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Record W4412594644 · doi:10.1038/s41598-025-10620-y

Numerical studies on thermo-hydraulic performance of solar air heater with quarter circle roughness ribs

2025· article· en· W4412594644 on OpenAlexaboutno aff
Yaregal Eneyew Bizuneh, Tazebew Dires Kassie, Atalay Enyew Bizuneh

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Rib cageMechanical engineeringMechanicsMeteorologyEngineeringStructural engineeringPhysicsGeographyArchaeology

Abstract

fetched live from OpenAlex

With their diverse range of applications, solar air heaters transform renewable solar energy into useful heat. The efficiency of solar air heater can be enhanced by exploring the effects of novel rib configurations. Despite the extensive work done so far on roughened solar air heaters, insufficient attention has been paid to the unique geometric features and potential advantages of quarter-circle ribs with respect to improving heat transfer efficiency. The fluid flow and heat transfer properties of a roughened solar air heater with quarter-circle-shaped ribs were examined in-depth using numerical analysis to improve the efficiency. The k-ε RNG turbulence model was used to conduct 2D steady-state numerical simulations, and the findings showed excellent agreement with the smooth duct and related literatures. The impact of rib spacing was explored by changing the rib relative pitch (p/e) from 6.67 to 13.3 for Reynolds range of 4000-20,000. The thermo-hydraulic performance factor was found to be 1.63. Additionally, it was shown that an increase in relative pitch (p/e) of 6.67 to 10 resulted in an increase in the Nusselt number for all Re values examined. At Re of 16,000, an enhancement of 2.42 times the Nu was made for p/e = 6.67. It was also noted that for all Re values taken into consideration, Nu falls with an increase in p/e from 10 to 13.3.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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 designBench or experimental
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

Citations3
Published2025
Admission routes1
Has abstractyes

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