Evaluation of RANS and LES turbulence models for simulating a steady 2-D plane wall jet
Bibliographic record
Abstract
Purpose This paper examines various turbulence models for numerical simulation of a steady, two-dimensional (2-D) plane wall jet without co-flow using the commercial CFD software (ANSYS FLUENT 14.5). The purpose of this paper is to decide the most suitable and most economical method for steady, 2-D plane wall jet simulation. Design/methodology/approach Seven Reynolds-averaged Navier–Stokes (RANS) turbulence models were evaluated with respect to typical jet scaling parameters such as the jet half-height and the decay of maximum jet velocity, as well as coefficients from the law of the wall and for skin friction. Then, a plane wall jet generating from a rectangular slot of 1:6 aspect ratio located adjacent to the wall was investigated in a three-dimensional (3-D) model using large eddy simulation (LES) and the Stress-omega Reynolds stress model (SWRSM), with the results compared to experimental measurements. Findings The comparisons of these simulated flow characteristics indicated that the SWRSM was the best of the seven RANS models for simulating the turbulent wall jet. When scaled with outer variables, LES and SWRSM gave generally indistinguishable mean velocity profiles. However, SWRSM performed better for near-wall mean velocity profiles when scaled with inner variables. In general, the results show that LES performed reasonably well when predicting the Reynolds stresses. Originality/value The main contribution of this article is in determining the capabilities of different RANS turbulence closures and LES for the prediction of the 2-D steady wall jet flow to identify the best modelling approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".