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Record W2185384308

Accuracy Improvement for Measurement of Gas Diffusivity through Thin Porous Media

2012· dissertation· en· W2185384308 on OpenAlexfundno aff
Lu Dong

Bibliographic record

VenueUWSpace (University of Waterloo) · 2012
Typedissertation
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal diffusivityDimensionless quantityPorous mediumDiffusionPorosityFourier numberGaseous diffusionMaterials scienceThermodynamicsMechanicsChemistryPhysicsHeat transfer coefficientHeat transferComposite material
DOInot available

Abstract

fetched live from OpenAlex

Accurate measurement of the gas diffusion coefficient through porous media is of significant interest to science and engineering applications including mass transfer through soils, building materials, and fuel cells to name a few. Accurate measurements are necessary for simulation and optimization of complex systems involving gas transport. The Loschmidt cell, or closed tube method has been extensively used to measuring the binary gas diffusion coefficient of gas pairs. Recent studies have used a modified Loschmidt cell with an additional porous sample to measure the effective diffusion coefficient through the porous sample. The method employs what is called the resistance network method for calculating the effective diffusion coefficient through the porous sample. \n \nIn this study, a one-dimensional simulation was developed to evaluate the accuracy of the resistance network method with a modified Loschmidt cell. Dimensionless parameters are shown to be applicable for both the conventional Loschmidt cell as well as the modified Loschmidt cell with the porous sample. A parametric simulation study was performed to show that the error relates closely to the ratio of diffusive resistances of the sample and bulk gas denoted as the resistance ratio, Ω*. With a simulated experimental duration of 250s, which is typical of experiments in literature, the error was found to be negligible when Ω* < 0.1 but increased dramatically for Ω* > 0.1 up to a maximum of approximately 20% error. The equivalent Fourier number, Fo_eq, based on the equivalent diffusivity, D_eq, was proposed as an approximate expression for the degree to which the concentration gradient in the test cell has evolved. It was found that the error has nearly a linear relationship with Fo_eq. Since a lower Fo_eq means a less decayed profile with significant transience remaining, as Fo_eq drops, the the error increases. By controlling the simulation test length for different thickness and diffusivity samples such that Fo_eq = 12.5, the error was reduced to less than 1% over most of the range of parameters and less than 6% over the full range of parameters spanning two orders of magnitude for both thickness and diffusivity. \n \nThe resistance network method requires the measurement of the sample thickness, a diffusion length, and two diffusion coefficients using with the modified Loschmidt cell (one with the porous sample and one without). Analysis found that the equation used for calculating the effective diffusion coefficient, D_eff, through the porous sample inherently magnifies the relative uncertainty of the measured values in the final calculated value for D_eff. When Ω* < 1, the percentage uncertainty in both diffusion coefficient measurements could potentially be magnified by one or more orders of magnitude. To mitigate uncertainty in D_eff, Ω* must be greater than 1 to ensure that the uncertainty is magnified by no more than a factor of 2. \n \nThis study recommends that modified Loschmidt experiments aim for Ω* = 1 and Fo_eq = 12.5 to greatly reduce the error and uncertainty in the measurement of D_eff.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.209
Teacher spread0.190 · 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 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

Citations3
Published2012
Admission routes1
Has abstractyes

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