Multimode Wind Tunnel Flow Field System Monitoring Based on KPLS
Abstract
:1. Introduction
2. Methodology
2.1. Mean Matrix KPLS Regression Model
- Initialize u randomly.
- .
- .
- .
- Repeat steps 2–5 until convergence.
- .
- Return to 2, until all latent variables are calculated.
2.2. Mach Number Monitoring Based on KPLS
3. Illustration and Discussion
3.1. Wind Tunnel System
3.2. Mach Number Prediction Based on KPLS Model
- A.
- Single-mode prediction
- B.
- Multimode prediction
3.3. Mach Number Monitoring
- A.
- Single-mode monitoring
- B.
- Multimode monitoring
- C.
- Fault monitoring
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Process Variable | Unit |
---|---|---|
1 | Total pressure | Bar |
2 | Stable static pressure | Bar |
3 | Test static pressure | Bar |
4 | Outlet static pressure | Bar |
5 | Temperature | °C |
6 | Humidity | % |
7 | Test section flow | m3/s |
8 | Attack angle | ° |
9 | Speed | m/s |
10 | Blade angle | ° |
11 | Main output pressure | Bar |
12 | Main inlet pressure | Bar |
13 | Main output temperature | °C |
14 | Main inlet temperature | °C |
15 | Auxiliary angle | ° |
16 | Auxiliary inlet pressure | Bar |
17 | Auxiliary output temperature | °C |
18 | Mass flow of auxiliary outlet | m3/s |
19 | Mass flow of auxiliary inlet | m3/s |
20 | Auxiliary gas flow | m3/s |
Data Blocks | Mach Number | Speed | Initial Attack Angle | Target Attack Angle | Attack Angle Step | Mode |
---|---|---|---|---|---|---|
1 | 0.7813 | 1800 | −4 | −2 | 2 | Mode 1 |
2 | 0.7831 | 1800 | −2 | 0 | 2 | |
3 | 0.7803 | 1800 | 0 | 2 | 2 | |
4 | 0.7726 | 1800 | 2 | 4 | 2 | |
5 | 0.7684 | 1800 | 4 | 5 | 1 | Mode 2 |
6 | 0.7647 | 1800 | 5 | 6 | 1 | |
7 | 0.7613 | 1800 | 6 | 7 | 1 | |
8 | 0.7581 | 1800 | 7 | 8 | 1 | |
9 | 0.7539 | 1800 | 8 | 9 | 1 |
Cases | Mean Matrix PLS Model | Mean Matrix KPLS Model |
---|---|---|
Test data 1 using the single-mode model | 2.730 × 10−3 | 0.204 × 10−3 |
Test data 2 using the single-mode model | 1.781 × 10−3 | 0.251 × 10−3 |
Test data 1 using the multimode model | 2.595 × 10−3 | 0.254 × 10−3 |
Test data 2 using the multimode model | 2.878 × 10−3 | 0.357 × 10−3 |
Cases | Mean Matrix PLS Model | Mean Matrix KPLS Model |
---|---|---|
Test data 1 using the single-mode model | 0.3938 | 0.9921 |
Test data 2 using the single-mode model | 0.4123 | 0.9994 |
Test data 1 using the multimode model | 0.4425 | 0.9978 |
Test data 2 using the multimode model | 0.4425 | 0.9961 |
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Guo, J.; Zhang, R.; Cui, X.; Ma, W.; Zhao, L. Multimode Wind Tunnel Flow Field System Monitoring Based on KPLS. Processes 2023, 11, 178. https://doi.org/10.3390/pr11010178
Guo J, Zhang R, Cui X, Ma W, Zhao L. Multimode Wind Tunnel Flow Field System Monitoring Based on KPLS. Processes. 2023; 11(1):178. https://doi.org/10.3390/pr11010178
Chicago/Turabian StyleGuo, Jin, Ran Zhang, Xiaochun Cui, Weitong Ma, and Luping Zhao. 2023. "Multimode Wind Tunnel Flow Field System Monitoring Based on KPLS" Processes 11, no. 1: 178. https://doi.org/10.3390/pr11010178
APA StyleGuo, J., Zhang, R., Cui, X., Ma, W., & Zhao, L. (2023). Multimode Wind Tunnel Flow Field System Monitoring Based on KPLS. Processes, 11(1), 178. https://doi.org/10.3390/pr11010178