Deep learning-assisted prediction of viscoplastic flow in superhydrophobic channels
Bibliographic record
Abstract
Viscoplastic fluid flows in channels with asymmetric superhydrophobic (SH) walls are analyzed using computational fluid dynamics simulations, while a machine learning framework is developed to enhance computational efficiency and accuracy. A deep learning framework, based on a convolutional neural network (CNN), is trained on numerical simulation data to reconstruct velocity fields and identify yield surfaces. Additional models, including multiple linear regression (MLR), support vector machine (SVM), extreme learning machine (ELM), and adaptive neuro-fuzzy inference system (ANFIS), estimate key flow variables such as effective slip length and normalized plug area, with their predictions compared to CNN results, using standard performance metrics. Our results reveal that the CNN demonstrates strong predictive accuracy, closely matching the performance of ELM and ANFIS while significantly outperforming MLR and SVM. The trained CNN also enables the optimization of SH wall characteristics, capturing the non-monotonic effects of SH wall groove misalignment (ε) and slip area fraction (φ) on velocity asymmetry. Optimal misalignment shifts from ε=0.25 at φ1=φ2=0.5 to ε≈0.35 for φ1=φ2=0.1 or φ1=φ2=0.9, where subscripts 1 and 2 refer to the lower and upper SH walls, respectively. In addition, CNN results reveal that the optimal slip area fraction φ2=0.5 remains unchanged for smaller upper wall groove periodicity lengths (ℓ2), but decreases as ℓ2 increases, leading to a ∼10% reduction in flow velocity asymmetry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".