Refrigeration system of a modern universal ice sports complex: design and implementation experience in Nizhny Novgorod
Bibliographic record
Abstract
Modern world-class ice sports complexes impose stringent requirements on the reliability, energy efficiency, and environmental safety of refrigeration systems. Under international restrictions on the use of conventional refrigerants such as HFCs and the need to comply with domestic regulations, engineering solutions that combine technological resilience with future adaptability are increasingly relevant. This article presents the design and implementation experience of the refrigeration system for a universal ice complex in Nizhny Novgorod, comprising three rinks (one main and two training). Particular attention is paid to equipment selection, redundancy scheme, use of dry coolers (dry coolers) to minimize refrigerant charge, and flexibility in temperature control for each rink. The design solutions achieved a total installed cooling capacity of 2100 kW against a calculated thermal load of 1360 kW, while complying with the Kigali Amendment to the Montreal Protocol and enabling a future transition to alternative refrigerants. Readers will gain insight into a comprehensive approach to designing energy-efficient and environmentally conscious refrigeration systems for ice rinks under Russian regulatory frameworks and global environmental challenges.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".