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Record W2787727018 · doi:10.3390/en11020387

Detailed Theoretical Characterization of a Transcritical CO2 Direct Expansion Ground Source Heat Pump Water Heater

2018· article· en· W2787727018 on OpenAlexafffund
Parham Eslami-Nejad, Messaoud Badache, Arash Bastani, Zine Aidoun

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsTranscritical cycleHeat pumpRefrigerantAir source heat pumpsHeat exchangerProcess engineeringEngineeringCoefficient of performanceMechanical engineeringEnvironmental sciencePetroleum engineering

Abstract

fetched live from OpenAlex

A new avenue in modern heat pump technology is related to the use of natural refrigerants such as carbon dioxide (CO2). The use of CO2 in direct expansion ground source heat pumps (DX-GSHP) has also gained significant interest as it offers opportunities for cost reduction of the ground loop, albeit some challenges remain in their development, design and use. To address these challenges and to characterize CO2-DX-GSHP performance for water heating applications, a detailed theoretical model and a fully-instrumented test apparatus was developed and built at CanmetENERGY Research Laboratory. The theoretical model was validated against a set of experimental results and adopted to investigate the performance of the system over a wide operating range. Validation results showed that the model predicts the experimental results within the measurement uncertainty. A detailed system performance analysis was also performed using the theoretical model to understand the system behavior and explore the actions required for performance improvement in future installations. The results of the analysis showed that improper design and control of some components, such as the gas cooler and ground heat exchanger can degrade the system performance by up to 25%, and the heat pump heating capacity by 7.5%.

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 categoriesInsufficient payload (model declined to judge)
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 score0.151
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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.

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

Citations17
Published2018
Admission routes2
Has abstractyes

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