考虑高压涡轮性能非确定的航空发动机鲁棒设计

王林; 潘旭; 杨锟

doi:10.13224/j.cnki.jasp.20230368

考虑高压涡轮性能非确定的航空发动机鲁棒设计

doi: 10.13224/j.cnki.jasp.20230368

中国航发商用航空发动机有限责任公司上海商用飞机发动机工程技术研究中心，上海 200241

基金项目: 国家科技重大专项（HT-J2019-Ⅰ-0006-0006）

详细信息

作者简介:
王林（1991-），男，工程师，博士，主要从事航空发动机优化设计研究。E-mail：wanglin1876@163.com

中图分类号: V235.1
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- 文章访问数: 90
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- 被引次数: 0
出版历程
- 收稿日期: 2023-06-03
- 网络出版日期: 2023-12-05

Robust optimization in aero-engine design considering uncertainty of high-pressure turbine performance

Shanghai Engineering Research Center of Civil Aero Engine，Commercial Aircraft Engine Company Limited，Aero Engine Corporation of China，Shanghai 200241，China

摘要

摘要:
为了降低制造过程中非确定性因素对航空发动机总体性能的影响，通过在设计阶段考虑高压涡轮性能的非确定性，构建鲁棒优化设计模型来优化总体性能的设计方案，使用蒙特卡洛仿真量化非确定性的影响，并开发了相应的全局优化算法进行求解。数值实验的结果验证了鲁棒设计模型的优势，与传统确定性设计方法相比，在高压涡轮性能非确定的情况下，鲁棒设计模型获得的方案能平均减少15.97%的总体性能离散程度，具有较优鲁棒性。
- 总体性能 /
- 非确定性 /
- 鲁棒设计 /
- 蒙特卡洛仿真 /
- 最优化
Abstract:
In order to reduce the impact of uncertain factors in manufacturing on the overall performance of aero-engines, a robust design model was constructed by considering the uncertainty of high-pressure turbine performance in the design stage. Monte Carlo simulation was used in this model to quantify the effect of uncertainty. A global optimization algorithm was developed to solve the robust design model. Numerical results validated the advantages of the robust design model. Its optimal solution can reduce the variability of the engine performance by an average of 15.97%, which showed better robustness in uncertain environment than the solution obtained by generic deterministic design method.
- engine performance /
- uncertainty /
- robust design /
- Monte Carlo simulation /
- optimization

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图 1 换算流量偏差的概率密度图

Figure 1. Probability density distribution of converted flow deviation

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图 2 效率偏差的概率密度图

Figure 2. Probability density distribution of efficiency deviation

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图 3 鲁棒优化设计方法架构图

Figure 3. Flowchart of robust optimization design method

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图 4 累计均值与相对偏差的变化趋势图

Figure 4. Cumulative value of mean and relative deviation

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图 5 优化算法的全局收敛性对比

Figure 5. Convergence comparison of different algorithms

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图 6 不同涡轮性能参数偏差对加权耗油率的影响

Figure 6. Influence of different turbine performance parameter deviations on total fuel consumption rate

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图 7 不同涡轮换算流量偏差对加权耗油率的影响

Figure 7. Influence of different converted flow deviations on total fuel consumption rate

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图 8 不同涡轮效率偏差对加权耗油率的影响

Figure 8. Influence of different efficiency deviations on total fuel consumption rate

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图 9 不同性能参数偏差下目标函数均值与标准差对比

Figure 9. Comparison results under different performance parameter deviations

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图 10 不同换算流量偏差下目标函数均值与标准差对比

Figure 10. Comparison results under different converted flow deviations

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图 11 不同效率偏差下目标函数均值与标准差对比

Figure 11. Comparison results under different efficiency deviations

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表 1 循环参数设计范围

Table 1. Value range of design parameters

循环参数	下界	上界
$ {\pi }_{\mathrm{O}\mathrm{F}} $	1	2
$ {\pi }_{\mathrm{H}\mathrm{C}} $	16	24
$ {T}_{4}/\mathrm{K} $	1600	2000
$ B $	6	12
$ {W}_{02}/ （\mathrm{k}\mathrm{g}/\mathrm{s}） $	150	400

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表 2 各典型工况的输入参数与约束

Table 2. Input parameters and constraints for typical working conditions

参数	典型工况
参数	经济巡航	高温起飞	最大爬升
高度/m	10668	0	10668
温度/K	0.785	0	0.785
马赫数	0	15	10
相对湿度/%	0	0	0
功率提取/kW	118.49	121.92	103.28
推力要求/kN	≥22.3636	≥133.3584	≥27.734
耗油率权重	0.62	0.04	0.34

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表 3 改进麻雀搜索算法参数设置

Table 3. Parameters of the proposed algorithm

算法参数	取值
麻雀数量	50
最大迭代次数	100
发现者占比	0.4
预警者占比	0.2
预警值	0.55
$ {w}_{i} $取值范围	1～3

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表 4 优化得到的最优设计方案

Table 4. Optimal design results obtained by the algorithm

循环参数	最优值
$ {\pi }_{\mathrm{O}\mathrm{F}} $	1.61047
$ {\pi }_{\mathrm{H}\mathrm{C}} $	23.7566
$ {T}_{4}/\mathrm{K} $	1902.76
$ B $	10.8741
$ {W}_{02}/ （\mathrm{k}\mathrm{g}/\mathrm{s}） $	212.824

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表 5 最优方案的耗油率统计分析

Table 5. Statistical analysis of SFC for the optimal solution

参数	耗油率/（g/（kN·s））
参数	经济巡航	高温起飞	最大爬升	加权和
均值	15.1350	6.8922	15.4819	15.0204
标准差	0.0428	0.0334	0.0536	0.0491

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表 6 高压涡轮性能非确定性参数偏差上下界取值

Table 6. Value range of uncertain parameters deviation %

参数	案例序号
参数	1	2	3	4	5
系数$ \mu $	−50	−25	0	25	50
效率偏差上界	0.5	0.75	1	1.25	1.5
效率偏差下界	−1	−1.5	−2	−2.5	−3
换算流量偏差上界	1.5	2.25	3	3.75	4.5
换算流量偏差下界	−2	−3	−4	−5	−6

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表 7 M2方法得到的最优设计方案

Table 7. Optimal design results obtained by M2

循环参数	最优值
$ {\pi }_{\mathrm{O}\mathrm{F}} $	1.5995
$ {\pi }_{\mathrm{H}\mathrm{C}} $	21.4379
$ {T}_{4}/\mathrm{K} $	1868.95
$ B $	10.2856
$ {W}_{02}/ （\mathrm{k}\mathrm{g}/\mathrm{s}） $	209.63

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