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Predicting buoyant jet characteristics: a machine learning approach

  • Hossein Hassanzadeh , Saptarshi Joshi and Seyed Mohammad Taghavi ORCID logo EMAIL logo
Published/Copyright: June 6, 2023
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 19 Issue 2

Abstract

We study positively buoyant miscible jets through high-speed imaging and planar laser-induced fluorescence methods, and we rely on supervised machine learning techniques to predict jet characteristics. These include, in particular, predictions to the laminar length and spread angle, over a wide range of Reynolds and Archimedes numbers. To make these predictions, we use linear regression, support vector regression, random forests, K-nearest neighbour, and artificial neural network algorithms. We evaluate the performance of the aforementioned models using various standard metrics, finding that the random forest algorithm is the best for predicting our jet characteristics. We also discover that this algorithm outperforms a recent empirical correlation, resulting in a significant increase in accuracy, especially for predicting the laminar length.

Keywords: buoyant jets; fluid mechanics; laminar length; machine learning; spread angle
Corresponding author: Seyed Mohammad Taghavi, Department of Chemical Engineering, Université Laval, Québec, QC, G1V 0A6, Canada, E-mail:

Funding source: Natural Sciences and Engineering Research Council of Canada

Award Identifier / Grant number: ALLRP 577111-22

Award Identifier / Grant number: CG109154

Award Identifier / Grant number: CG132931

Funding source: Canada Research Chairs

Award Identifier / Grant number: CG125810

Funding source: Petroleum Technology Alliance Canada

Award Identifier / Grant number: AUPRF2022-000124

Funding source: Canada Foundation for Innovation

Award Identifier / Grant number: GF130120

Award Identifier / Grant number: GF525075

Award Identifier / Grant number: GQ130119

Acknowledgement

The authors would like to thank Mr. R. Mahmoudi for his valuable comments.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research has been carried out at Université Laval. The authors wish to acknowledge the financial support of this research by PTAC-AUPRF via Grant No. AUPRF2022-000124 and NSERC via Alliance Grant No. ALLRP 577111-22 (“Towards Net-Zero Emissions: mechanics, processes and materials to support risk-based well decommissioning”). The authors also express their gratitude to the Natural Sciences and Engineering Research Council of Canada, via the Discovery Grant (Grant No. CG109154) and Research Tools and Instruments Grant (Grant No. CG132931), the Canada Research Chair on Modeling Complex Flows (Grant No. CG125810), and the Canada Foundation for Innovation via the John R. Evans Leaders Fund (Grant No. GF130120, GQ130119 and GF525075). SJ also acknowledges the Mitacs Globalink Research Internship Award.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2023-03-19
Accepted: 2023-05-18
Published Online: 2023-06-06

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. CPPM special issue in honor of Professor Faïçal Larachi
  4. Research Articles
  5. Predicting buoyant jet characteristics: a machine learning approach
  6. Insights into the bubble formation dynamics in converging shape microchannels using CLSVOF method
  7. Comparison of different CFD approaches for the simulation of developing free surface two-phase flow in straight and bent pipes
  8. A new approach to model the fluid dynamics in sandwich packings
  9. Effect of novel mixed impeller on local bubble size and flow regime transition in pilot scale gas-liquid stirred tank reactor
  10. Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches
  11. Extractive desulfurization of crude petroleum oil and liquid fuels using trihexyl tetradecyl phosphonium bis(2-ethylhexyl) phosphate ionic liquid
  12. CFD-aided contraction-expansion static mixer design for oil-in-water emulsification
  13. Liquid-liquid flow pattern and mass transfer in a rotating millimeter channel reactor
  14. By-product Eucalyptus leaves valorization in the basic dye adsorption: kinetic equilibrium and thermodynamic study
https://doi.org/10.1515/cppm-2023-0026
Keywords for this article
buoyant jets; fluid mechanics; laminar length; machine learning; spread angle

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. CPPM special issue in honor of Professor Faïçal Larachi
  4. Research Articles
  5. Predicting buoyant jet characteristics: a machine learning approach
  6. Insights into the bubble formation dynamics in converging shape microchannels using CLSVOF method
  7. Comparison of different CFD approaches for the simulation of developing free surface two-phase flow in straight and bent pipes
  8. A new approach to model the fluid dynamics in sandwich packings
  9. Effect of novel mixed impeller on local bubble size and flow regime transition in pilot scale gas-liquid stirred tank reactor
  10. Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches
  11. Extractive desulfurization of crude petroleum oil and liquid fuels using trihexyl tetradecyl phosphonium bis(2-ethylhexyl) phosphate ionic liquid
  12. CFD-aided contraction-expansion static mixer design for oil-in-water emulsification
  13. Liquid-liquid flow pattern and mass transfer in a rotating millimeter channel reactor
  14. By-product Eucalyptus leaves valorization in the basic dye adsorption: kinetic equilibrium and thermodynamic study
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