基于DAC-HOM的航空发动机部件特性分区协同修正

贾宝惠; 朱梓榆; 薛鹏

doi:10.13224/j.cnki.jasp.20250583

基于DAC-HOM的航空发动机部件特性分区协同修正

doi: 10.13224/j.cnki.jasp.20250583

贾宝惠^1,,
朱梓榆¹,
薛鹏^2,*, ,

1.
中国民航大学航空工程学院，天津 300300
2.
中国民航大学工程技术训练中心，天津 300300

基金项目: 国家自然科学基金（U2033209）；中央高校基本科研业务费专项（KJZ53420240023）

详细信息

作者简介:
贾宝惠（1971-），女，教授、博士生导师，硕士，研究领域为民用航空器维修工程与持续安全性分析。E-mail：jiabaohui@sina.com

通讯作者:
薛鹏（1982-），男，讲师，硕士，研究领域为航空发动机故障诊断与损伤修复。E-mail：pxue@cauc.edu.cn

中图分类号: V235.13
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出版历程
- 收稿日期: 2025-12-13
- 网络出版日期: 2026-05-06

Correction of aero-engine component maps based on DAC-HOM with partitioned collaborative strategy

Jia Baohui^1
,,
Zhu Ziyu¹,
Xue Peng^{2,*
, ,}

1.
School of Aeronautical Engineering，Civil Aviation University of China，Tianjin 300300，China
2.
The Engineering Technology Training Center，Civil Aviation University of China，Tianjin 300300，China

摘要

摘要:
针对航空发动机部件特性图修正中的局部适应性与全域一致性难以兼顾的问题，提出一种动态自适应混合优化模型（DAC-HOM）分区协同修正框架。该方法以内嵌的DAC-HOM作为统一修正内核，由“转速-工况分布”双自由度表征的Bernstein基自适应曲面与协方差自适应多臂赌博机增强河马优化算法（CABE-hippo）耦合构成，在确保特性曲面光滑可控的同时，实现自适应寻优与动态边界调节。在此基础上，引入面向双源异构数据的分区协同修正策略，分别针对地面试车工况与基于快速存取记录器（QAR）飞行数据的高空工况实施两步独立修正后进行融合，获得全局一致的特性图。与传统先初修再分区精修的递进修正方法相比，其有效抑制了局部误差向全域传播。算例结果表明：该框架将全域平均误差降至2.61%，排气温度、高压转子转速和燃油流量误差分别降低了83.6%、16.5%和46.8%，显著提升了航空发动机数学模型非设计点性能匹配精度。
- 数学模型 /
- 部件特性 /
- 混合优化模型 /
- 分区协同修正 /
- 非设计点性能匹配
Abstract:
To address the conflict between local adaptability and global consistency in aero-engine component map correction, a dynamic adaptive correction-hybrid optimization model (DAC-HOM) partition-based collaborative correction framework was proposed. The framework employed an embedded DAC-HOM as the unified correction kernel. By coupling this kernel with a Bernstein-basis adaptive surface characterized by dual degrees of freedom in rotational speed and operating-point distribution and a covariance-adaptive bandit-enhanced hippopotamus optimization algorithm (CABE-hippo), the framework enabled adaptive optimization and dynamic boundary adjustment for smooth characteristic surfaces. Building on this, a partition-based collaborative correction strategy for dual-source heterogeneous data was introduced, in which ground test conditions and high-altitude operating conditions represented by quick access recorder (QAR) flight data were corrected separately and then fused to obtain a globally consistent characteristic map. Compared with traditional progressive correction approaches, the proposed method effectively suppressed the propagation of local errors into the global region. Numerical results showed that the framework reduced the overall mean error to 2.61%, and decreased exhaust gas temperature, high-pressure rotor speed and fuel flow errors by 83.6%, 16.5%, and 46.8%, respectively. The proposed framework significantly improved the performance matching accuracy of aero-engine mathematical models at off-design points.
- mathematical model /
- component characteristics /
- hybrid optimization model /
- partitioned collaborative correction /
- off-design performance matching

HTML

图 1 分区协同修正框架

Figure 1. Partitioned collaborative correction framework

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图 2 部件特性示意图

Figure 2. Component map schematic

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图 3 R_line重参数化

Figure 3. R_line reparameterization

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图 4 优化算法流程对比

Figure 4. Comparison of optimization algorithm flows

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图 5 试车与QAR数据分布示意图

Figure 5. Engine test and QAR data distribution schematic

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图 6 分区协同修正流程

Figure 6. Partitioned collaborative correction flow

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图 7 分排涡扇发动机气路系统结构图

Figure 7. Separate-exhaust turbofan engine gas system structure diagram

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图 8 试车数据修正结果

Figure 8. Test rig data correction results

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图 9 适应度进化曲线

Figure 9. Evolution of the fitness curve

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图 10 优化算法误差对比

Figure 10. Error comparison of optimization algorithms

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图 11 消融试验适应度进化曲线

Figure 11. Fitness evolution curve of ablation studies

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图 12 QAR训练集修正结果

Figure 12. QAR training set correction results

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图 13 Bernstein基自适应因子曲面

Figure 13. Adaptive factor surface based on Bernstein basis

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图 14 部件特性图修正前后对比

Figure 14. Comparison of compressor characteristic map before and after correction

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图 15 验证集修正前后误差分布图

Figure 15. Error distributions on the validation set before and after correction

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图 16 测试集关键参数对比结果

Figure 16. Comparison results of key parameters on the test set

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表 1 全域边界

Table 1. Global boundary

部件	系数	W	PR	η
风扇	u	2.2857	3.43	1.714
	u²	1.14	2.286	0.57
	v	0.306	0.051	0.102
低压压气机	u	2.28	2.143	1.714
	u²	1.53	1.02	0.255
	v	0.625	0.3125	0.104
高压压气机	u	0.99	0.48	0.467
	u²	0.49	0.25	0.24
	v	0.2083	0.05	0.0417

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表 2 超参数设置

Table 2. Hyperparameter settings

机制	参数	符号	数值（阈值）
CMA^[24]	初始步长	$ {\sigma }_{0} $	0.25·\|\|U−L\|\|₂
CMA^[24]	种群规模	N	$ 4+\left\lfloor 3\cdot \ln\; n\right\rfloor $
Bandit^[25]	探索率	$ \in $	[0.15, 1]
Bandit^[25]	步长范围	$ \eta ,\sigma $	[0.01, 0.2]
Cosine^[26]	搜索强度边界	$ {\boldsymbol{S}}_{\min },{\boldsymbol{S}}_{\max } $	[0.01, 1]

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表 3 设计点参数

Table 3. Design point parameters

参数	数值
高度/m	0
马赫数	0
燃烧室出口温度/K	1639
燃烧室燃烧效率	0.997
高压压气机压比	12
进气道总压恢复系数	0.992
风扇效率	0.89
增压级效率	0.85
涡轮效率	0.88
高压涡轮导向器冷却气比例（V_CHN）	0.06
低压涡轮导向器冷却气比例（V_CLN）	0.02

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表 4 设计点性能匹配结果

Table 4. Design point performance matching results

参数	目标值	仿真值	误差/%
进口换算流量/（kg/s）	355.17	355.262	0.026
排气温度（EGT）/K	1073	1070	0.270
推力（F_n）/kN	117	117.30	0.250
起飞燃油流量W_f/（kg/s）	1.24	1.243	0.240
低压转子转速N₁/（r/min）	5175	5175	0
高压转子转速N₂/（r/min）	14460	14460.4	0.10
涵道比（BPR）	5.1	5.107	0.130
油气比（FAR）	0.024	0.02457	0.300
整机压比（p₃/p₂）	32.8	32.740	0.182

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表 5 试车数据

Table 5. Test rig data

参数	试车工况
参数	1	2	3	4
N₁/（r/min）	5191	5148	5010	4909
N₂/（r/min）	14599	14565	14457	14387
EGT/K	1117.65	1108.43	1080.40	1059.15
W_f/（kg/s）	1.29	1.26	1.16	1.09
F_n/kN	114.69	113.02	106.31	100.52
p₁₇/kPa	174.62	173.51	169.27	165.44
p₂₅/kPa	241.33	240.28	233.30	225.73
p₃/kPa	408.526	407.439	384.172	364.057
p₅/kPa	163.29	161.59	155.81	151.53
T₂₅/K	399.35	397.15	392.25	389.65
T₃/K	840.05	835.15	821.95	812.25
T₅/K	875.15	868.15	848.05	833.05

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表 6 不同优化算法性能对比

Table 6. Performance comparison of different optimization algorithms

对比维度	CABE-hippo	PSO	GA	HBA	SAO
收敛速度	最快，10$ \pm $5次迭代	初期快，6次停滞	慢，需30+次迭代	中等，20～30次	较慢
最终适应度值	0.014（全局最优）	0.016（局部最优）	0.019（未完全收敛）	0.015（次优）	0.020（最差）
稳定性（D_std）	0.0008	0.0025	0.0030	0.0020	0.0056
全局搜索能力	强（后期持续改进）	较弱（早熟收敛）	依赖遗传操作（局部开发弱）	中等（20次后改进放缓）	差（易陷局部极值）

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表 7 CABE-hippo消融试验性能对比

Table 7. Performance comparison of CABE-hlippo in ablation studies

对比维度	CABE-hippo	w/o CMA	w/o Bandit	w/o Cosine
收敛迭代次数（均值）	4	36	21	7
最终适应度值	0.0140	0.0165	0.0201	0.0148
稳定性（D_std）	0.0008	0.0011	0.0015	0.0024
试车平均误差/%	0.625	0.647	0.758	0.639

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表 8 DBSCAN分区参数

Table 8. DBSCAN partition parameters

参数名称	设置	扰动范围
聚类特征	N_c-R_line
特征归一化方式	Min-Max 归一化
距离度量	欧式距离
邻域半径	0.032	0.029～0.035
核心点最小样本数	10	8～12

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表 9 QAR训练集基准值

Table 9. QAR training set baseline values

工况	N_1/%	N₂/%	EGT/%	W_f/%	p₃/%	T₃/%	区域
1	60.88	84.25	59.18	12.36	25.02	66.67	L1
2	62.87	82.63	56.20	12.85	23.79	67.24	L1
3	81.63	93.13	72.69	29.60	47.54	80.65	L2
4	80.01	92.38	70.92	27.97	45.34	79.66	L2
5	85.38	94.63	76.44	34.32	52.30	82.86	M1
6	84.75	94.25	74.32	33.50	51.43	82.44	M1
7	83.38	93.75	71.61	31.71	49.64	81.64	M1
8	84.75	93.13	73.84	29.11	44.41	81.57	M1
9	86.75	92.88	76.61	25.70	37.49	80.23	M2
10	86.88	93.38	76.89	26.84	39.74	80.43	M2
11	86.75	93.13	77.17	26.35	39.29	80.22	M2
12	87.13	92.38	76.24	26.35	37.54	80.28	M2
13	92.63	96.25	82.20	66.19	85.93	85.42	H1
14	93.38	96.75	82.39	67.98	86.23	85.50	H1
15	96.63	98.63	80.52	52.69	61.26	89.23	H2
16	97.25	99.03	81.17	58.22	65.92	89.42	H2

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表 10 QAR训练集修正误差对比

Table 10. Comparison of QAR training set correction errors %

方法	N₂		EGT		W_f		p₃		T₃
方法	$ {E}_{\text{avg}} $	$ {E}_{\text{max}} $	$ {E}_{\text{avg}} $	$ {E}_{\text{max}} $	$ {E}_{\text{avg}} $	$ {E}_{\text{max}} $	$ {E}_{\text{avg}} $	$ {E}_{\text{max}} $	$ {E}_{\text{avg}} $	$ {E}_{\text{max}} $
1	4.710	6.303	4.210	8.490	7.394	16.457	10.274	21.981	7.518	20.690
2	4.475	5.830	4.180	9.100	6.550	15.176	3.144	12.799	1.282	3.933
3	4.350	8.370	10.900	25.902	10.530	22.130	6.808	24.150	2.077	6.603
4	1.140	3.454	1.290	3.335	2.330	4.530	1.616	3.553	1.078	1.473

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表 11 修正方法综合性能对比分析

Table 11. Comprehensive performance comparative analysis of correction methods %

方法	M区（重叠域）		H区（外推区）		全域性能
方法	E_avg	D_std	E_RMSE	I_CEPI	E_avg	E_max	阈值通过率（误差阈值小于<5%）	鲁棒性（D_std）
1	6.39	1.28	10.55	9.64	6.83	25.90	65.9	4.77
2	4.93	0.74	7.80	6.97	6.55	15.47	81.2	4.12
3	1.56	0.48	14.88	13.92	10.53	21.98	43.1	7.31
4	1.64	0.25	2.18		2.61	5.70	92.6	1.29

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表 12 目标搜索边界

Table 12. Objective search bounds

部件	系数	W	PR	$ \eta $
风扇（FAN）	β_FAN,0	1.1429	1.7150	0.8570
	β_FAN,1	0.9143	1.3720	0.5700
	β_FAN,2	2.2829	4.0010	1.4270
	δ_FAN	0.9804	5.8824	2.9412
低压压气机（LPC）	β_LPC,0	1.1400	0.8570	1.0715
	β_LPC,1	0.9120	0.8572	0.6856
	β_LPC,2	2.670	2.0915	1.1120
	δ_LPC	0.480	0.9600	2.8846
高压压气机（HPC）	β_HPC,0	1.4402	6.001	7.1942
	β_HPC,1	0.3960	0.1920	0.1868
	β_HPC,2	0.9850	0.4900	0.4735
	δ_HPC	0.4950	0.2400	0.2335

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表 13 验证集样本

Table 13. Validation set samples

区域	样本N₁ 范围/%	特性图N_c 分区区间	样本N_c 范围	样本量
L1	[50.02, 66.75]	[0.50, 0.74]	[0.60, 0.74]	240
L2	[66.75, 81.83]	[0.74, 0.90]	[0.74, 0.89]	372
M1	[81.83, 84.20]	[0.90, 0.93]	[0.91, 0.93]	481
M2	[84.20, 89.75]	[0.93, 0.99]	[0.93, 0.99]	959
H1	[89.75, 92.60]	[0.99, 1.03]	[0.99, 1.03]	156
H2	[92.60, 100.25]	[1.03, 1.22]	[1.03, 1.10]	311

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表 14 验证集误差对比

Table 14. Comparison of validation set error %

区域	指标	E_avg		E_max
区域	指标	修前	修后	修前	修后
L	W_f	12.17	3.512	29.94	4.0177
	EGT	20.64	4.214	29.92	4.49
	N₂	7.39	2.76	27.28	3.0167
M	W_f	14.10	1.146	23.60	1.61
	EGT	23.59	2.046	29.95	2.213
	N₂	5.57	0.8976	8.465	0.98
H	W_f	10.76	3.622	20.95	4.43
	EGT	14.2	4.930	29.17	5.24
	N₂	7.68	4.2490	11.51	6.092

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