Numerical analysis of the flow around a circular cylinder using RANS and LES
Bibliographic record
Abstract
The present study is to simulate the flow past a circular cylinder at a Reynolds (Re) number of 5800, which is based on free-stream velocity and the cylinder diameter. The cylinder is slightly heated and the amount of heat is small enough to be considered as a passive scalar. Due to its complexity, the flow around a circular cylinder is considered as a challenging problem for computational fluid dynamics (CFD) simulation. Re-averaged Navier–Stokes (RANS) equations and large eddy simulation (LES) are two commonly used approaches in turbulent flow simulation. In this study, these two methods are both investigated by employing a CFD software called FLUENT. For two-dimensional (2D) simulation, the renormalization group k–ϵ model is used with enhanced wall treatment. Moreover, 2D LES is also tested, which reveals the necessity for three-dimensional (3D) LES computations. For 3D simulations, computations with the Smagorinsky–Lilly subgrid-scale (SGS) model and dynamic SGS model are used. A phase-averaging technique is employed to study turbulence structure in the circular cylinder wake. An instantaneous quantity is decomposed into a time-mean component, a coherent component and an incoherent component (Reynolds and Hussain Citation1972). After the triple decomposition and structural averaging, the coherent contributions to the Reynolds stresses and temperature variance can be analyzed. The reference phase for phase averaging is calculated for the time history of the lift coefficient CL. Both velocity field and temperature field are investigated and compared with the experimental measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".