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摘要:
针对传统多学科设计优化(MDO)模型复杂导致计算量大、收敛困难等问题,以高超声速飞机为研究对象,提出一种基于贝叶斯优化理论的飞机翼面布局与任务轨迹一体化设计优化方法。构建包含翼面特征参数的气动特性代理模型,在此基础上,以高超声速飞机翼面特征参数为输入,以
hp 自适应Radau伪谱法求解得到的最优飞行轨迹燃油消耗量为输出,基于贝叶斯优化方法构建翼面布局-轨迹一体化迭代设计流程,通过期望改进(EI)策略更新样本点参数,实现面向特定飞行任务的高超声速飞机翼面布局与任务轨迹一体化优化设计。仿真结果表明:所提方法能够在满足优化结果收敛精度的前提下,显著提升迭代设计效率,具有较强的工程应用价值。Abstract:An integrated optimization approach based on Bayesian optimization theory is suggested for hypersonic aircraft in order to lower the computing cost and speed up the rate of convergence during multidisciplinary design optimization (MDO). This approach simultaneously gives the best wing arrangement and matching mission trajectory. Firstly, surrogate models of aerodynamic characteristics coefficients are constructed for hypersonic aircraft with different wing configurations. Based on this, an integrated iterative design process for the wing layout and trajectory is built using the Bayesian optimization approach. The output of this process is the optimal fuel consumption determined by the
hp adaptive Radau pseudospectral method, while the input is the wing design parameters. With the sample points updated through the expected improvement (EI) function, the wing layout and corresponding optimal mission trajectory for specific flight missions are updated automatically. Simulation results show that the proposed method can significantly improve the iterative design efficiency while keeping the convergence accuracy, and it shows great value in engineering applications. -
表 1 高超声速飞机基准几何特征参数
Table 1. Reference parameters of geometric features of hypersonic aircraft
几何参数 数值 机长/m 22.484 机宽/m 3.400 机高/m 1.121 翼展/m 3.600 翼根弦长/m 9.256 翼梢弦长/m 1.000 主翼第一后掠角/(°) 70 主翼第二后掠角/(°) 38.133 机翼投影面积/m2 32.085 空重/kg 11000 起飞质量/kg 20000 表 2 飞行任务条件
Table 2. Flight mission conditions
任务条件 h/km Ma m/kg $\gamma $/(°) r/km 爬升段
初始条件17.6 3 20000 0 0 爬升段
终端条件24.2 5 $ \geqslant $ 11000 0 巡航段
终端条件24.2 5 $ \geqslant $ 11000 0 1000 表 3 优化结果
Table 3. Optimal results
方法 ${\theta _1}$/(°) ${c_1}$/m 剩余质量/kg 计算时间/s 基准翼面 70.000 1.000 17007.482 13.056 贝叶斯优化 77.549 0.800 17115.889 637.475 遗传算法 77.383 0.800 17115.938 15517.108 -
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