Application of Machine Learning to Predict Blockage in Multiphase Flow
Abstract
:1. Introduction
2. Methodology
2.1. Experiments
2.2. CFD-DEM Model
2.3. Machine Learning
3. Results
3.1. Machine Learning
Dataset
3.2. Experiments and CFD-DEM Model
3.2.1. Validation
3.2.2. Results
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Case | Precision | Recall | F1-Score |
---|---|---|---|
No Block | 1.00 | 0.80 | 0.89 |
Block | 0.96 | 1.00 | 0.98 |
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Saparbayeva, N.; Balakin, B.V.; Struchalin, P.G.; Rahman, T.; Alyaev, S. Application of Machine Learning to Predict Blockage in Multiphase Flow. Computation 2024, 12, 67. https://doi.org/10.3390/computation12040067
Saparbayeva N, Balakin BV, Struchalin PG, Rahman T, Alyaev S. Application of Machine Learning to Predict Blockage in Multiphase Flow. Computation. 2024; 12(4):67. https://doi.org/10.3390/computation12040067
Chicago/Turabian StyleSaparbayeva, Nazerke, Boris V. Balakin, Pavel G. Struchalin, Talal Rahman, and Sergey Alyaev. 2024. "Application of Machine Learning to Predict Blockage in Multiphase Flow" Computation 12, no. 4: 67. https://doi.org/10.3390/computation12040067