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Record W2948640320 · doi:10.3390/en12112170

Domestic Hot Water Storage Tank Utilizing Phase Change Materials (PCMs): Numerical Approach

2019· article· en· W2948640320 on OpenAlexafffund
Ayman M. Bayomy, Stephen Davies, M. Ziad Saghir

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal energy storageStorage water heaterStorage tankPhase-change materialEnvironmental sciencePhotovoltaic systemThermalEnergy storageNuclear engineeringWater storagePhase changeEnvironmental engineeringWaste managementMaterials scienceProcess engineeringMeteorologyEngineeringWater heaterElectrical engineeringThermodynamicsMechanical engineeringEngineering physics

Abstract

fetched live from OpenAlex

Thermal energy storage (TES) is an essential part of a solar thermal/hot water system. It was shown that TES significantly enhances the efficiency and cost effectiveness of solar thermal systems by fulfilling the gap/mismatch between the solar radiation supply during the day and peak demand/load when sun is not available. In the present paper, a three-dimensional numerical model of a water-based thermal storage tank to provide domestic hot water demand is conducted. Phase change material (PCM) was used in the tank as a thermal storage medium and was connected to a photovoltaic thermal collector. The present paper shows the effectiveness of utilizing PCMs in a commercial 30-gallon domestic hot water tank used in buildings. The storage efficiency and the outlet water temperature were predicted to evaluate the storage system performance for different charging flow rates and different numbers of families demands. The results revealed that increases in the hot water supply coming from the solar collector caused increases in the outlet water temperature during the discharge period for one family demand. In such a case, it was observed that the storage efficiency was relatively low. Due to low demand (only one family), the PCMs were not completely crystallized at the end of the discharge period. The results showed that the increases in the family’s demand improve the thermal storage efficiency due to the increases in the portion of the energy that is recovered during the nighttime.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.051
GPT teacher head0.297
Teacher spread0.246 · 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

Citations37
Published2019
Admission routes2
Has abstractyes

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