{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T21:50:41Z","timestamp":1792187441704,"version":"4.4.0"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DEPUTYSHIP FOR RESEARCH &amp; INNOVATION, MINISTRY OF EDUCATION, SAUDI ARABIA","award":["IFT20081"],"award-info":[{"award-number":["IFT20081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The traditional procedure of predicting the settling velocity of a spherical particle is inconvenient as it involves iterations, complex correlations, and an unpredictable degree of uncertainty. The limitations can be addressed efficiently with artificial intelligence-based machine-learning algorithms (MLAs). The limited number of isolated studies conducted to date were constricted to specific fluid rheology, a particular MLA, and insufficient data. In the current study, the generalized application of ML was comprehensively investigated for Newtonian and three varieties of non-Newtonian fluids such as Power-law, Bingham, and Herschel Bulkley. A diverse set of nine MLAs were trained and tested using a large dataset of 967 samples. The ranges of generalized particle Reynolds number (ReG) and drag coefficient (CD) for the dataset were 10\u22123 &lt; ReG (-) &lt; 104 and 10\u22121 &lt; CD (-) &lt; 105, respectively. The performances of the models were statistically evaluated using an evaluation metric of the coefficient-of-determination (R2), root-mean-square-error (RMSE), mean-squared-error (MSE), and mean-absolute-error (MAE). The support vector regression with polynomial kernel demonstrated the optimum performance with R2 = 0.92, RMSE = 0.066, MSE = 0.0044, and MAE = 0.044. Its generalization capability was validated using the ten-fold-cross-validation technique, leave-one-feature-out experiment, and leave-one-data-set-out validation. The outcome of the current investigation was a generalized approach to modeling the settling velocity.<\/jats:p>","DOI":"10.3390\/sym13010071","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T19:54:46Z","timestamp":1609703686000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Modeling the Settling Velocity of a Sphere in Newtonian and Non-Newtonian Fluids with Machine-Learning Algorithms"],"prefix":"10.3390","volume":"13","author":[{"given":"Sayeed","family":"Rushd","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8288-1782","authenticated-orcid":false,"given":"Noor","family":"Hafsa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8544-2550","authenticated-orcid":false,"given":"Majdi","family":"Al-Faiad","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5069-0447","authenticated-orcid":false,"given":"Md","family":"Arifuzzaman","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Agwu, O.E., Akpabio, J.U., and Dosunmu, A. (2019). Artificial Neural network model for predicting drill cuttings settling velocity. 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