Thermal shock mechanism and thermal environment influencing factors of a new concentric canister launcher
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摘要: 针对新型同心筒自力发射高速热冲击载荷下热环境评估与影响因子决策问题，结合弹性变形和域动分层结合的动网格技术，求解了二维轴对称Navier-Stokes方程，分析了新型路基同心筒流场机理与热冲击特性，并确定了热环境评价指标；通过建立以优化拉丁超立方试验设计和径向基神经网络为理论基础的近似数学模型，解决了CFD自动建模困难、计算量大的难点；结合径向基神经网络训练方法，对导弹热环境的影响因子进行了智能决策研究。分析表明：倒吸进入新型同心筒内筒的低温气体有力改善了同心筒热环境；建立的近似模型精度较高，满足工程需求；对导弹热环境的影响因子从大到小依次为筒底导流板直径、筒底导流板长度、导流器高度；为导弹热环境多学科优化设计提供参考。Abstract: In this work, by adopting dynamic mesh technology along with the spring based smoothing method and the laying based zone moving method, we have numerically solved the axisymmetric N-S equations, analyzed the flow field mechanism and thermal shock characteristics, identified the thermal environment evaluating and influencing factors that are essential for dealing with problems in decision making of the new land-based concentric canister launcher (CCL) under the high-speed thermal shock load condition, and determined the evaluation index of the thermal environment. The mathematic model was established by optimal Latin hypercube design and radial basis function neural network (RBFNN), thus greatly facilitating the automatic modeling and compensating for the large amount of calculation for CFD. The intelligent decision research of the influencing factors for the missile thermal environment was performed using the RBFNN training method. The numerical results show that the thermal environment of the internal canister and the external cylinder are improved by the cryogenic gas coming from the cylinder port; the approximate model is accurate enough to meet the engineering standards required; the influencing factors for the missile thermal environment load are, according to their ranking from high to low, are the following: The diameter of the cylinder bottom baffle plate, the length of the cylinder bottom baffle plate, the height of the deflector. The research of the influencing factors will lay a solid foundation for the multidisciplinary optimization of the thermal environment.
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图 4 2种方案观测1面温度时程曲线
Figure 4. Temperature histories of the two schemes on observation plane 1
图 5 路基新型方案在不同时刻温度分布云图
Figure 5. Contour of temperature distribution at different time in new roadbed scheme
图 6 新型同心筒优化参数
Figure 6. Optimized parameters of a new concentric canister launcher scheme
图 7 3种随机方案观测1面温度时程曲线
Figure 7. Temperature histories of three random schemeson observation plane 1
图 10 热环境评价指标变化比例曲线
Figure 10. The changing ratio curve of the evaluating indexof thermal environment
表 1 3种随机方案中观测1面相关参量
Table 1. Related parameters of three random schemes on observation plane 1
方案 Tmax/K $\int_{0}^{0.1}{(T-300)}\text{d}t$ 1 642.88 7.029 2 1 059.20 22.849 3 1 372.37 14.510 
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表 2 优化拉丁超立方设计样本空间
Table 2. Sample space of optimal Latin hypercube design
试验 L1/Max(L1) L2/Max(L2) d/Max(d) 1 0.613 6 0.905 6 0.936 5 2 0.823 7 0.571 4 0.796 6 3 0.810 2 0.796 6 0.822 0 4 0.688 1 0.593 2 0.663 1 ⋮ ⋮ ⋮ ⋮ 8 0.667 8 0.578 7 0.853 8 ⋮ ⋮ ⋮ ⋮ 26 0.606 8 0.680 4 0.847 5 ⋮ ⋮ ⋮ ⋮ 45 0.993 2 0.941 9 0.803 0
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表 3 热环境评价指标随机误差分析
Table 3. Random error analysis of thermal environment evaluating index
试验 $\int_{0}^{0.1}{(T-300)}\text{d}t$ CFD计算值 径向基网络预测值 |ε|/% 6 11.901 4 11.676 9 1.89 15 20.417 3 20.660 9 1.19 29 13.720 6 13.229 8 3.71 38 15.852 2 15.699 8 0.96 54 18.396 3 18.085 5 1.69
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