AI-driven prediction of central plug morphology in Poiseuille flow of Bingham fluids with superhydrophobic walls
Bibliographic record
Abstract
This study presents a machine learning-based framework for predicting the flow behavior and central plug morphology of a viscoplastic Poiseuille flow in channels with two superhydrophobic (SH) walls. Here, the lower wall has constant hydrophobicity and groove characteristics, and the upper wall features varying groove periodicity length, slip area fraction, and slip number. Numerical simulations, conducted using OpenFOAM with the Papanastasiou regularization for modelling the viscoplastic fluid, generated a comprehensive database encompassing key flow parameters, including the Bingham number. Four predictive models i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were used. To assess the accuracy of the models, statistical indices such as correlation coefficient (R), variance accounted for (VAF), root mean square error (RMSE), mean absolute error (MAE), and mean absolute relative error (MARE), were employed. ANFIS demonstrated the highest accuracy, while ELM provided competitive performance with significantly faster computation and simpler hyperparameter tuning. A sensitivity analysis conducted using ELM revealed that the accurate prediction of the normalized area of the center plug is primarily influenced by the groove periodicity length of the upper wall.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".