| 基于分解策略的飞行器气动隐身优化设计研究 |
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| 引用本文： | 陈保,白俊强,黎明. 基于分解策略的飞行器气动隐身优化设计研究[J]. 气体物理, 2019, 4(6): 40-49. DOI: 10.19527/j.cnki.2096-1642.0779 |
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| 作者姓名： | 陈保 白俊强 黎明 |
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| 作者单位： | 1.西北工业大学航空学院，陕西西安 710072 |
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| 摘 要： | 为了综合提高飞行器的气动性能与隐身性能，文章利用基于分解策略的梯度优化方法，对某跨声速机翼进行气动隐身综合优化设计.采用Tchebycheff方法，将气动隐身多学科多目标优化问题分解为多个单目标优化子问题，再对每个单目标子问题进行梯度优化.通过求解离散伴随方程获得气动目标对设计变量的梯度，采用自动微分方法对物理光学法（physical optics，PO）程序进行微分，即可得到雷达散射截面（radar cross section，RCS）对设计变量的梯度.经过综合优化，获得优化解集中各给定权重系数对应下的分支解，相比初始机翼，优化机翼的阻力系数减小，升阻比提高，重点方位的雷达散射截面均值减小，验证了该优化设计方法具有较好的实用性.
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| 关 键 词： | 分解策略 梯度优化 离散伴随方程 多学科优化 物理光学法 |
| 收稿时间： | 2019-06-22 |
A Study of Aerodynamic and Stealth Optimization Design forAircraft Based on Decomposition |
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| Affiliation: | 1.School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China2.Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China |
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| Abstract: | To improve aerodynamic performance and stealth performance of aircraft synthetically, in this paper, a gradient optimization method based on decomposition was used in the aerodynamic and stealth integrated optimization design of a transonic wing. Based on Tchebycheff approach, the multi-disciplinary multi-objective optimization problem of aerodynamics and stealth was decomposed into several single-objective optimization subproblems. Each single-objective subproblem was solved by using gradient optimization methods. The gradient of the aerodynamic objective parameters to design variables could be obtained via the discrete adjoint equations. The physical optics (PO) code is differentiated through the automatic differentiation, thus the gradient of the radar cross section (RCS) to design variables would be obtained. Every branch solution corresponding to the weight coefficient given in the optimization set is obtained by integrated optimization design. Both the drag coefficient of the optimized wing and the mean value of RCS at key position decrease, and the lift-drag ration of the optimized wing increases, as compared with the original wing. It is verified that the optimization method has better practicability. |
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