Two-phase volume-averaged predictive theory of dilute ferrofluid spin-up flow in a rotating magnetic field
Bibliographic record
Abstract
We have developed a parameter-free, two-phase, volume-averaged approach to predictively describe the spin-up flow of dilute, cluster-free ferrofluids excited by low-frequency rotating magnetic fields. Predictive validation of the model was performed through a thorough comparison with local velocity profile measurements, and it demonstrated its ability to capture the spin-up flow dynamics without the need for parameter tuning by carefully delineating the validity domain of the ferrofluid dilutedness conditions. To gain insight into the underlying flow mechanisms, we performed a systematic parametric analysis examining the effects of the induced magnetic field, the dipolar interactions between magnetic nanoparticles and the demagnetizing field. How these mechanisms shape the flow of dilute ferrofluids excited by low-frequency rotating fields in a standard spin-up flow geometry has been addressed using probabilistic nanoparticle orientational dynamics, combining Faxén's laws and the Smoluchowski equation to describe the transport of particle magnetic moments. Our findings revealed that the induced magnetic field is the primary driving force of ferrofluid spin-up flow. The dipole interactions and demagnetizing field, on the other hand, contribute only as secondary phenomena to the overall flow behaviour. Furthermore, we have discussed the potential extension of the two-phase approach, in particular with respect to the formation of chain-like aggregates under the influence of strong magnetic fields. Overall, our study provides valuable insights into the complex dynamics of ferrofluid flow and contributes to a comprehensive understanding of the key mechanisms governing the spin-up flow of dilute ferrofluids excited by low-frequency rotating magnetic fields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".