Summary on the Results of Two Computational Fluid Dynamic Benchmarks of Tube and Different Channel Geometries
Bibliographic record
Abstract
Two computational fluid dynamic (CFD) benchmarks have been performed to assess the prediction accuracy and sensitivity of CFD codes for heat transfer in different geometries. The first benchmark focused on heat transfer to water in a tube (first benchmark), while the second benchmark covered heat transfer to water in two different channel geometries (second benchmark) at supercritical pressures. In the first round with the experimental data unknown to the participants (i.e., blind calculations), CFD calculations were conducted with initial boundary conditions and simpler CFD models. After assessment against measurements, the calculations were repeated with the refined boundary conditions and material properties in the follow-up calculation phase. Overall, the CFD codes seem to be able to capture the general trend of heat transfer in the tube and the annular channel but further improvements are required in order to enhance the prediction accuracy. Finally, sensitivity analyses on the numerical mesh and the boundary conditions were performed. It was found that the prediction accuracy has not been improved with the introduction of finer meshes and the effect of mass flux on the result is the strongest among various investigated boundary conditions.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".