IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4887-d1488716.html
   My bibliography  Save this article

Assessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasma-Assisted Ignition to Turbulent Flame Propagation

Author

Listed:
  • Mashrur Ertija Shejan

    (Department of Mechanical Engineering, Idaho State University, Colonial Hall, Room 102, Pocatello, ID 83209, USA)

  • Sharif Md Yousuf Bhuiyan

    (Department of Mechanical Engineering, Idaho State University, Colonial Hall, Room 102, Pocatello, ID 83209, USA)

  • Marco P. Schoen

    (Department of Mechanical Engineering, Idaho State University, Colonial Hall, Room 102, Pocatello, ID 83209, USA)

  • Rajib Mahamud

    (Department of Mechanical Engineering, Idaho State University, Colonial Hall, Room 102, Pocatello, ID 83209, USA)

Abstract

Combustion involves the study of multiphysics phenomena that includes fluid and chemical kinetics, chemical reactions and complex nonlinear processes across various time and space scales. Accurate simulation of combustion is essential for designing energy conversion systems. Nonetheless, due to its multiscale, multiphysics nature, simulating these systems at full resolution is typically difficult. The massive and complex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manage the substantial amount of combustion data that is either obtained through experiments or simulations, and thereby can find the hidden patterns underlying these data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, offering promising opportunities to explore its integration with combustion research. This work provides an in-depth overview of machine learning applications in turbulent combustion modeling and presents the application of machine learning models: Decision Trees (DT) and Random Forests (RF), for the spatio-temporal prediction of plasma-assisted ignition kernels, based on the initial degree of ionization, with model validations against DNS data. The results demonstrate that properly trained machine learning models can accurately predict the spatio-temporal ignition kernel profile based on the initial energy deposition and distribution.

Suggested Citation

  • Mashrur Ertija Shejan & Sharif Md Yousuf Bhuiyan & Marco P. Schoen & Rajib Mahamud, 2024. "Assessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasma-Assisted Ignition to Turbulent Flame Propagation," Energies, MDPI, vol. 17(19), pages 1-33, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4887-:d:1488716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4887/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/19/4887/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4887-:d:1488716. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.