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Record W2038646562 · doi:10.1115/imece2010-39004

Investigation of Heat Transfer in Microchannels With Asymmetric and Periodic Slip at the Walls

2010· article· en· W2038646562 on OpenAlexaff
Akshay C. Gunde, Suman Chakraborty, Sushanta K. Mitra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrochannelSlip (aerodynamics)MechanicsMaterials scienceMicrofluidicsFluid dynamicsHeat transferFabricationSiliconBoundary value problemMechanical engineeringNanotechnologyThermodynamicsEngineeringOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Recent development of microfluidic applications in various areas like cooling of silicon chips, VLSI, aircraft avionics, X-Ray and laser equipments has led to an increased study of coupled fluid flow and heat transfer in microchannels. A major issue in the mathematical modeling of these phenomena is the applicability of the no-slip boundary condition at solid-fluid interfaces. Most of such micro-scale investigations consider a slip velocity at solid boundaries, which has been observed in a number of experiemntal studies [1], [2]. In cases involving the heating of substrate and/or transport fluid, definite formation of nanobubbles from the fluid has been established [3]. These bubbles migrate to the channel walls and deposit on them in the form of random clusters. As a result, due to the minimized shear resistance offered by the surfaces of such bubbles to the fluid, variation in slip length is observed along the channel walls. Hence, in order that theoretical studies lead to physically acceptable results required for the fabrication of microfluidic devices, such variation of slip length encountered by a fluid in a microchannel must be included in these analysis.

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 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 score0.303
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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
Published2010
Admission routes1
Has abstractyes

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