Insights for Modelling Turbulence in a Backward-Facing Step Flow in a Narrow Channel
Bibliographic record
Abstract
Turbulent backward-facing step (BFS) flows in narrow channels apply to scenarios where the aspect ratio (channel width to depth ratio of the expanded channel) is less than 10.While such flows may be prevalent in practical cases such as compact cooling devices and turbine blade cooling channels with ribs, detailed experimental flow measurements are relatively expensive and rare [1,2].To facilitate parametric design at an amenable cost, it is imperative to employ appropriate tools that model the turbulent flow field.This is particularly important due to the complicated flow phenomena of the recirculation region, where flow separation is rampant and deterministic of the associated pressure differential cost of the flow system.However, current turbulent modelling tools are not sufficiently tuned for such a complex flow.This work is aimed at addressing this need.To that end, multicomponent velocity measurements of the flow field in the recirculation region of a narrow-channelled BFS are obtained and assessed to provide insights into how turbulence may be modelled.The experimental data is obtained using two-dimensional two-component high resolution particle image velocimetry.The measurements were conducted in an optically accessible channel, designed to simulate a closed BFS of step height h, aspect ratio 7.7 and expansion ratio 1.25.With the Reynolds number of the in-coming flow based on the maximum streamwise velocity and h at ~6200, turbulent flow in the channel was assured.Measurements across multiple spanwise planes of the recirculation region were subsequently obtained and evaluated.This was done to specifically study low and high-order moment turbulence statistics of the flow field relevant for turbulent modelling.The results show intricate and distinctive trends of the turbulent eddy viscosity, Prandtl mixing length, and coefficients of the Kolmogorov-Prandtl (K-P) expression for single and two-equation models.In the separated region, the eddy viscosity distributions in the wall-normal direction vary most with distance from the step up to 0.3h.The profiles are different from other flows.Notably, they deviate remarkably from that observed in a turbulent boundary layer (TBL) flow, with maximum values far exceeding it as well as that of a wide-channelled BFS flow [3].The mixing length profiles over the bottom wall are, on the other hand, similarly distributed in the streamwise direction.However, when assessed as a length scale in the K-P expression, the mixing length yields a coefficient that is not unity.The evaluation of planar estimates of the production and dissipation of energy yield coefficients of the K-P expression that are also non-uniform.They also suggest that underlying basis of a Smagorinsky-Lilly large eddy simulation model is inapplicable in the separated region of a narrow-channelled BFS flow.These results reveal that for narrow-channelled BFS flows, a single-equation turbulence model may be appropriately used to provide acceptable simulations.However, much more complex accounts of Reynolds stresses should be considered for accurate predictions of eddy viscosity-based turbulence models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".