An evolutionary algorithm (EA) was applied in this study to optimize the landing flight path of a delta-winged supersonic transport (SST). However, it is difficult for a delta wing with a large sweepback angle to redu...
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An evolutionary algorithm (EA) was applied in this study to optimize the landing flight path of a delta-winged supersonic transport (SST). However, it is difficult for a delta wing with a large sweepback angle to reduce the aerodynamic drag during supersonic cruising to gain sufficient lift force at low speeds, particularly during takeoff and landing. Besides, high-fidelity computational fluid dynamics is required to evaluate the flight path with a complex flowfield. This study performed an efficient flight simulation based on the Kriging model-assisted aerodynamic estimation to carry out global optimization. Then, the designs of the flight and control sequence were realized for time-series optimization of effective SST landing. To develop the EA, two design scenarios were considered;one involved only the elevator, which is an aerodynamic control surface that controls the aircraft, and the other involved introducing thrust control in addition to elevator control. In the scenario involving only elevator control, feasible solutions could not be obtained owing to the poor low-speed aerodynamic performance of the SST. This paper presents several feasible solutions enabling reasonable SST landing performance in the scenario involving the elevator and thrust controls along with descriptions regarding the optimum flight and control sequences. In addition, we analyzed the solutions by analyzing the variance to obtain qualitative information. Consequently, we determined that elevator control was considerably effective in cases with the microburst effect than in cases without the microburst effect.
作者：
Zhang, QianWang, QunjingLi, GuoliAnhui Univ
Sch Elect Engn & Automat Hefei 230039 Peoples R China Anhui Univ
Engn Res Ctr Power Qual Minist Educ Hefei 230039 Peoples R China Anhui Univ
Natl Engn Lab Energy Saving Motor & Control Techn Hefei 230039 Peoples R China
The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna se...
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ISBN:
(纸本)9781479983896
The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna servo turntable, such as low-speeding, changing-over, acceleration or deceleration etc. In this paper, a kind of switched model is introduced to describe the relationship between input and output signals in complex working conditions for the turntable. Then the parameters identification of switched system is summarized as a constrained multi-objective problem (CMOP). Further, the Comprehensive Learning Particle Swarm Optimization (CLPSO) is applied to the proposed CMOP to obtain a set of appropriate parameters. Finally, the simulation and quality of fitness calculation results demonstrate the precision of the switched model and effectiveness of the identification algorithm.
The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna se...
详细信息
ISBN:
(纸本)9781479984671
The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna servo turntable, such as lowspeeding, changing-over, acceleration or deceleration etc. In this paper, a kind of switched model is introduced to describe the relationship between input and output signals in complex working conditions for the turntable. Then the parameters identification of switched system is summarized as a constrained multi-objective problem (CMOP). Further, the Comprehensive Learning Particle Swarm Optimization (CLPSO) is applied to the proposed CMOP to obtain a set of appropriate parameters. Finally, the simulation and quality of fitness calculation results demonstrate the precision of the switched model and effectiveness of the identification algorithm.
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