Parameterization Sensitivity and Instability Characteristics of the Maximum Sustainable Heat Flux Framework for Predicting Turbulent Collapse
Bibliographic record
Abstract
Abstract A maximum sustainable heat flux (MSHF) framework for the collapse of turbulence in the stable boundary layer has been previously studied using a one-dimensional model of Couette flow with parameterized turbulent fluxes. This study further investigates the stability properties of this model and assesses the robustness of the MSHF framework for predicting turbulent collapse to the choice of turbulence parameterization. The dynamic stability properties of the system are studied through numerical analysis of linearized equations of motion, and these results are compared with numerical solutions of the fully nonlinear system. While the MSHF mechanism and the qualitative features of the equilibrium structure are robust to changes in turbulence parameterizations, important quantitative differences between the models are found. While the equilibrium structures for Businger–Dyer-type stability functions are independent of the roughness length , all of the other relations show a strong dependence on with regard to their shapes and the value of the MSHF. Equilibrium curves for some of the parameterizations exhibit multiple extrema, and transitions between stable and unstable regimes occur at extrema of the equilibrium curves in parameter space. Along the unstable branch(es), the Couette flow model has only a single unstable mode for all turbulence parameterizations considered. The MSHF framework is qualitatively robust to the choice of parameterization, but its use to predict the collapse of turbulence in the SBL is quantitatively sensitive to the turbulence scheme, especially for small values of .
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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 | 0.001 | 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) | 0.000 | 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".