Experimental study on the performance evaluation of active chilled beams in cooling operation under varied boundary conditions
Bibliographic record
Abstract
Abstract This study investigates the thermal comfort and indoor air quality performance of Active Chilled Beams (ACB) in cooling operation mode in an open office environment with asymmetrical loads. Many studies on thermal comfort using ACB in cooling mode have been conducted; most of the studies confirm that thermal comfort is satisfactory because the temperature gradient and the airspeeds are acceptable at the occupant level. However, these studies do not specifically address the local discomfort in cooling mode. Furthermore, these studies do not consider performance under different ACB configurations or varied boundary conditions such as those found in real offices. This paper reports the results of an experimental study that addresses the above issues. A laboratory experiment was designed to simulate a multi-occupant open office with an ACB subjected to asymmetric boundary conditions. The results demonstrate that discomfort draft risk at the ankle level is higher when the ACB is oriented parallel to the window. Furthermore, the results suggest that shape (the type of ACB) and throw of ACB affect air distribution. The results emphasize the importance of properly selecting, orienting, and designing ACB, not just to offset the room loads, but to match the proportions and boundary conditions of the office.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".