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A simple method for validating a simulation model of a radiant ceiling panel with thermal energy storage

2021· article· en· W4200223376 on OpenAlexaff
Andrés Gallardo, Umberto Berardi

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCeiling (cloud)HVACRadiant heatingThermal massThermal comfortPhase-change materialThermalEnvironmental scienceNuclear engineeringThermal energy storageMechanical engineeringSimulationMaterials scienceComputer scienceEngineeringAir conditioningThermodynamicsStructural engineering

Abstract

fetched live from OpenAlex

Abstract This paper focuses on validating a simulation model of a radiant ceiling panel (RCP) incorporating phase change materials (PCM) for heating and cooling applications in buildings. The development of an RCP with thermal energy storage capacity aims to encourage high thermal mass radiant systems in existing buildings to replace the traditional all-air HVAC system. First, a heat flow meter (HFM) is used to perform enthalpy measurements at a product scale (macro-encapsulated PCM). Then, a small test chamber is constructed to measure the dynamic thermal performance of an RCP with PCM under well-known and realistic boundary conditions. A known thermal resistance is used to establish a realistic heat transfer coefficient between room air (represented by the temperature of a temperature-controlled metal plate) and ceiling. The results show that HFM enthalpy measurements of products incorporating PCM are within ± 2% of manufacturers’ data. Additionally, results indicate that a test chamber can be used for validating a dynamic simulation model of the RCP with PCM installed in a room. The proposed method can be helpful during the system optimization phase, as many conditions and sample configurations can be tested without spending too much time or money on test rooms or real building monitoring.

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)0.000
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: none
Teacher disagreement score0.617
Threshold uncertainty score0.387

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 communication0.0000.000
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 head0.119
GPT teacher head0.328
Teacher spread0.209 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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