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Record W2100346319 · doi:10.1029/2009jb007047

Permeability of vesicular Stromboli basaltic glass: Lattice Boltzmann simulations and laboratory measurements

2010· article· en· W2100346319 on OpenAlexaff
Li‐Ping Bai, Don R. Baker, Reghan J. Hill

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBubblePorosityLattice Boltzmann methodsPermeability (electromagnetism)BasaltGeologyPorous mediumMineralogyMechanicsMaterials sciencePhysicsGeotechnical engineeringChemistryGeochemistry

Abstract

fetched live from OpenAlex

The permeabilities of vesicular Stromboli basaltic glasses were determined using lattice Boltzmann (LB) simulations and laboratory measurements. Lattice Boltzmann simulations were performed to simulate flow through vesicular Stromboli basaltic glasses with porosities, Φ, from 5% to 92%. The simulations and measurements provide a power law Darcian permeability‐porosity relationship k(Φ) = c (Φ)5 with c = 2.35 × 10−20 from LB simulations and 5.33 × 10−21 from measurements, where k is in m2. These permeabilities of vesiculated basalts are about 1 to 2 orders of magnitude higher than in rhyolitic and dacitic volcanic rocks with the same porosity; this difference is attributed to a higher bubble interconnectivity and larger bubble apertures in our basaltic samples. The Darcian flow permeability k1 (m2) and non‐Darcian flow permeability k2 (m) are highly dependent on bubble size, D, and porosity with k1 = 7.66 × 10−17[D2Φ3/(1 − Φ)2] and k2 = 2.78 × 10−9[DΦ3/(1 − Φ)]. Samples with power law bubble size distributions can produce higher permeabilities than samples with exponential bubble size distributions. The Darcian and non‐Darcian flow regimes are delineated, demonstrating that the Darcian flow occurs at the Forchheimer number, Fo, below 0.2–1, and the transitional flow (Forchheimer flow) occurs in the Forchheimer number range 1 to 10. The correlations between friction factor, fk, and Fo are constrained by the permeability measurements, and are in good agreement with simulations: fk = (1.11 ± 0.17) + [(0.66 ± 0.39)/Fo] (measurements) and fk = (0.59 ± 0.49) + [(1.0 ± 0.01)/Fo] (LB simulations). Our results show that fk depends on k2, pore size, and pore geometry at small Fo and tends to be a constant at large Fo. The fk − Fo correlations imply a gradual transition from Darcian to non‐Darcian flow, rather than an abrupt change. Modeling the relationship between permeability created by water exsolution and depth suggests that significant increases of permeability occur at depths of ∼100–2000 m for melts with initial water concentrations of 1–4 wt %. At these depths, for gas flow through vesicular magma with a velocity 0.1–1 m s−1, Fo is in the range ∼0.5–47, corresponding to the transitional flow regime. For a gas flow with a velocity over ∼10 m s−1, Fo can attain values well above the transition flow regime. Our results imply that transitional flow or turbulent flow probably prevails in vesicular magma.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.335
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2010
Admission routes1
Has abstractyes

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