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Record W7033936343

Selection of low GWP participating gas for a passive cooling skylight

2024· article· en· W7033936343 on OpenAlexaboutno aff

Bibliographic record

VenueÅbo Akademi University Research Portal · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsnot available
Fundersnot available
KeywordsSkylightSelection (genetic algorithm)Cooling towerVentilation (architecture)
DOInot available

Abstract

fetched live from OpenAlex

Rising temperatures as a result of climate make the cooling of building envelopes and creating thermal comfort for more and more people a challenge while energy use must become more efficient.In addition to active (electricity-driven) systems, passive cooling methods are being developed for night-time as well as day-time cooling of buildings.A passive cooling skylight under development at Åbo Akademi has demonstrated a night-time passive cooling effect of ~100 W/m 2 .Its performance depends strongly on the gas used inside the skylight, that will pick up thermal radiation (at long wavelengths, LW) via a lower window and after a natural convection transfer inside the skylight, release the heat to the sky via an upper window.Proof-of-concept work at ÅA involved comparing the use of air with the performance when using carbon dioxide CO2, ammonia NH3 or pentafluoro ethane, i.e. hydrofluorocarbon refrigerant HFC-125.Best results reported were obtained with the latter, but as a result of the 2016 Kigali Amendment to the 1986 Montreal Protocol on HFCs, an alternative for HFC-125 needs to be found, because of its global warming potential (GWP).With GWP = 1 for CO2, = 0 for NH3, for HFC-125 the GWP = 3500.Since a passive cooling skylight can be ranked as a refrigeration installation it cannot contain a gas with a GWP >150 after 2030 under European regulation.The prime material property of a suitable gas is a high emissivity / absorption in the LW range 8-14 µm, also known as the atmospheric window, where, apart from water vapour, nothing prevents radiative heat transfer from Earth directly to space.The hypothesis was that HFC-152a or HFC-41, with GWP values < 150 could replace HFC-125 for this skylight application.Besides GWP, other features that must not prevent the widespread use of the technology are flammability and chemical / physical stability in general, toxicity and, of course, costs.CFD simulations (Ansys Fluent) were used the calculate the passive cooling (LW) heat fluxes, temperatures, the convection flow field, and the transported heat inside the skylight, comparing the mentioned gases.and The results show that the performance of HFC-152a (117.8W/m 2 ) and slightly less so HFC-41 (115.4W/m 2 ), both with a GWP < 150, can match the performance achieved so far with HFC-125 (117.3W/m 2 ), compared to 100.5 W/m 2 with air.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.049
GPT teacher head0.310
Teacher spread0.262 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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