization and can evaluate the aircraft trajectory under the descent situation successfully. |
| first_indexed | 2024-03-05T20:28:32Z |
| format | Article |
| id | utm.eprints-82296 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:28:32Z |
| publishDate | 2018 |
| publisher | Jurnal Mekanikal |
| record_format | dspace |
| spelling | utm.eprints-822962019-11-21T01:20:29Z http://eprints.utm.my/82296/ Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm Othman, Norazila Mansor, Shuhaimi Ab. Wahid, Mastura Abdul Latif, Ainullotfi Wan Omar, Wan Zaidi Mohd. Jaafar, Mohammad Nazri Wan Ali, Wan Khairuddin Dahalan, Md. Nizam Mohd. Nasir, Mohd. Nazri Kanazaki, Masahiro TJ Mechanical engineering and machinery A time-series flight trajectory technique was developed for use in a civil aircraft during descent. The three-degree-of-freedom (3-DoF) equations of motion were solved via time-series prediction of aerodynamic forces. In the present evaluation, the microburst effect during the descent was considered. The single-objective optimization problem, in which the cost function indicating the trajectory efficiency was minimized, was solved by means of a Kriging model based genetic algorithm (GA) which produces an efficient global optimization process. The optimal trajectory results were compared with those without the microburst condition during the descent. The minimization solution converged well in each case for both conditions plus the differences in flight profiles based on the trajectory history were smaller than those of the solutions before optimization. An analysis of variance and parallel coordinate plot were applied to acquire the quantitative information for the initial condition of descend. The results revealed that the aerodynamic control factors, such as elevators angle and angles of attack, were effective for the minimization of the cost function when microburst is in effective range. According to the visualization results, it was found that a higher airspeed and a larger aerodynamic control by initial elevator were effective for minimizing the cost function when microburst has appeared. It shows that the developed model produced efficient global optimization and can evaluate the aircraft trajectory under the descent situation successfully. Jurnal Mekanikal 2018 Article PeerReviewed Othman, Norazila and Mansor, Shuhaimi and Ab. Wahid, Mastura and Abdul Latif, Ainullotfi and Wan Omar, Wan Zaidi and Mohd. Jaafar, Mohammad Nazri and Wan Ali, Wan Khairuddin and Dahalan, Md. Nizam and Mohd. Nasir, Mohd. Nazri and Kanazaki, Masahiro (2018) Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm. Jurnal Mekanikal, 41 (2). pp. 59-63. ISSN 2289-3873 https://jurnalmekanikal.utm.my |
| spellingShingle | TJ Mechanical engineering and machinery Othman, Norazila Mansor, Shuhaimi Ab. Wahid, Mastura Abdul Latif, Ainullotfi Wan Omar, Wan Zaidi Mohd. Jaafar, Mohammad Nazri Wan Ali, Wan Khairuddin Dahalan, Md. Nizam Mohd. Nasir, Mohd. Nazri Kanazaki, Masahiro Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm |
| title | Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm |
| title_full | Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm |
| title_fullStr | Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm |
| title_full_unstemmed | Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm |
| title_short | Aircraft trajectory optimization during descent using a Kriging-model-based-genetic algorithm |
| title_sort | aircraft trajectory optimization during descent using a kriging model based genetic algorithm |
| topic | TJ Mechanical engineering and machinery |
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