Efficient Calculation of Radiation Heat Transfer in Participating Media
Bibliographic record
Abstract
A low-cost computational solution to radiation problems can be obtained by using a simple model, such as the P 1 model, but the accuracy can be very poor. High accuracy can be obtained by solving the radiative transfer equation, but the solution cost can be exorbitant for strongly participating media. The Q L method presented in this paper allows the radiation heat transfer to be computed from a single equation for the average intensity, like the P 1 model, but the Q L equation contains parameters that account for a nonuniform intensity distribution. The method converges to the solution of the radiative transfer equation with grid refinement and will accommodate any scattering phase function. For a given spatial and directional discretization, and for problems involving radiation only, the accuracy of the Q L method is shown to equal or exceed that of the finite volume method. The solution cost of the Q L method is comparable to the finite volume method for weakly participating media, but for strongly participating media the Q L method is much less costly. The Q L method is designed for application in general-purpose codes in which radiation is but one of several important processes, and it is in such applications that the major benefits of the Q L method are expected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".