Effects of Nanoparticles on Friction Reduction in Fluid Flow
Bibliographic record
Abstract
The interaction between a flowing fluid and the solid surfaces over which it flows is fundamental to determining the resistance to flow offered by the solid. This interaction is commonly described as friction or drag in fluid mechanics and is the basis for the no-slip boundary condition in fluid dynamics. The slip behaviour of the fluid near the solid boundary, commonly quantified as the "slip length," is often used to quantify friction reduction. Nanoparticles (NPs) have been proposed to influence the fluid flow in reservoirs through several mechanisms, including modifying the slip behaviour of a fluid. This thesis investigates whether coating the surface of channels in glass micromodels with silica NPs affects the slip length of oil flow and water flow. Silica NPs with different shapes, surface coating and charges were tested to understand how the nature of these nanomaterials can affect friction. In-line coating, immersion, and spin coating were evaluated to determine how effectively each method coated the surface of the channel with NPs. Particle deposition was evaluated by water droplet contact angle measurement, scanning electron microscope (SEM) imaging, and elemental analysis. A uniform, crack-free, and stable distribution of NPs on the surface was observed using spin coating. Hydrophilic silica NP coating affected water differently from oil, causing a reduction in friction while oil flooding but an increase in friction for water. On the other hand, partially hydrophobic silica NPs reduced the friction for both water and oil flooding. The fundamental understanding of how NPs can be used as friction reducers for oil production will open new opportunities for designing low-energy and more sustainable oil production methods.
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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 | 0.000 |
| 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) | 0.001 | 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".