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Record W2020445876 · doi:10.1115/fedsm-icnmm2010-31129

Modeling Laminar Fluid Flows in Rectangular Vapor Grooves of Evaporators Used in Loop Heat Pipes

2010· article· en· W2020445876 on OpenAlexaff
Nirmalakanth Jesuthasan, B. R. Baliga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsLaminar flowPressure dropMechanicsMaterials scienceHeat transferFluid dynamicsWorking fluidElectronics coolingThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Loop heat pipes (LHPs) are devices in which capillary forces in a wick and liquid-vapor phase-change phenomena are used to achieve continuous and relatively high rates of transfer of thermal energy from a heat source to a heat sink. Quasi one-dimensional models of the fluid flow and heat transfer within LHPs, with empirical correlations as inputs, are commonly used as the basis of cost-effective computer simulations for the design and optimization of these devices for specific applications. The focus in this work is on laminar fluid flows in straight rectangular vapor grooves of flat evaporators used in LHPs. The pressure drops for such fluid flows are computed in available quasi one-dimensional models of LHPs using correlations for a friction factor that applies strictly only in the fully-developed region of flows in straight rectangular ducts with impermeable walls. The resulting errors can become serious if the pressure drop in the vapor grooves is a significant contributor to the overall pressure drop in the LHP. Thus, to enhance the capabilities of current quasi one-dimensional models of LHPs, more accurate correlations for predicting the aforementioned pressure drop are needed. In this work, a three-dimensional parabolic finite volume method is used to simulate laminar Newtonian fluid flows in straight rectangular vapor grooves of flat evaporators, for a representative range of LHP operating conditions. The mathematical model, computational methodology, results, and suitable correlations for the pressure drops are presented and discussed in this paper.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.608

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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