Design and optimization of dynamic total pressure probe structure in aero-engines
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摘要:
提出一种具有强化散热结构的短测管型总压探针方案，建立了总压探针的多物理场数值模拟模型，提出一种基于数值模拟和改进粒子群算法的总压探针结构联合优化方法，采用混合网格划分技术减少网格数量，以正态分布初始化粒子位置，同时增加种群学习因子和自适应邻域比例，提高算法的优化效率。数值仿真结果表明，数值模拟模型中网格顶点数量减少了47%，改进后优化算法迭代代数减少了37%，优化效率高，获得的总压探针结构满足温度约束，且长度短、质量轻。
Abstract:A short-tube total pressure probe with an enhanced heat transfer structure was proposed. The multi-physical numerical simulation model of the probe was established. Based on the numerical simulation and the improved Particle Swarm Algorithm, a joint approach was proposed to optimize the probe’s structure. A hybrid grid generation technology was utilized to reduce the number of meshes. The normal distribution was employed to initialize the particle’s position. The social adjustment weight and the neighbor fraction were increased to improve the optimization efficiency of the algorithm. The simulation results showed that the number of grid vertices was reduced by 47%, and the iteration of optimization was reduced by 37%; the optimization efficiency was improved. The optimized total pressure probe satisfied the temperature constraints with the short length and the light mass.
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图 4 总压探针多物理场数值模拟几何结构图（单位：mm）
Figure 4. Geometric structure of multi-physical numerical simulation model of total pressure probe （unit: mm）
图 5 多物理场模型参数耦合作用机制
Figure 5. Coupling mechanism of parameters in multi-physics numerical model
图 7 基于数值模拟和粒子群算法的联合优化方法流程
Figure 7. Process of joint optimization method of numerical calculation and PSO
图 9 总压探针内沿中心轴线的温度分布
Figure 9. Temperature distribution along the central axis inside the total pressure probe
图 10 粒子群算法的最优目标函数曲线
Figure 10. Optimal objective function of particle swarm optimization
表 1 不同飞行条件和发动机状态下的截面3和截面13的气流参数
Table 1. Flow parameters of section 3 and section 13 under different flight conditions and engine conditions
nh,cor Ma H/km Ts13/K ps13/Pa V13/（m/s） Ts3/K ps3/Pa V3/（m/s） 1.0 0 0 463.1 4.1×105 54.6 805.4 2.6×106 76.1 1.0 0.2 0 466.8 4.2×105 54.7 811.5 2.6×106 76.4 … … … … … … … … … 1.0 1.6 16 527.3 1.7×105 56.2 907.7 1.1×106 80.5 0.90 0 0 402.8 2.7×105 75.3 688.9 1.6×106 72.0 0.90 0.2 0 406.1 2.7×105 75.4 694.2 1.6×106 72.2 … … … … … … … … … 0.90 1.6 16 458.3 1.1×105 78.4 777.2 6.5×105 76.2 0.80 0 0 352.2 1.7×105 102.3 579.2 8.9×105 68.2 … … … … … … … … … 0.80 1.6 16 400.6 6.9×104 107.5 654.8 3.7×105 72.4 
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表 2 不同内外涵网格划分参数
Table 2. Grid parameters of different internal and external grid partitions
序号 网格参数 网格统计数据 Le,max/mm Le,min/mm ke,max nv/105 ne/105 nf/105 1 53 11.2 1.4 1.27 3.76 8.8 2 20.9 6.42 1.25 1.5 5.2 10 3 10.8 3.21 1.15 2.1 8.5 14
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表 3 不同总压探针网格划分参数
Table 3. Grid parameters of different total pressure probe grid partitions
序号 短测管网格参数/mm 环肋片网格参数/mm 网格统计数据 Le,max Le,min Le,max Le,min nv/105 ne/105 nf/105 4 2 1.5 0.6 0.4 0.89 2.6 6.2 5 1.5 1 0.5 0.3 0.98 2.8 6.9 6 1 0.5 0.4 0.2 1.3 3.8 8.8 7 0.5 0.3 0.3 0.1 1.8 5.2 13
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表 4 不同总压探针网格划分下的数值模拟结果
Table 4. Simulation results of different total pressure probe grid partitions
序号 Te/K ts/min 模拟结果1 模拟结果2 模拟结果3 平均值 模拟结果1 模拟结果2 模拟结果3 平均值 4 678.50 677.31 678.83 678.21 30 33 33 32 5 657.82 658.64 656.43 657.63 36 37 35 36 6 657.12 656.32 659.42 657.62 54 55 59 56 7 656.38 658.25 654.12 656.25 83 82 81 82
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表 5 改进后粒子群算法各代最优粒子参数统计
Table 5. Statistics of optimal particle parameters of each iteration of improved PSO
迭代次数 ${s_0}{\text{/mm}}$ $s{\text{/mm}}$ $\delta {\text{/mm}}$ ${d_2}{\text{/mm}}$ $n$ $ {L_{{\text{zg}}}}{\text{/mm}} $ $ m{\text{/g}} $ $ {T_{\text{e}}}{\text{/K}} $ 0 3.2 4.2 0.5 13.7 2 16.5 5.1 653.8 5 3.7 4.4 0.7 11.4 2 16.7 5.1 652.4 10 2.5 4.8 0.6 12.7 2 18.8 4.4 646.9 15 1.0 5.0 0.4 8.0 1 16.5 4.6 657.2 25 1.0 4.4 0.5 11.7 3 16.3 5.0 649.5
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