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Record W3189402753 · doi:10.3390/en14164915

Assessing the Energy, Indoor Air Quality, and Moisture Performance for a Three-Story Building Using an Integrated Model, Part Two: Integrating the Indoor Air Quality, Moisture, and Thermal Comfort

2021· article· en· W3189402753 on OpenAlexaffabout
Seyedmohammadreza Heibati, Wahid Maref, Hamed H. Saber

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsIndoor air qualityAirflowEnvironmental scienceInfiltration (HVAC)Thermal comfortVentilation (architecture)Natural ventilationMoistureAir quality indexRelative humidityEnvironmental engineeringMeteorologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, an integrated model that coupled CONTAM and WUFI was developed to assess the indoor air quality (IAQ), moisture, and thermal comfort performance. The coupling method of CONTAM and WUFI is described based on the exchange of airflow rate control variables as infiltration, natural and mechanical ventilation parameters between heat and moisture flow balance equations in WUFI and contaminant flow balances equations in CONTAM. To evaluate the predictions of the integrated model compared to single models of CONTAM and WUFI, four scenarios were used. These scenarios are airtight-fan off, airtight-fan on, leaky-fan off, and leaky-fan on, and were defined for a three-story house subjected to three different climate conditions of Montreal, Vancouver, and Miami. The measures of the simulated indoor CO2, PM2.5, and VOCs obtained by CONTAM; the simulated indoor relative humidity (RH), predicted percentage of dissatisfied (PPD), and predicted mean vote (PMV) obtained by WUFI; and those obtained by the integrated model are compared separately for all scenarios in Montreal, Vancouver, and Miami. Finally, the optimal scenarios are selected. The simulated results of the optimal scenarios with the integrated model method (−28.88% to 46.39%) are different from those obtained with the single models. This is due to the inability of the single models to correct the airflow variables.

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 categoriesMeta-epidemiology (narrow)
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 score0.096
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.047
GPT teacher head0.308
Teacher spread0.261 · 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.

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

Citations13
Published2021
Admission routes2
Has abstractyes

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