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Record W1963767778 · doi:10.1504/ijex.2014.066610

Exergy analysis of a multi-tank thermal storage system for solar heating applications

2014· article· en· W1963767778 on OpenAlexafffund
Ryan Dickinson, Cynthia A. Cruickshank

Bibliographic record

VenueInternational Journal of Exergy · 2014
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTRNSYSExergyEnvironmental scienceNuclear engineeringVolume (thermodynamics)Work (physics)MechanicsExergy efficiencyCharge (physics)Materials scienceConstant (computer programming)ThermodynamicsThermalComputer sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

This paper presents the results of an experimental study on the stored exergy of a stratified multi-tank system when subjected to various charge and discharge strategies. Tests were performed over 8-h and 48-h test periods, for constant temperature charging, variable input power charging, constant volume hourly draws and variable volume hourly draws. For each set of test parameters, three different charge and discharge configurations were evaluated. Results showed that for constant charge and discharge conditions, the parallel charge and parallel discharge configuration achieved the highest stored exergy value at the end of the test period. Under variable charge and discharge conditions, the series charge and series discharge configuration resulted in higher stored exergy values. This work was supported by computer modelling conducted in the TRNSYS simulation environment.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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