Transient conducto-radiative heat transfer in a singlemonte-carlo algorithm : Handling the nonlinearity
Bibliographic record
Abstract
It was recently shown that the Green formulation of transient coupled heat transfercould be turned into a Monte-Carlo algorithm that adresses the solution in a direct manner, withoutiterating between the modes, i.e. sampling heat-transfer paths that combine conduction, convectionand radiation at the lower level. The main benefit of such an approach is that the temperature at a givenlocation and a given time can be evaluated without constructing the whole temperature field. There isa strong similarity with standard radiative transfer Monte-Carlo algorithms that evaluate the specificintensity at a given location and in a given direction without constructing the whole intensity field(backward tracking of a radiative path from the observation location to the radiative sources). Here,heat-transfer paths are tracked backwards in both time and space, allowing a straightforward use ofcomputer graphics tools developed for the cinema industry for the evaluation of temperature insidecomplex industrial geometries. However, as Green formalism is used from the start, this method islimited to linearised heat-transfer, notably to limited temperature differences, so that radiative trans-fer can be linearised around a reference temperature. We propose a new branching-path sampling ap-proach that bypasses this constraint. In this poster, we explain the formal developments to obtain theMonte-Carlo algorithm and we present one implementation in complex geometry conducto-radiativeconfigurations, leading to a first-level discussion of this observed numerical behaviour in terms ofboth computation time and branching statistics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".