Large eddy simulation analysis of a model reactive tracer through spatial filtering
Bibliographic record
Abstract
Large eddy simulations (LES) provide a methodology for both analyzing and simulating multi-scale flows when the smallest scales of motion cannot be resolved. Within environmental flows there exist numerous biogeochemical processes involving tracers undergoing reactions. In this study, we perform an a posteriori LES analysis on a direct numerical simulation of an idealized model reactive tracer subjected to three-dimensional turbulence induced by a Rayleigh–Taylor instability. The governing equations, including an advection–diffusion–reaction equation for the reactive tracer, are filtered, and the resulting sub-filter-scale terms are expressed in terms of interactions between scales. The procedure is demonstrated for a generalized degree N polynomial reaction function. Various spectral filters are applied to the data and compared. The preferential choice is to use the widest filter possible with a smoothed cutoff. The sub-filter-scale reaction term that results from filtering the reaction function is considered for each of the filter choices. When using a particularly harsh filter, local balances are found for the resolved scale and cross-scale components of the sub-filter-scale reaction term. The same result is shown for the vertical sub-filter-scale flux for both a reactive and a passive tracer. The components of the sub-filter-scale reaction and vertical flux terms involving interactions at the sub-filter-scale do not show any evidence of local balances and are distributed around the fine turbulent structures in the flow. This suggests that parameterizations for the sub-filter-scale terms would benefit from considering event specific dynamics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".