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Record W4390034445 · doi:10.55908/sdgs.v11i12.1791

Experimental Analysis of the Dual Source Heat Pump with Varying Water Temperature

2023· article· en· W4390034445 on OpenAlexaff
Edwin King Ehiorobo, Adedeji Daniel Gbadebo

Bibliographic record

VenueJournal of Law and Sustainable Development · 2023
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsEmera (Canada)
Fundersnot available
KeywordsCoefficient of performanceHeat pumpAir source heat pumpsRelative humidityPower consumptionGas compressorEnvironmental sciencePower (physics)ThermodynamicsMaterials scienceMechanicsPhysicsHeat exchanger

Abstract

fetched live from OpenAlex

Purpose: The aim is to examine how water temperature influence the coefficient of performance (COP) of a dual-source heat pump (DSHP) and to show how water temperature influence the relationship between heating capacity and power consumption of the DSHP. Methods: An experimental method to examine an installed 5kW DSHP system with an attached converters and other connected components, which arrangement properly controls for the indoor temperature and relative humidity of the laboratory. Results and Conclusions: The findings reflects that the COP of the DSHP pumps increases at low water temperature. As water temperature increases, the heat loss to water and compressor power consumption decreases, and in turn reduce the COP. The average temperature increases from 36.55oC to 59.15oC and COP reduces, by 40%, from 2.83–1.12. Implications: The implication is that the DSHP which is shown to be energy efficient is environmentally friendly, and as a result, would remain very useful in the fifth generation of heating systems with the use of smart technology in future. Originality/Value: The value of the article lies in its focus on the performance of the DSHP as an energy and efficiency device for in heating and cooling in buildings.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.160

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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 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
Published2023
Admission routes1
Has abstractyes

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