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Analysis of the energy and thermal performance of a radiant cooling panel system with integrated phase change materials in very hot and humid conditions

2019· article· en· W2981612770 on OpenAlexaff
Andrés Gallardo, Umberto Berardi

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnergy consumptionPhase-change materialEnvironmental scienceRadiant coolingThermal comfortVentilation (architecture)Energy (signal processing)Radiant heatingEfficient energy useThermalRadiant energyHVACAutomotive engineeringNuclear engineeringMeteorologyAir conditioningEngineeringMaterials scienceMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract This study evaluates the energy and thermal performance of a radiant cooling panel system with integrated phase change materials (PCMs) for application in building retrofit projects. The focus is on the energy saving potential of using two different strategies to avoid condensation in very hot and humid climate conditions. The cooling energy consumption of a DOE medium office prototype building model with a conventional all-air system is used as a baseline for comparing the proposed configurations. The results show that the radiant system yielded energy savings of around 48% compared to the all-air system for existing office buildings. Results also show that the proposed system is able to operate only during the night-time, which improves the plant efficiency. Thus, the energy savings are mainly due to a reduction in the energy consumption of the cold generation system and lower energy use for ventilation compared with conventional air systems.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.232
Teacher spread0.201 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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