Analyzing and improving the thermal performance of road network weighing stations through measurements and CFD modeling
Bibliographic record
Abstract
Weighing stations ensure the safety and durability of road infrastructures. In cold climates, weighing stations are heated to melt accumulated snow and maintain an adequate operating temperature, resulting in significant energy consumption. The objective of this work is to understand the heat transfer and airflow within weighing stations and identify potential improvements. A CFD model was developed and validated, based on measurements in a weighing station in Quebec City, Canada. Then, three performance metrics were defined to assess thermal uniformity inside the pit, the heat flux available for snow melting, and the amount of heat losses. A parametric study was performed by varying the heater configuration and capacity, as well as the airtightness of the pit, to identify the most influential variables. Results showed that the heat losses due to airflow through the different gaps in the station were dominant, representing around 54% of the heat input in the current situation. Adopting a new configuration (more heaters of smaller capacity) and improving airtightness significantly improved thermal performance under simulated conditions. The methods and results from this paper are useful to engineers who design, maintain, operate and renovate weighting stations and other similar heat transfer systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".