A General Dynamic Linear Tensor-Diffusivity Subgrid-Scale Heat Flux Model for Large-Eddy Simulation of Turbulent Thermal Flows
Bibliographic record
Abstract
In this article, a general dynamic linear tensor diffusivity model is proposed for representing the subgrid-scale (SGS) heat flux (HF). The tensor diffusivity for the model is an inhomogeneous linear function of the resolved strain and rotation rate tensors, and includes three conventional dynamic SGS HF modeling approaches as special cases. In contrast to the dynamic SGS eddy diffusivity modeling approach, the proposed model admits more degrees of freedom for representing the SGS thermal diffusivity, allows for nonalignment between the SGS HF and resolved temperature gradient, and consequently provides a more realistic geometric representation of the SGS heat flux. To validate the proposed modeling approach, numerical simulations have been performed based on a combined forced- and natural-convention flow in a vertical channel with a Reynolds number and a Grashof number Gr = 9.6 × 105. In comparison with the reported direct numerical simulation data and the results obtained using the conventional dynamic SGS eddy diffusivity model, it is shown that the proposed model is able to provide good predictions of various flow quantities at the resolved scale and, more important, offer new insights into near-wall flow physics at the subgrid scale.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".