Analyzing and improving the thermal performance of road network weighing stations through measurements and CFD modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it