基于Kriging算法的压气机健康参数主从式插值建模

曾力; 陈仁祥; 董绍江

doi:10.3969/j.issn.0258-2724.20191118

基于Kriging算法的压气机健康参数主从式插值建模

doi: 10.3969/j.issn.0258-2724.20191118

基金项目: 国家自然科学基金面上项目（51975079）

详细信息

作者简介:
曾力（1982—），男，讲师，研究方向为燃气轮机健康状态监测及故障诊断，E-mail：zengli_sichuan@163.com

中图分类号: V221.3
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- 文章访问数: 389
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- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2019-11-26
- 修回日期: 2020-06-30
- 网络出版日期: 2021-08-13
- 刊出日期: 2021-10-15

Master-Slave Interpolation Modeling of Compressor Healthy Parameters Based on Kriging Algorithm

摘要

摘要: 针对航空燃气轮机压气机数字化建模过程中由于缺少压气机流量系数导致模型精度偏低的问题，基于Kriging插值算法构造了面向压气机流量系数估计的主从式建模方法，分析了高维空间下对应于不同压气机换算转速的流量系数分布特征；基于流量系数的特征提取方法探索了流量系数、换算转速、增压比之间的映射关系，并提出了关于这三类参数的多维样本向量构造方法；基于Kriging算法建立了适用于燃气轮机过渡工况下压气机流量系数主从式插值模型. 研究结果表明：与传统的Kriging插值方法及牛顿插值法相比，基于主从式模型的流量系数计算结果更接近实际值，计算精度提高了近10%；主模型可输出流量系数的估值向量，插值效率相比传统Kriging模型提高了近15%.
- 压气机 /
- 流量系数 /
- Kriging插值 /
- 主从建模
Abstract: In order to solve the problem that the accuracy of the model is low due to the lack of compressor flow coefficient in the process of digital modeling of gas turbine compressor, a master-slave interpolation model for flow coefficient is constructed based on the Kriging interpolation algorithm. The distribution characteristics of flow coefficient in high-dimensional space are studied. The mapping relationship among flow coefficient, conversion speed and pressure ratio are explored, and a high-dimensional sample construction method for these three parameters is proposed. A high-precision flow coefficient master-slave interpolation model of gas turbine under transient conditions is established based on the Kriging algorithm. The results show that compared with the traditional Kriging interpolation method and the Newton interpolation method, the calculation result of flow coefficient based on master-slave model is closer to the actual value, and the calculation accuracy is improved by nearly 10%. In addition, the master model can output the estimated vector. Compared with the traditional Kriging model, the interpolation efficiency is improved by nearly 15%.
- compressor /
- flow coefficient /
- Kriging algorithm /
- master-slave mode

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图 1 传统Kriging插值估计法

Figure 1. Traditional Kriging interpolation estimation method

下载: 全尺寸图片幻灯片

图 2 主从式Kriging插值估计法

Figure 2. Master-slave Kriging interpolation estimation method

下载: 全尺寸图片幻灯片

图 3 基于主从式模型的流量系数向量插值

Figure 3. Calculation of flow coefficients vector based on the Master-slave model

下载: 全尺寸图片幻灯片

图 4 基于主从式模型的流量系数单点插值

Figure 4. Flow coefficient calculation of single-points based on the Master-slave model

下载: 全尺寸图片幻灯片

表 1 待估计流量系数

Table 1. Flow coefficients to be estimated

${ {\overline n} _{\rm{des} } }$	${\overline \pi } _{1\_{\rm{des} } }$	${\overline \pi } _{2\_{\rm{des} } }$	${\overline \pi } _{3\_{\rm{des} } }$	${\overline \pi } _{4\_{\rm{des} } }$	${\overline \pi } _{5\_{\rm{des} } }$	${\overline \pi } _{6\_{\rm{des} } }$	${\overline \pi } _{7\_{\rm{des} } }$	${\overline \pi } _{8\_{\rm{des} } }$	${\overline \pi } _{9\_{\rm{des} } }$	${\overline \pi } _{10\_{\rm{des} } }$
0.3315	0.600 0	0.593 0	0.586 0	0.577 0	0.5675	0.555 0	0.545 0	0.534 9	0.525 9	0.516 0
0.8478	0.4125	0.409 0	1.390 0	1.3583	1.3178	1.260 0	1.205 0	1.150 0	1.100 0	1.045 0
1.1268	1.620 0	1.577 0	1.532 5	1.485 0	1.4375	1.389 5	1.332 5	1.280 0	1.225 5	1.170 0

下载: 导出CSV

表 2 流量系数精度比较

Table 2. Accuracy comparison of flow coefficients

换算转速	传统 Kriging 算法	主从插值模型	流量系数实际值
0.331 5	0.302 7	0.296 3	0.296 3
	0.333 5	0.343 5	0.336 3
	0.359 5	0.372 2	0.369 8
	0.386 7	0.396 1	0.395 8
	0.408 8	0.421 8	0.413 9
	0.430 0	0.438 8	0.433 3
	0.442 1	0.451 2	0.450 0
	0.451 9	0.457 2	0.458 3
	0.459 7	0.462 1	0.466 7
	0.468 6	0.469 0	0.472 2
0.847 8	1.146 7	1.140 6	1.144 4
	1.150 6	1.181 0	1.177 8
	1.171 1	1.203 0	1.205 6
	1.201 6	1.223 5	1.227 8
	1.232 2	1.239 4	1.240 0
	1.257 6	1.248 5	1.244 4
	1.263 9	1.254 1	1.247 8
	1.259 8	1.254 6	1.250 0
	1.254 5	1.254 6	1.250 6
	1.253 1	1.256 6	1.251 1
1.126 8	1.365 3	1.355 1	1.355 6
	1.3593	1.3551	1.3556
	1.3614	1.3552	1.3556
	1.3646	1.3552	1.3556
	1.3648	1.3553	1.3556
	1.3620	1.3553	1.3556
	1.3589	1.3553	1.3556
	1.3595	1.3553	1.3556
	1.3622	1.3553	1.3556
	1.3575	1.3553	1.3556

下载: 导出CSV

表 3 流量系数目标点估值

Table 3. Estimation of target point of flow coefficients

换算转速	牛顿插值	传统 Kriging 算法	主从插值模型	流量系数实际值
0.218 2	0.357 6	0.356 7	0.379 3	0.385 2
0.901 8	1.069 8	1.062 3	1.108 7	1.096 7
1.062 0	1.312 7	1.292 8	1.332 5	1.348 9

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