Monte Carlo Simulations to Determine Effective Gas Diffusivity of an Actual Porous Medium Image Obtained From Focused Ion Beam Scanning Electron Microscopy
Bibliographic record
Abstract
Computer simulation methods have been widely used to study the effective transport properties of porous media. Simulation methods such as Monte Carlo (MC) have been used to characterize the randomness associated with such media. Most of these simulations have been carried out in artificially generated porous media represented as random pack of spheres or randomly arranged cylindrical fibers. In the present work, we apply MC simulation to images of an actual porous medium, obtained by using Focus Ion Beam Scanning Electron Microscopy (FIB SEM). The porous medium under consideration is the porous electrode of Solid Oxide Fuel Cell (SOFC). The effective gas diffusivity of the SOFC electrode is calculated based on mean square displacement method. The simulations are performed initially on a 2D image and are further carried out on a 3D image. For the validation of the numerical method, the porosity of the electrode is calculated and is used for the computation of the effective diffusivity by Bruggeman equation. In such case, the porosity is calculated by Hit and Miss MC approach and is further validated with the porosity values available in the literature. This work also addresses the effect of tessellation of solid surfaces in discrete geometries on the calculation of effective transport properties. Such computation of effective properties from the actual porous media geometry can be further used in the cell modeling and optimization of different components of the SOFC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".