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Record W1976184747 · doi:10.1080/19401490903046785

Thermal performance modelling of complex fenestration systems

2009· article· en· W1976184747 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Building Performance Simulation · 2009
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsGlazingFacadeFenestrationThermalComputational fluid dynamicsShadingPhotovoltaic systemThermal massThermal comfortSolar gainLouverMechanical engineeringEngineeringComputer scienceSolar energyStructural engineeringAerospace engineeringMeteorologyElectrical engineeringCivil engineering

Abstract

fetched live from OpenAlex

Complex fenestration systems (CFS) have become standard elements in facade design of high performance buildings. They include, for example, shading devices to control illumination, solar heat gains, glare and view-out, and photovoltaic elements imbedded in glazing layers to produce electrical energy on site. However, current methodologies to evaluate the thermal performance of CFS are limited to few products and types. This article develops a general methodology to compute the thermal performance of CFS. The methodology assumes each system layer as porous with calculated effective radiation and thermal properties. A new thermal penetration length model was developed to account for the effects of porous layers on the convective film coefficients of adjacent gas spaces, and applied to various types of shading devices. This methodology is validated using the available measurement and computational fluid dynamics (CFD) simulation results for the U-factor of double-glazed windows with between-pane and internal blinds.

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.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.033
GPT teacher head0.232
Teacher spread0.198 · 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