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Part 1: Document Description
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Citation |
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Title: |
CFD-DEM and machine learning for modelling of plugging in pipes with cohesive particles |
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Identification Number: |
doi:10.18710/ARTNTB |
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Distributor: |
DataverseNO |
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Date of Distribution: |
2024-05-03 |
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Version: |
1 |
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Bibliographic Citation: |
Balakin, Boris; Saparbayeva, Nazerke, 2024, "CFD-DEM and machine learning for modelling of plugging in pipes with cohesive particles", https://doi.org/10.18710/ARTNTB, DataverseNO, V1 |
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Citation |
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Title: |
CFD-DEM and machine learning for modelling of plugging in pipes with cohesive particles |
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Identification Number: |
doi:10.18710/ARTNTB |
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Authoring Entity: |
Balakin, Boris (Western Norway University of Applied Sciences) |
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Saparbayeva, Nazerke (Western Norway University of Applied Sciences) |
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Producer: |
Western Norway University of Applied Sciences |
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Software used in Production: |
STAR-CCM+ |
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Software used in Production: |
Python |
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Grant Number: |
300286 |
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Distributor: |
DataverseNO |
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Distributor: |
Western Norway University of Applied Sciences |
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Access Authority: |
Balakin, Boris |
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Depositor: |
Balakin, Boris |
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Date of Deposit: |
2024-04-24 |
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Series Name: |
Fundamental studies of plugging in multiphase flows with adhesive particles. Numerical series. |
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Series Information: |
The simulations reproduce experiments on plugging of ice-in-decane slurry conducted in the multiphase flow loop of Høgskulen på Vestlandet (FLOWCHART project) |
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Holdings Information: |
https://doi.org/10.18710/ARTNTB |
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Study Scope |
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Keywords: |
Chemistry, Computer and Information Science, Engineering, Physics, CFD-DEM, plugging, clogging, multiphase, machine learning, random forest |
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Abstract: |
<p>This dataset contains model data for the CFD-DEM simulation of plugging in multiphase flows with cohesive particles. The main results available from the simulations are presented in the dataset together with the overall description of the model.</p> <p>The second model is the random forest-based machine learning (ML) routine trained using experiments and CFD-DEM simulations. The source of the ML model is provided together with the training dataset.</p> |
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Date of Collection: |
2023-05-01-2024-03-01 |
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Kind of Data: |
simulation data |
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Kind of Data: |
experimental data |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Saparbayeva, Nazerke, and Boris V. Balakin. "CFD-DEM model of plugging in flow with cohesive particles." Scientific Reports 13.1 (2023): 17188. |
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Identification Number: |
10.1038/s41598-023-44202-7 |
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Bibliographic Citation: |
Saparbayeva, Nazerke, and Boris V. Balakin. "CFD-DEM model of plugging in flow with cohesive particles." Scientific Reports 13.1 (2023): 17188. |
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Citation |
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Title: |
Saparbayeva, Nazerke, et al. "Application of Machine Learning to Predict Blockage in Multiphase Flow." Computation 12.4 (2024): 67. |
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Identification Number: |
10.3390/computation12040067 |
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Bibliographic Citation: |
Saparbayeva, Nazerke, et al. "Application of Machine Learning to Predict Blockage in Multiphase Flow." Computation 12.4 (2024): 67. |
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Label: |
00_ReadMe.txt |
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Notes: |
text/plain |
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_gamma=0.78.txt |
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Notes: |
text/plain |
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_gamma=0.88.txt |
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Notes: |
text/plain |
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_gamma=0.8.txt |
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Notes: |
text/plain |
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MLcode.py |
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Notes: |
text/x-python |
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PluggingCases.txt |
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Notes: |
text/plain |
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Label: |
SummaryReport.pdf |
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Notes: |
application/pdf |