ON THE ACCURACY OF THE PRESSURE FLUCTUATIONS CALCULATED FROM AN LBM SIMULATION OF TURBULENT CHANNEL FLOW
Bibliographic record
Abstract
In this paper the pressure fluctuations from a simulation of turbulent channel flow computed with the D3Q19 athermal lattice Boltzmann method (LBM) are compared to those calculated by the spectral simulation of Moser et al. (1999). Special care was taken to ensure that the computational domains used in each simulation were the same in order to eliminate the effect of the domain size on the turbulence statistics. It was found that the LBM over-predicts the variance of the pressure fluctuations by as much as 7%. A number of possible causes for this over-prediction were investigated, and it was concluded that the over-prediction is most likely caused by compressibility effects since the Mach number of the LBM simulation was 0.2 while the spectral simulation was incompressible. The compressibility of the LBM was examined further by comparing the LBM results to a fully-compressible discontinuous Galerkin simulation with the same Mach number. It was determined that, while the effect of the compressibility on the pressure fluctuations was similar, the density and temperature fluctuations were very different. This is because the D3Q19 LBM does not have enough degrees of freedom to allow the temperature to vary. For this reason, it is not recommended that this LBM be used for simulations in which the effect of compressibility is thought to be important.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".