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Record W4320526982 · doi:10.1166/jon.2023.1954

Thermo-Hydraulic Performance of Mini Channels in the Presence of Nanoparticles Phase Change Material Slab

2023· article· en· W4320526982 on OpenAlexaff
M. Ziad Saghir, M.M. Rahman

Bibliographic record

VenueJournal of Nanofluids · 2023
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrochannelMaterials scienceMechanicsReynolds numberNanofluidSlabPhase-change materialFlow (mathematics)Volumetric flow ratePhase (matter)Finite element methodHydraulic diameterThermodynamicsHeat transferPhase changePhysicsStructural engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, we investigate the effectiveness of combining mini-channel configurations in the presence of phase change material slabs. Different phase change material slabs connected with four mini channels were studied numerically. The Navier-Stokes and energy equations for the flow together with the energy equation for the phase change material considering the two-phase system were solved numerically using the finite element technique. Amongst the parameters investigated in this analysis is the Reynolds number, or in other terms, the flow rate. It is found that heat extraction continues as the flow rate within the microchannel increases until the velocity and thermal boundary layers have fully developed. When these layers are fully grown, adding slabs of phase change materials to the system allows for even more heat extraction. Therefore, a combination of mini-channel and phase change material is the best solution for combined heat extraction from a hot surface. This is especially true for circulating flows near the creeping flow with a low Reynolds number.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.344

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 science0.0010.000
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 head0.071
GPT teacher head0.315
Teacher spread0.244 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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