Preliminary design of a snow storage system for cooling a poultry house placed in Québec
Bibliographic record
Abstract
While conventional renewable energies such as hydro, solar, wind or geothermal are constantly being developed and implemented, there has been far too little research into the use of snow for cooling purposes. A Snow Storage System (SSS) consists of a deposit where snow is stored and insulated during the winter as well as a cooling station that uses the cold of the snow to condition a building during the summer. This technology can only be applied to countries with both snow and hot seasons, but there is a big potential in some regions -for example, in certain parts of Canada -for the use of this system.Current SSS utilisation mainly consists of big facilities like the Sundsvall Hospital in Sweden, the Sapporo airport in Japan or the Oslo’s airport in Norway, all of which are analyzed in this project. Nevertheless, the chief objective of the project is to study the implementation of this technology in a smaller facility, specifically a broiler house situated in Quebec. This broiler house only has a ventilation system to refrigerate the building, and some days in summer the ventilation is insufficient to meet the necessary quality and health standards. Moreover, this problem will likely worsen as temperatures in Canada are projected to increase in future years during the warmer seasons.MATLAB software has been used to program and simulate the model of a melting snow pile while applying the load needed to cool the broiler house. As a preliminary design, the model only takes into account the volume of snow melted due to rain (Vrain), due to ground contact (Vground) and due to convection with air (Vair), this last factor representing 80% of the total. For the periods of refrigeration, the model uses the volume melted due to the cooling system (Vcool) XIto calculate the total, this lastrepresenting 90% of the total. The size of the SSS allows for the air conditioning of two different flocks during the summer.The model gives satisfactory results and serves as a tool that can size the SSS and adapt its dimensions to fit the cooling load needed for the building. Even though this tool was used to size a SSS for a broiler house placed in Quebec, it could also be used effectively for other facilities
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".