SPH as an Inverse Numerical Tool for the Prediction of Diffusive Properties in Porous Media
Bibliographic record
Abstract
Heat and mass transfer and fluid flow in porous media are usually characterized by, or associated with, the effective thermal conductivity, the effective mass diffusivity and the permeability, respectively. All these macroscopic quantities are conceptually established on a phenomenological “equivalence” basis. They may contain the influence of porous micro-structures upon the corresponding diffusive process; however, the detailed nature inside the porous medium is lumped and neglected. Pore scale numerical modelling has the potential of providing adequate meso-/micro- scale insight into the transport process in porous medium, as well as obtaining macroscopic properties, which can encompass the complex pore-structure details. Modelling heat/mass transfer and fluid flow in complicated porous micro-structures presents a major challenge to numerical methods due to their multiscale and multiphysics nature. A relatively-novel numerical technique - the meshless Lagrangian-based Smoothed Particle Hydrodynamics (SPH) method is thought to be capable of making a significant contribution to this research field. This work deals primarily with the SPH modelling of heat conduction and fluid flow in 2-D isotropic porous media. The porous matrix is formed by randomly including a different component into a base component. Various pore-structures are realized by changing the inclusion shape/size, or the relative arrangement condition between inclusions. Pore-scale heat transfer and fluid flow streams are visualized, and both heat transfer and fluid flow always follow, as expected, the paths of least resistance through the porous structures. In what concerns the effective thermal conductivity, for the porous media with the base component of larger bulk thermal conductivity, the “flexible” EMT model, which can accommodate, to some extent, the influence from the porous micro-structures on the effective thermal conductivity by adjusting the so-called flexible factor ff, gives effective thermal conductivities agreeable to the SPH predictions across the whole composition range if ff is taken to be ~ 4.5; the effective thermal conductivity shows a weak dependence on the inclusion shape/size and the relative arrangement condition between inclusions; however, for porous media with dispersed inclusions, which component has larger bulk thermal conductivity presents a strong effect upon the effective thermal conductivity. The SPH fluid flow simulation results confirm the macroscopic Darcy’s law to be valid only in the creeping flow regime; the dimensionless permeability (normalized by the squared characteristic dimension of the inclusions) is found to have an exponential dependence on the porosity within the intermediate porosity range, and the derived dimensionless permeability /""porosity relation is found to have only a minor dependence on either the relative arrangement condition between inclusions or the inclusion shape/area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".