A neural network approach to improve Reynolds-averaged Navier–Stokes modeling of bluff body wakes
Bibliographic record
Abstract
This study explores a machine learning based correction method of Reynolds Averaged Navier–Stokes (RANS) k–ω Shear Stress Transport (SST) turbulence model in simulating flow around wall-mounted finite rectangular prisms at a Reynolds number of 2.5 × 103. Comparisons with Large Eddy Simulation (LES) reveal successful prediction of mean flow global features, coherent wake characteristics, and key flow parameters by the RANS k–ω SST model. While accurately capturing shear-layer separation, recirculation, and reattachment phenomena, the k–ω SST model tends to significantly overestimate the reattachment length (XR) and underpredict global flow variables. To address this, a backpropagation multi-layer perceptron artificial neural network algorithm is introduced to correct wake parameters of the k–ω SST model. By utilizing LES data to train the algorithm, predictive accuracy of the wake parameters, including reattachment length, recirculation length, drag coefficient, lift coefficient, and base pressure coefficient, is enhanced by more than 97%. These results demonstrate that the algorithm is effective in improving k–ω SST predictions, offering a cost-effective tool to achieve accuracies comparable to LES. The study contributes to refining RANS k–ω SST simulations, showcasing the potential of machine learning in mitigating limitations and enhancing predictive capabilities of RANS models in simulating complex flow scenarios involving wall-mounted rectangular prisms.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".