Heat Transfer Enhancement Using Ferrofluids
Bibliographic record
Abstract
In this paper we demonstrate how ferrofluids (magnetic nanofluids) can be used to significantly enhance heat transfer rates in microchannels to help meet the ever-growing demand for high heat flux removal. There are a few mechanisms that can be used to aid mixing and to enhance heat transfer in inherently low Reynold number flow in microchannels. Firstly, the addition of a small amount of the thermally conductive paramagnetic particles can increase the thermal conductivity of the base fluid. For example, a solution of 5% particles can increase the thermal conductivity by approximately 10%, with only a 4% increase in dynamic viscosity. With the application of a magnetic field, the effective thermal conductivity can be increased further in the direction of the field, due to the formation of particle chains. Heat transfer can be additionally enhanced by applying a magnetic field to disrupt the flow, but a non-uniform magnetic field is required. Two-phase slug flow using a plug of ferrofluid in an immiscible liquid can have a large and controllable effect on the heat transfer rate.
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".