I. Bahiuddin, S. Mazlan, F. Imaduddin, M. I. Shapiai, Ubaidillah, D. A. Sugeng
{"title":"Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis","authors":"I. Bahiuddin, S. Mazlan, F. Imaduddin, M. I. Shapiai, Ubaidillah, D. A. Sugeng","doi":"10.1515/jmbm-2022-0309","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":17354,"journal":{"name":"Journal of the Mechanical Behavior of Materials","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Mechanical Behavior of Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jmbm-2022-0309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The journal focuses on the micromechanics and nanomechanics of materials, the relationship between structure and mechanical properties, material instabilities and fracture, as well as size effects and length/time scale transitions. Articles on cutting edge theory, simulations and experiments – used as tools for revealing novel material properties and designing new devices for structural, thermo-chemo-mechanical, and opto-electro-mechanical applications – are encouraged. Synthesis/processing and related traditional mechanics/materials science themes are not within the scope of JMBM. The Editorial Board also organizes topical issues on emerging areas by invitation. Topics Metals and Alloys Ceramics and Glasses Soils and Geomaterials Concrete and Cementitious Materials Polymers and Composites Wood and Paper Elastomers and Biomaterials Liquid Crystals and Suspensions Electromagnetic and Optoelectronic Materials High-energy Density Storage Materials Monument Restoration and Cultural Heritage Preservation Materials Nanomaterials Complex and Emerging Materials.