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Record W4405014796 · doi:10.1080/15435075.2024.2428995

Thermodynamic analysis and performance evaluation of a parabolic trough collector receiver with external annular fins

2024· article· en· W4405014796 on OpenAlexfundno aff
Jacob Mator Aketch, Tunde Bello‐Ochende

Bibliographic record

VenueInternational Journal of Green Energy · 2024
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
FundersMastercard Foundation
KeywordsParabolic troughTrough (economics)MechanicsEnvironmental scienceMaterials scienceEngineeringMechanical engineeringPhysicsElectrical engineeringSolar energyEconomics

Abstract

fetched live from OpenAlex

This paper investigates the influence of external annular fins on the thermodynamic performance of parabolic trough collector (PTC) receiver. The objective is achieved by developing thermal and fluid flow models with geometry of varied fin lengths and fin numbers and implemented in ANSYS Fluent software. With an assumption of evacuated annulus space, measured uniform heat fluxes were applied on respective halves. The results show an increase in thermal efficiency by 2.26% for Tin = 350 K, 2.21% for Tin = 400 K, and 2.22% for Tin = 500 K for varying Reynolds numbers and fin lengths, t = 5 mm. It is also observed that entropy generation reduces by 1.44% for Tin = 350, Re = 5000, and t = 5 mm. Low turbulence records higher heat transfer irreversibility as measured by the Bejan number. Exergy efficiency positively responds with an increase of 2.44% for Re = 5000, 2.30% for Re = 10000, and 2.30% for Re = 20000 for t = 5 mm. Due to the observed improvement, this passive enhancement technique can be utilized to design a high performing receiver system, thus maximizing the availability and reliability of PTC system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.257
Teacher spread0.242 · 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
Published2024
Admission routes1
Has abstractyes

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