Development and validation of a generic evaporator model of chillers
Bibliographic record
Abstract
Chillers consume approximately half of a building’s total energy, and variables within a chiller evaporator are crucial for system operation, control, and design of the secondary chilled water loop. Although detailed physical models like computational fluid dynamics are used to study evaporators, their complexity and high computational cost make them impractical for Heating, Ventilation, and Air Conditioning (HVAC) systems with limited data in a building automation system.This paper presents a grey-box model for chiller evaporators under steady-state conditions, integrating both physical and data-driven approaches. The model development starts with an analysis of the evaporator energy balance and heat transfer on both the water and refrigerant sides. It is then simplified into a practical equation with a simple format, requiring a minimal number of model input variables that are usually available in building automation system. The proposed model targets to estimate the chilled water temperature difference across the chiller evaporator. The case study of a real institutional building with a dataset at 15-minute intervals is used to validate the model performance. Results indicate it achieved a high accuracy with a coefficient of variance of root mean square error of 3.9%. The proposed model can be used to study HVAC operation optimization, fault detection and diagnosis, ultimately contributing to improved energy efficiency and system reliability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".