Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review-Reference-Cited by-同舟云学术

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Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review

Published:2024-01-30 Issue:6 Volume:63 Page:2509-2518
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Molana Maysam¹²^ORCID,Darougheh Sahar³,Biglar Abbas⁴,Chamkha Ali J.⁵,Zoldak Philip²

Affiliation:

1. Department of Mechanical Engineering, Wayne State University, Detroit, Michigan 48202, United States

2. Enginuity Power Systems, Clinton Twp, Michigan 48035, United States

3. College of Information Sciences and Technology, Pennsylvania State University, University Park, Pennsylvania 16802, United States

4. Department of Industrial Engineering, Islamic Azad University Doroud Branch, Doroud Postal Code 1477893855, Iran

5. Faculty of Engineering, Kuwait College of Science and Technology, P.O. Box 177, Doha 35004, Kuwait

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.3c04097

Reference62 articles.

1. Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine

2. Wagh, M.; Joo, N. R.; Zoldak, P.; Won, H.; Ra, Y.; Shi, Y. Real Fuel Modeling for Gasoline Compression Ignition Engine. SAE Technical Paper; 2020-01-0784; 2020 .

3. Artificial neural networks combined with experimental design: A “soft” approach for chemical kinetics

4. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

5. Film cooling improvement analysis in gas turbine blades by swirling coolant flow using a numerical study and an RBF artificial neural network

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