The temperature decomposition method for periodic thermal flows with general wall conditions
Bibliographic record
Abstract
Thermal flows through periodic structures can be found in many industrial applications. By taking advantage of the relationships of flow and thermal fields in periodic modules, computer simulations can be performed over a one-module domain; however, the results can be applied to individual modules. At present this approach is limited to systems with relatively simple boundary situations: either the temperature or heat flux can be specified over the wall surfaces. To address this concern, we develop a temperature decomposition method that can work with more general boundary situations, including the mixed (temperature on some locations and heat flux on other locations) and the convective boundary conditions. The regular temperature is split into two components, namely the transient and equilibrium parts. The transient part decays with the flow and the temperature approaches the equilibrium part gradually. The two components can be solved independently under similar governing equations but different wall and inlet/outlet boundary conditions. The regular temperature can then be quickly obtained by adding them together according to the transient coefficients of individual periodic modules. The algorithm and implementation are described in details, and the method is discussed thoroughly from mathematical and physical considerations. Carefully designed example simulations are also presented to demonstrate the capacity and usefulness of this method for future simulations of thermal periodic flows using various numerical schemes.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".