Machine learning and parametrisation of multi-cell structures of secondary circulation in a tight open channel bend using LES
Bibliographic record
Abstract
Large eddy simulations of an open channel bend are performed at a variety of water depths and flow rates. The results at several cross sections are decomposed into sub-cells of secondary circulation using clusters of instantaneous vortices. The strength and position of the sub-cells are then modelled using decision trees, multiple linear regression, multi-layer perceptrons, and adaptive neuro-fuzzy inference systems to obtain parametric models of secondary circulation development in a channel bend. The development of individual cells and total circulation is shown for an arbitrary flow condition using the model, as well as the dependence of all the circulation output variables on the input parameters of aspect ratio and Froude number. The positions of the sub-cells (but not their circulations) are largely independent of the Froude number, and the cross-stream position of the centre cell is found to behave linearly. The model with the best performance across all predicted variables is the ANFIS model without classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".