Computational Fluid Dynamics (CFD) model and simulation results for the airflow and temperature profiles inside a weighing station of the road network for eleven different design scenarios
Bibliographic record
Abstract
Weighing stations of the road network consist of a pit, where the weighing equipment is located, and slabs at ground level on which the trucks are driven during the weighing process. Snow and ice accumulation on slab surfaces, as well as the freezing of the weighing equipment, could be detrimental to operations. Therefore, the pit of weighing stations needs to be heated during winter, which contributes to snow melting and the protection of weighing equipment. This results in significant energy consumption. This dataset contains: a) A readme file explaining the available data and how to use it; b) For 11 different cases, CFD (Computational Fluid Dynamics) models developed on ANSYS FLUENT R2 2023 (*.cas.h5 files). The 11 cases include a reference case, a validation case, and 9 other cases with alternative designs. The cases are described in the readme file and correspond to different designs of the system in terms of heater configuration and position, and gap size (airtightness); c) For the same 11 cases, simulation results reporting 3D profiles of temperature, velocity, pressure, turbulence quantities in the pit (*.dat.h5 files).
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".