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Record W2023889964 · doi:10.11159/jffhmt.2014.006

Numerical Analysis of Heat Transfer Performance of Flat Plate Solar Collectors

2014· article· en· W2023889964 on OpenAlexvenueno aff
E. Ekramian, S.Gh. Etemad, Masoud Haghshenasfard

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2014
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsMolar absorptivityMaterials scienceTube (container)ThermalGlass tubeOpticsHeat transferNanofluids in solar collectorsThermal efficiencyComposite materialMass flow rateVolumetric flow rateMechanicsPhotovoltaic thermal hybrid solar collectorPhysicsChemistryThermodynamics

Abstract

fetched live from OpenAlex

In this study, numerical analysis was used to investigate the effect of different parameters on thermal efficiency of flat plate solar collectors. Various geometries were examined in order to assess the influence of geometrical characteristics and operating conditions on thermal efficiency of solar collectors. Important parameters such as absorber thickness, riser position, shape of tube cross section, absorber material, absorber absorptivity, glass transmissivity, and mass flow rate have been investigated. Results show that the efficiency of collector with risers on top of the absorber plate is 4.2% more than that of the collector with risers on bottom. Also the tube cross-sectional geometry shows strong effect on the efficiency e.g. the efficiency of collectors with circular tubes is 38.4% more than that of collectors with triangular cross sections. Thermal efficiency of solar collectors increases with increasing the fluid flow rate, plate absorptivity, absorber thickness, and glass transmissivity.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations68
Published2014
Admission routes1
Has abstractyes

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