Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis
Machine learning's prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learnin...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2024
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| Online Access: | http://eprints.utm.my/108955/1/SaifulAmri2024_ReviewofModelingSchemesandMachine.pdf |
| _version_ | 1824452073297543168 |
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| author | Irfan Bahiuddin, Irfan Bahiuddin Mazlan, Saiful Amri Fitrian Imaduddin, Fitrian Imaduddin Shapiai, Mohd. Ibrahim Ubaidillah, Ubaidillah Dhani Avianto Sugeng, Dhani Avianto Sugeng |
| author_facet | Irfan Bahiuddin, Irfan Bahiuddin Mazlan, Saiful Amri Fitrian Imaduddin, Fitrian Imaduddin Shapiai, Mohd. Ibrahim Ubaidillah, Ubaidillah Dhani Avianto Sugeng, Dhani Avianto Sugeng |
| author_sort | Irfan Bahiuddin, Irfan Bahiuddin |
| collection | ePrints |
| description | Machine learning's prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial-temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction. |
| first_indexed | 2025-02-19T02:44:43Z |
| format | Article |
| id | utm.eprints-108955 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2025-02-19T02:44:43Z |
| publishDate | 2024 |
| publisher | De Gruyter |
| record_format | dspace |
| spelling | utm.eprints-1089552024-12-16T00:46:24Z http://eprints.utm.my/108955/ Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis Irfan Bahiuddin, Irfan Bahiuddin Mazlan, Saiful Amri Fitrian Imaduddin, Fitrian Imaduddin Shapiai, Mohd. Ibrahim Ubaidillah, Ubaidillah Dhani Avianto Sugeng, Dhani Avianto Sugeng TJ Mechanical engineering and machinery Machine learning's prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial-temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction. De Gruyter 2024-01 Article PeerReviewed application/pdf en http://eprints.utm.my/108955/1/SaifulAmri2024_ReviewofModelingSchemesandMachine.pdf Irfan Bahiuddin, Irfan Bahiuddin and Mazlan, Saiful Amri and Fitrian Imaduddin, Fitrian Imaduddin and Shapiai, Mohd. Ibrahim and Ubaidillah, Ubaidillah and Dhani Avianto Sugeng, Dhani Avianto Sugeng (2024) Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis. Journal of the Mechanical Behavior of Materials, 33 (1). pp. 1-21. ISSN 2191-0243 http://dx.doi.org/10.1515/jmbm-2022-0309 DOI:10.1515/jmbm-2022-0309 |
| spellingShingle | TJ Mechanical engineering and machinery Irfan Bahiuddin, Irfan Bahiuddin Mazlan, Saiful Amri Fitrian Imaduddin, Fitrian Imaduddin Shapiai, Mohd. Ibrahim Ubaidillah, Ubaidillah Dhani Avianto Sugeng, Dhani Avianto Sugeng Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| title | Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| title_full | Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| title_fullStr | Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| title_full_unstemmed | Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| title_short | Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| title_sort | review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utm.my/108955/1/SaifulAmri2024_ReviewofModelingSchemesandMachine.pdf |
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