Modeling the Settling Velocity of a Sphere in Newtonian and Non-Newtonian Fluids with Machine-Learning Algorithms
نویسندگان
چکیده
The traditional procedure of predicting the settling velocity a spherical particle is inconvenient as it involves iterations, complex correlations, and an unpredictable degree uncertainty. limitations can be addressed efficiently with artificial intelligence-based machine-learning algorithms (MLAs). limited number isolated studies conducted to date were constricted specific fluid rheology, particular MLA, insufficient data. In current study, generalized application ML was comprehensively investigated for Newtonian three varieties non-Newtonian fluids such Power-law, Bingham, Herschel Bulkley. A diverse set nine MLAs trained tested using large dataset 967 samples. ranges Reynolds (ReG) drag coefficient (CD) 10?3 < ReG (-) 104 10?1 CD 105, respectively. performances models statistically evaluated evaluation metric coefficient-of-determination (R2), root-mean-square-error (RMSE), mean-squared-error (MSE), mean-absolute-error (MAE). support vector regression polynomial kernel demonstrated optimum performance R2 = 0.92, RMSE 0.066, MSE 0.0044, MAE 0.044. Its generalization capability validated ten-fold-cross-validation technique, leave-one-feature-out experiment, leave-one-data-set-out validation. outcome investigation approach modeling velocity.
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ژورنال
عنوان ژورنال: Symmetry
سال: 2021
ISSN: ['0865-4824', '2226-1877']
DOI: https://doi.org/10.3390/sym13010071