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Record W2099108731 · doi:10.1115/fedsm2006-98355

CFD Modeling of Gas-Liquid Flow and Heat Transfer in a High Pressure Water Electrolysis System

2006· article· en· W2099108731 on OpenAlexafffund
V. M. Agranat, С. В. Жубрин, Aura María, J. Hinatsu, M. Stemp, Masahiro Kawaji

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of TorontoHydrogenics (Canada)
FundersNatural Resources Canada
KeywordsSeparator (oil production)MechanicsComputational fluid dynamicsMaterials scienceHeat transferBubbleVolumetric flow rateThermodynamicsVolume of fluid methodTwo-phase flowMultiphase flowFlow (mathematics)Fluid dynamicsPhysics

Abstract

fetched live from OpenAlex

A high-pressure water electrolysis system has been investigated numerically and experimentally. The advanced CFD model of two-phase flow, which calculated the 3D distributions of pressure, gas and liquid velocities and gas and liquid volume fractions, has been developed to account for all the major components in the system, and appropriate constitutive equations for two-phase flow parameters were selected for various parts of the system, such as the cell stack, riser, separator and downcomer. Heat transfer between the two phases, and between the gas-liquid mixture and cooling coils located in the gas-liquid separator was also accounted for. The model was validated using comparisons of predicted liquid flow rate with the liquid flow rate measured in the downcomer, where a single-phase liquid flow existed. The effects of pressure, current density, number of cells, and bubble size were investigated with the numerical model. The numerical predictions matched the general trends obtained from the experimental results with regard to the effects of pressure and current density on the liquid flow rate. The validated CFD model is being used as a cell design tool at Hydrogenics Corporation.

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 score0.174
Threshold uncertainty score0.314

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 head0.002
GPT teacher head0.140
Teacher spread0.138 · 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
Published2006
Admission routes2
Has abstractyes

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