MAXIMUM-ENTROPY INSPIRED INTERPOLATIVE CLOSURE FOR RADIATIVE HEAT TRANSFER IN GRAY PARTICIPATING MEDIA
Bibliographic record
Abstract
A new interpolative-based approximation of the second-order maximum-entropy (M2) moment closure for predicting radiative heat transfer in gray participating media is proposed and described. In addition to maintaining the desirable properties of the original M2 closure, the proposed interpolative approximation provides significant reduction in computational costs compared to the expensive numerical solution of the corresponding optimization problem for entropy maximization. Its predictive capabilities are assessed, by considering test problems involving radiative heat transfer within two-dimensional enclosures, the results for which are compared to solutions of the first-order maximum entropy (M1) moment closure, as well as those of the more commonly adopted spherical harmonics moment closure techniques (first-order P1 and third-order P3) and the popular discrete ordinates method (DOM). The latter is used as a benchmark for comparisons, whenever exact solutions are not available. The numerical results show that the solutions of the proposed interpolative M2 closure are better than those of M1, P1 and P3 for virtually all cases considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".