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Record W4390912973 · doi:10.3390/app14020759

Water Flow Boiling in Micro/Mini Channels Using Volume of Fluid Model

2024· article· en· W4390912973 on OpenAlexaff
Muhammad Umer Khan Mughal, Khalid Waheed, Muhammad Imran Sadiq, Altaf Hossain Molla, Zambri Harun, Amin Etminan

Bibliographic record

VenueApplied Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVolume of fluid methodMechanicsComputational fluid dynamicsHeat transferHeat exchangerMaterials scienceBoilingMicro heat exchangerAdiabatic processThermodynamicsMechanical engineeringFlow (mathematics)Heat transfer coefficientEngineeringPhysics

Abstract

fetched live from OpenAlex

Recent advancements in computational fluid dynamics (CFD) have triggered research in the field of heat exchangers. Driven by the need to decrease the size of heat exchangers, many researchers have exploited the higher heat transfer achieved by replacing single-phase flow systems with boiling counterparts. The concept of using mini-channels to provide compact heat exchangers while maintaining heat transfer performance is relatively new. A minimal number of researchers have reported simulations of water-steam systems in mini-channels. This paper presents a numerical study of the heat transfer performance (HTP) of mini channels in a water-steam system using the volume of fluid (VOF) model coupled with the Lee phase change model on commercial CFD software ANSYS. The numerical model consisted of a 1 mm × 1.5 mm × 52 mm channel with boundary conditions: top adiabatic; constant heat flux at the bottom surface; left/right periodic; mass flow inlet and pressure outlet. A mesh independence study was carried out for the proposed model, and simulations were validated against the experimental results of heat transfer versus vapor quality for a wide range of mass and heat fluxes. The VOF model best predicts experimental HTC at high mass fluxes, although the results at low mass fluxes were predicted with reasonable accuracy. Based on the agreement of numerical and numerical results, the VOF model turned out to be a promising candidate for designing compact micro/mini channel heat exchangers.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.306

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 head0.032
GPT teacher head0.250
Teacher spread0.218 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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