A Very Large Eddy Simulation Model Using a Reductionist Inlet Turbulence Generator and Wall Modeling for Stable Atmospheric Boundary Layers
Bibliographic record
Abstract
Abstract— Despite many advances in numerical simulation of stable boundary layers (SBL), most of the models developed are complex and computationally expensive. A computational fluid dynamics (CFD) strategy is proposed that combines very large eddy simulation (VLES) with a reductionist inflow turbulence generator and wall modeling aimed at affordable and practical simulation of SBL. Unlike the standard LES requiring the filter width to be of the scale of grid size, the filter width in VLES can be set at a value between the grid size and the large characteristic length scales of the flow. This strategy, along with the application of wall treatments, results in the significant reduction of computational costs. Moreover, the reductionist approach of the inflow turbulence generator minimizes the number of required input parameters to the model, which makes the model suitable for practical applications. A series of sensitivity studies are conducted to refine the numerical parameters including the grid resolution, filter width, and the inflow turbulence generator variables controlling the length and time scales of the eddies generated at the inlet. The performance of the model is successfully evaluated against wind-tunnel measurements for mean velocity, mean temperature, and turbulence profiles for four different thermal stability levels ranging from weak to strong stability. The spectral analysis of the model for velocity components, temperature, momentum, and heat fluxes showed that the model is capable of successfully resolving the energy cascade for almost two orders of magnitude of wave numbers and partially matching the well-known log-log slopes for the inertial subrange.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".