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Record W1980957868 · doi:10.1299/mej.14-00338

Performance evaluation of a plate type condenser for heat pump water heater

2014· article· en· W1980957868 on OpenAlexaff
Hitoshi Asano, Kazuya Honda, Nobuhiro Takeda, Masaki Kondo, K. Nishimura

Bibliographic record

VenueMechanical Engineering Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsRefrigerantHeat transferHeat exchangerMechanicsMaterials scienceCondenser (optics)Plate heat exchangerThermodynamicsAir source heat pumpsMicro heat exchangerSubcoolingPlate fin heat exchangerWater chillerOpticsPhysics

Abstract

fetched live from OpenAlex

This study deals with an application of blazed plate heat exchangers to a condenser used in a heat pump water heater. The heat transfer performance was experimentally evaluated by using a pump driven two-phase flow loop in the operating condition for water heating at the environmental temperature. The effects of number of refrigerant paths and setting orientation, such as a vertical orientation with downward refrigerant flow or a horizontal orientation, were evaluated. HFC134a was used as the refrigerant. Superheated vapor was supplied to the condenser, and heated water, and then subcooled liquid was exhausted. Water was supplied to form a counter flow heat exchange. Three kinds of heat exchangers with different refrigerant paths of 6, 10, and 14 were used. Refrigerant temperatures at the exit of each path were measured by inserted thermocouples to evaluate the flow distribution. As a result, it was shown that vertical orientation produced higher heat transfer rate than the horizontal orientation. For the horizontal orientation, heat transfer rate decreased with an increase of refrigerant channels from 10 to 14. The reason would be due to a maldistribution of the refrigerant. The deterioration in heat transfer performance for the horizontal orientation could be improved by the inclination of 15° of the heat exchanger.

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)0.001
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: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.224
Teacher spread0.206 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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