Pore fabric anisotropy: testing the equivalent pore concept using magnetic measurements on synthetic voids of known geometry
Bibliographic record
Abstract
We present an experimental and modelling study of pore fabric anisotropy using the method of anisotropy of magnetic susceptibility (AMS) applied to synthetic void spaces of known dimensions saturated with a high susceptibility magnetic ferrofluid. We analysed the data using the equivalent pore concept (EPC) proposed by Hrouda et al., who consider the theoretical demagnetization factors of an ellipsoid in order to relate physical pore fabric to magnetic measurements of lineation, foliation and bulk anisotropy. To test this theory, synthetic samples were prepared from cylindrical polycarbonate blanks, 25 mm in diameter by 22 mm long. A variety of ‘special fabrics’ were prepared by machining internal void spaces of: (a) a quasi-spherical fabric comprising a cylinder 10 mm in diameter by 8.8 mm long, (b) a capillary-like fabric comprising a set of 19 equally spaced holes, (c) a bedding-like fabric comprising a linear row of five larger diameter holes and (d) a crack-like fabric comprising a stack of four penny-shaped voids. A second set of quasi-spheroidal fabrics were prepared by machining a hemispherical cutter to different depths into the blanks. Eight samples were prepared with principal axial to radial axis ratios (a/r) from 0.75 to 1.3 (i.e. from oblateness through sphericity to prolateness). With the exception of the quasi-spherical fabric, the ‘special fabrics’ exhibit high anisotropy, with a maximum foliation of 1.41 and a maximum lineation of 1.29. Using a ferrofluid with a fixed intrinsic susceptibility of 1.09 SI, the quasi-spheroidal shape effect is investigated with change in value of the a/r ratio. As the a/r ratio increases, foliation decreases and lineation increases, reflecting the change from an oblate to a prolate fabric. The EPC is then used to estimate the physical void anisotropy from the magnetic measurements of lineation and foliation for direct comparison with the known geometry. Overall, the EPC method makes a reasonable job of estimating the void geometry, but it underestimates the physical void anisotropy by an average of about 8 per cent. We, therefore, report the effect of varying the intrinsic susceptibility of the ferrofluid on a void with a constant a/r ratio of 1.2. As ferrofluid concentration is increased, the EPC predicted void geometry converges to the known physical void geometry. However, even for the highest intrinsic susceptibility ferrofluid used (3.34 SI) the EPC underpredicts the known void anisotropy. We, therefore, propose a simple, empirical correction factor that allows the EPC method accurately to predict real physical void space anisotropy from AMS measurements.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".