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Record W3008509745 · doi:10.1139/tcsme-2019-0046

Correlations between dynamic thermal properties, energy consumption, and comfort in wood, concrete, and lightweight buildings

2020· article· en· W3008509745 on OpenAlexafffundvenueabout
A. Pépin, Louis Gosselin, Jonathan Dallaire

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBuilding envelopeThermal transmittanceThermal massEnvelope (radar)ThermalThermal comfortEnvironmental scienceEnergy consumptionMaterials scienceThermal resistanceStructural engineeringMeteorologyEngineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

An office building located in Québec City (QC, Canada) with different envelope assemblies has been simulated to determine the energy consumption and thermal comfort that they provide. The resistance, thermal mass, and materials (concrete, cross-laminated timbers (CLT), and light-frame) were varied in a series of 164 different scenarios, and the energy intensities for heating and cooling were determined in each case, along with the discomfort index. Results show that the materiel used to provide thermal mass has a larger impact on comfort and energy consumption than the value of the thermal mass thickness itself. It was also attempted to correlate the performance of the envelope assessed through energy simulations with four dynamic thermal properties (i.e., dynamic transmittance, areal heat capacity, decrement factor, and time lag). The internal areal heat capacity appeared to be the most important variable to explain variations of performance of the envelope.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.763

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.170
Teacher spread0.160 · 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 designObservational
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

Citations1
Published2020
Admission routes4
Has abstractyes

Explore more

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