A Review of Soft Sensor Methods for Mach number Measurement at LAPAN Supersonic Wind Tunnel
Keywords:
Wind Tunnel, Supersonic, Soft Sensor, Pressure SensorAbstract
Wind tunnel is used to provide air flow to an object so that the aerodynamics characteristic of the object can be determined. The given air flow will be varying according to the object working condition. LAPAN Supersonic Wind Tunnel can operate at the speed of 1 – 3.5 Mach. The value of 1 – 3.5 Mach is determined from calculating the measurement of static pressure and stagnation pressure. Sometimes problem occurs when one of the sensor was unavailable to use, this situation can delay the ongoing test in the wind tunnel. The purpose of this paper is to review the soft sensor method to be applied as a backup for the physical sensor
Downloads
References
B. Ilić, M. Milosavljević, J. Isaković, and M. Miloš, "Stagnation Pressure Transient Control in a Supersonic Blowdown Wind Tunnel Test Facility," Materials Today: Proceedings, vol. 3, pp. 987-992, // 2016.
E. Krause, Fluid Mechanics: With Problems and Solutions, and an Aerodynamics Laboratory: Springer, 2005.
A. N. Shahrbabaki, M. Bazazzadeh, A. Shahriari, and M. D. Manshadi, "Intelligent Controller Design for a Blowdown Supersonic Wind Tunnel," International Journal of Control and Automation, vol. 7, 2014.
Jefri Abner H., Prawito, P., Agus Aribowo, A Labview Based Optimization of Supersonic Wind Tunnel Instrumentation Systems, Indonesian Journal of Electrical Engineering and Informatics, Vol. 8 No.2, pp. 353-363, 2020.
L. Fortuna, S. Graziani, A. Rizzo, and M. G. Xibilia, Soft Sensors for Monitoring and Control of Industrial Processes: Springer London, 2007.
B. O. S. Teixeira, W. S. Castro, A. F. Teixeira, and L. A. Aguirre, "Data-driven soft sensor of downhole pressure for a gas-lift oil well," Control Engineering Practice, vol. 22, pp. 34-43, 1// 2014
S. Shokri, M. A. Marvast, M. T. Sadeghi, and S. Narasimhan, "Combination of data rectification techniques and soft sensor model for robust prediction of sulfur content in HDS process," Journal of the Taiwan Institute of Chemical Engineers, vol. 58, pp. 117-126, 1// 2016
Y. G. Li, W. H. Gui, C. H. Yang, and Y. F. Xie, "Soft sensor and expert control for blending and digestion process in alumina metallurgical industry," Journal of Process Control, vol. 23, pp. 1012-1021, 8// 2013.
J. G. Webster and H. Eren, Measurement, Instrumentation, and Sensors Handbook, Second Edition: Electromagnetic, Optical, Radiation, Chemical, and Biomedical Measurement: Taylor & Francis, 2014.
C. Shang, F. Yang, D. Huang, and W. Lyu, "Data-driven soft sensor development based on deep learning technique," Journal of Process Control, vol. 24, pp. 223-233, 3// 2014.
F. A. A. Souza, R. Araújo, and J. Mendes, "Review of soft sensor methods for regression applications," Chemometrics and Intelligent Laboratory Systems, vol. 152, pp. 69-79, 3/15/ 2016.
Y. Wu and X. Luo, "A novel calibration approach of soft sensor based on multirate data fusion technology," Journal of Process Control, vol. 20, pp. 1252-1260, 12// 2010.
P. Kadlec and B. Gabrys, "Local learning-based adaptive soft sensor for catalyst activation prediction," AIChE Journal, vol. 57, pp. 1288-1301, 2011.
H. J. Galicia, Q. P. He, and J. Wang, "Adaptive Outlier Detection and Classification for Online Soft Sensor Update," IFAC Proceedings Volumes, vol. 45, pp. 402-407, // 2012.
P. Kadlec, B. Gabrys, and S. Strandt, "Data-driven Soft Sensors in the process industry," Computers & Chemical Engineering, vol. 33, pp. 795-814, 4/21/ 2009.
Ya Huang, Neil Ferguson, Principal component analysis of the cross-axis apparent mass nonlinearity during whole-body vibration, Mechanical systems and signal processing, 2020.
Anjali Krishnan, L. J. Williams, Anthony R. McIntosh, Herve Abdi, Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression, Journal Neuroimage, 2010.
Alberto Munoz, Javier M. Moguerza, Gabriel Martos, Support Vector Machines, Wiley Statsref: Statistics Reference Online, 2019.
Uma K., M. Hanumanthapa, Data Collection Methods and Data Pre-processing Techniques for Healthcare Data Using Data Mining, International Journal of Scientific & Engineering Research, Vol. 8 Issue 6, 2017.
Serdar Sahin, Antonio M. Cipriano, et.al, Iterative Decision Feedback Equalization Using Online Prediction, IEEE Access, 2019.
