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Experimental study on the performance evaluation of active chilled beams in cooling operation under varied boundary conditions

2019· article· en· W2982277430 on OpenAlexaff
Marc-Antoine Jean, Rohit Upadhyay, Mike Koupriyanov, Rodrigo Mora

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsBritish Columbia Institute of TechnologyPageau Morel and Associates (Canada)
Fundersnot available
KeywordsOffset (computer science)Thermal comfortSimulationThermalMode (computer interface)Boundary value problemEngineeringComputer scienceAutomotive engineeringMeteorologyMathematics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.245
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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