LAPSE:2025.0214v1
Published Article
LAPSE:2025.0214v1
Dynamic analysis for prediction of flow patterns in an oscillatory baffled reactor using machine learning
June 27, 2025
Abstract
In the present paper, we come up with application of machine learning using data for flow visualization as a method for predicting unsteady flow patterns in oscillatory baffled reactors (OBRs). Application of the proper orthogonal decomposition (POD) is investigated for dynamic analysis of spatio-temporal data acquired by particle image velocimetry (PIV) to determine inputs and outputs for neural network model. It has demonstrated that three sets of modes and time-varying mode coefficients extracted by the POD could be useful for dynamic analysis and prediction of time-variant flow patterns in OBR. Also it is shown that decomposition of the time-series data for the mode coefficients by Fourier series expansion was effective for deriving reduced order model.
In the present paper, we come up with application of machine learning using data for flow visualization as a method for predicting unsteady flow patterns in oscillatory baffled reactors (OBRs). Application of the proper orthogonal decomposition (POD) is investigated for dynamic analysis of spatio-temporal data acquired by particle image velocimetry (PIV) to determine inputs and outputs for neural network model. It has demonstrated that three sets of modes and time-varying mode coefficients extracted by the POD could be useful for dynamic analysis and prediction of time-variant flow patterns in OBR. Also it is shown that decomposition of the time-series data for the mode coefficients by Fourier series expansion was effective for deriving reduced order model.
Record ID
Keywords
Neural network model, Oscillatory baffled reactor, Proper orthogonal decomposition
Suggested Citation
Matsumoto H, Kambayashi Y, Yoshikawa S, Ookawara S. Dynamic analysis for prediction of flow patterns in an oscillatory baffled reactor using machine learning. Systems and Control Transactions 4:394-398 (2025) https://doi.org/10.69997/sct.101931
Author Affiliations
Matsumoto H: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan
Kambayashi Y: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan
Yoshikawa S: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan
Ookawara S: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan; Yasuda Womens University, Department of Aesthetic Design and Technology, Hiroshima, Japan
Kambayashi Y: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan
Yoshikawa S: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan
Ookawara S: Institute of Science Tokyo, Department of Chemical Science and Engineering, Tokyo, Japan; Yasuda Womens University, Department of Aesthetic Design and Technology, Hiroshima, Japan
Journal Name
Systems and Control Transactions
Volume
4
First Page
394
Last Page
398
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 0394-0398-1729-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0214v1
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https://doi.org/10.69997/sct.101931
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Jun 27, 2025
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References Cited
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