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Record W4242136068 · doi:10.5383/ijtee.10.02.012

Parametric Quantification of Low GWP Refrigerant for Thermosyphon Driven Solar Water Heating System

2015· article· en· W4242136068 on OpenAlexvenueaboutno aff
Naeem Abas, Nasrullah Khan

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantThermosiphonEnvironmental scienceHeat pumpExergy efficiencyProcess engineeringThermodynamicsMeteorologyExergyEngineeringHeat exchangerMechanical engineeringGeography

Abstract

fetched live from OpenAlex

Modern lifestyle, industrialization and economy thrive on energy which is getting expensive overtime. Thermosyphon based systems are getting attraction for their promising heat transfer efficiency and zero energy utilization. Refrigerants having ozone depletion potential (ODP) and high Global Warming Potential (GWP) have been banned or under time bared permission under Montreal (1987) and Kyoto (1997) protocols. We have devolved a Refrigerant Parametric Quantification (RPQ) method for the choice of optimal refrigerant for density driven solar water heaters. A set of 29 refrigerants are simulated Using REFPROP under various temperature and pressure conditions. The optimal parameters of thermosyphon system are identified from governing equations, international environment safety protocols and open literature. The proposed RPQ method shows most appropriate refrigerant for given temperature range. In second part, the proposed system is simulated in TRNSYS using forced circulation method. In the end, a glass evacuated tube collector is developed and tested on the principal of thermosyphon, employing the best refrigerant emerged from simulation study.

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: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.285

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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
Published2015
Admission routes2
Has abstractyes

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