PERFORMANCE OPTIMIZATION of FIGHTER XF-103 by GENETIC ALGORITHMS (Case Study: Minimum Time to Climb Problem)
Fighter climb path optimization is one of the aircraft performance optimization problem focused on how to find the best way to minimize time to climb in executing the mission. There are three approaching methods applied on this study to overcome the minimum time to climb problem. The first is conven...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/43480 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Fighter climb path optimization is one of the aircraft performance optimization problem focused on how to find the best way to minimize time to climb in executing the mission. There are three approaching methods applied on this study to overcome the minimum time to climb problem. The first is conventional method using steady state approximation; in this method, optimum climb path is achieved by choosing the best speed at given altitude in subsonic area. The second is Rutowski method; in this method, optimum climb path is achieved by choosing speed (or altitude) at given constant energy, or more widely known as energy-state approximation. Both methods become the optimization basis for minimum time to climb. The third is point-mass approximation using dynamic modeling with two state point-mass equation, ????? and ?? as state variable, mass is considered as linier function of range and three state point mass equation, V’, h’ and mass , m’ is state variable and the path angle, ???? as control variable. Minimum time climb optimizasion in this study is treated by applying Genetic Algorithms (GA) method. GA can not be directly applied to solve minimum time climb problem. Without knowing total time to climb, it would be hard to complete the integration of dynamics equations In this study, range variable is used to replace time variable as independent variable. This is a simple way to overcome the difficulty of solving the minimum time problem directly. GA optimization using two state variable dynamic modeling results in 258.8 seconds of climb time which is 5.6% more realistic compare to Rutowski method and 42.7% more optimistic compare to conventional method. Whereas, three state variable dynamics modeling results in 263.7 seconds in climb time. This result is 7.4% more realistic compare to Rutowski method and 41.7% more optimistic compare to conventional method. The presented results are based on the configuration data of XF-103.
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