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Record W4248053867 · doi:10.32920/ryerson.14654091

Commercial Building Thermal Mass Precooling for Conditioning Reduction Under the Operative Temperature Metric

2021· preprint· en· W4248053867 on OpenAlexaff
Christopher Raghubar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan UniversitySciencetech (Canada)SAIT Polytechnic
FundersInstitut National des Sciences Appliquées de LyonIndian National Science Academy
KeywordsThermal comfortCooling loadSlabSetpointOperative temperatureVentilation (architecture)Environmental scienceAir conditioningSolar gainThermal massHeat transfer coefficientRadiant coolingNuclear engineeringHeat transferThermalMechanicsThermodynamicsMechanical engineeringEngineeringStructural engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Building thermal mass precooling is highly variable due to the uncertainty of the convective heat transfer coefficient, and current research neglects the radiative cooling effects of reduced interior surface temperatures. The research presented aims to address the shortcomings of current research by modelling night ventilation through concrete slab hollow cores, increasing confidence in the heat transfer coefficient estimate; and modelling the operative temperature experienced by an occupant in an open office with simplified geometry. The cooling load of a baseline non-ventilated slab was determined through a custom numerical model and the operative temperature of the baseline was assigned to the same model with hollow core slab ventilation to determine the ambient air setpoint temperature associated cooling load. The ventilated model was found to achieve 35% cooling energy savings compared to the baseline, with compromised occupant comfort in the early morning, and improved occupant comfort for the rest of the day.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.015
GPT teacher head0.257
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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