Development of Machine Learning based wall shear stress models for LES in the presence of adverse pressure gradients and separation
Bibliographic record
Abstract
The Mixture Density Network (MDN), initially developed to predict uncertainty, is used as a wall shear stress model in wall-modeled Large Eddy Simulations (wmLES) of turbulent separated flows. Separation is a common phenomenon in turbomachinery (e.g., compressor and turbine blades), due to strong adverse pressure gradients and curvature effects. However, most standard wall shear stress (WSS) models are no longer applicable in non-equilibrium conditions because of their inherent modeling assumptions about the boundary layer (i.e., fully turbulent, at equilibrium, and attached). In this study, the MDN is trained on turbulent channel flows at various friction Reynolds numbers and on the two-dimensional periodic hill at the bulk Reynolds number of 10,595. The latter test case is designed to allow separation from the hill crest, followed by a massive recirculation bubble and reattachment of the free shear layer on the flat bottom surface. The model takes the velocity field, instantaneous and mean pressure gradients, and wall curvature as inputs. The model outputs the probability distribution of the two wall-parallel components of the wall shear stress. The databases are carefully non-dimensionalized using the kinematic viscosity and wall-model height for better generalizability. The model was successfully evaluated a priori on synthetic data generated from the law-of-the-wall. The relevance of the MDN-model was evaluated a posteriori by performing wmLES using the in-house flow solver Argo-DG on two channel flows and a separated flow.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".