Experimental Study of Heat Transfer Mechanisms and Energy Consumption in a Heated Truck Weigh Station during Winter
Bibliographic record
Abstract
This study presents the findings of an extensive experimental campaign conducted at a weigh station located in Quebec City (Canada) during the winter 2023-2024.The primary objective was to understand the thermal dynamics and energy consumption profile of the weigh station when the unit heaters beneath the weighing platforms were activated.Our investigation revealed that non-uniform temperature distributions within the pit, where the scales and associated equipment are located, resulted from the inactive state of one of the unit heaters during the initial 36 days of the experimental campaign and additionally due to their arrangement.Furthermore, distinct energy consumption profiles were observed during operational periods when all seven-unit heaters were in use compared to periods with only six unit heaters operating.The utilization of all unit heaters improved the control of the heating system, which is based on two thermocouple temperature readings, leading to a range of energy consumption more adapted to outdoor temperature fluctuations.Overall, increasing the heating capacity of the weighing station by 10 kW raised the average pit temperature.This allowed the pit temperature to stay within the adequate working temperature for the weighing equipment within the pit while reducing the average energy consumption of the weighing station.In addition, this study presents a dataset for validating steady-state Computational Fluid Dynamics (CFD) models of weighing stations, contributing to future optimization of design and operational strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".