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Record W4210851872 · doi:10.32920/ryerson.14638482.v1

Resistor-network formulation of forced convection in asymmetrically-heated channels - hydrodynamically-developed laminar flow

2021· preprint· en· W4210851872 on OpenAlexfundno aff
David Naylor, John L. Wright

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsLaminar flowMechanicsForced convectionNusselt numberResistorHeat transferWork (physics)ConvectionFlow (mathematics)Convective heat transferNatural convectionThermodynamicsChannel (broadcasting)Materials scienceComputer sciencePhysicsReynolds numberTelecommunications

Abstract

fetched live from OpenAlex

The computation of forced-convective heat transfer from the walls of an asymmetrically heated channel to the fluid passing through in a laminar, hydrodynamically-developed flow is known as the asymmetric Graetz problem. Several analytical and numerical solutions for this problem have been published and many variations and extensions have been investigated. Recently, there has been a renewed interest in this problem due to its applications in emerging areas such as microchannels and fuel cells. In the present work, the asymmetric Graetz problem is examined in a resistor-network framework. The formulation of the problem in terms of three convective resistances leads to temperature-independent Nusselt numbers that are free of the singularities found in previous results. The proposed approach also offers more detail regarding the split of heat transfer between the channel walls and the flow. This work is part of an ongoing project on resistor-network modeling and characterization of multi-temperature convection problems.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.220
Teacher spread0.206 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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