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Record W4389987779 · doi:10.1016/j.isci.2023.108765

19th-century thermosiphon ventilation and its potential for heat recovery in buildings today

2023· article· en· W4389987779 on OpenAlexafffundabout
Anna Halepaska, Annmarie Adams, Salmaan Craig

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMcGill University
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill UniversityMcGill University
KeywordsThermosiphonBuoyancyVentilation (architecture)Heat flowParliamentHeat exchangerEngineeringMechanicsMechanical engineeringEnvironmental scienceThermodynamicsThermalPhysicsLawPolitical science

Abstract

fetched live from OpenAlex

Summary A forgotten thermosiphon scheme is found in Montreal's former Royal Victoria Hospital and traced back to the original Center Block of Canada's Parliament Hill. This discovery inspires an investigation into the fluid mechanics of heat recovery with buoyancy ventilation, where interior spaces are arranged in an open thermal loop with heat exchange through partition walls. Flow visualizations with physical models are used to corroborate the archival evidence and show how the historical scheme worked. The scheme is then generalized, defining a criterion for steady unidirectional flow (λ>1) and a heat recovery limit when room temperatures upstream and downstream reach equilibrium (ε≤50%). This mathematical model is validated experimentally, demonstrating steady flow (λ∼2.21) close to the efficiency limit (ε∼0.40) with a balanced thermal design (NTU∼1) . Further analysis shows significant heating savings are possible in mildly cold seasons compared to natural displacement (74%) and natural mixing (60%) ventilation.

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)0.001
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: none
Teacher disagreement score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.008
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.230
Teacher spread0.219 · 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
Published2023
Admission routes3
Has abstractyes

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