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Record W2975478428 · doi:10.18280/mmep.060313

Advances of Nanofluids in Solar Collectors - A Review of Numerical Studies

2019· review· en· W2975478428 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2019
Typereview
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidMaterials scienceEngineering physicsEnvironmental scienceNanotechnologyEngineeringNanoparticle

Abstract

fetched live from OpenAlex

This paper presents a detailed review of the numerical studies carried out by various researchers in order to obtain enhanced heat transfer in free, forced, and mixed convection, under laminar, transition, and turbulent flow regimes, by using nanofluids in different solar collector geometries. Recently, nanofluids have been increasingly used in various solar collector configurations. Nano-sized metallic or non-metallic particles such as Cu, Au, Al2O3, SiO2, TiO2, CuO, etc, were used in the heat transfer fluid for various solid volume fractions. The average size of the particles was less than 100 nm. The higher conductivity of nanoparticles even at low particle concentration results in higher thermal conductivity of the base fluid and improves the thermal characteristics of the system. Nanoparticle size, type and shape are important factors for the thermal conductivity enhancement of the nanofluid with nanoparticles.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.082
GPT teacher head0.304
Teacher spread0.222 · 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