Transportation of high concentration suspension using \nvisco-plastic lubrication
Bibliographic record
Abstract
Surface engineering plays an important role in different industries. Enhancing the surface properties while keeping the base material properties is the ultimate goal of surface engineering. If the coating is manufactured by melting the particles with heat, it is called thermal spraying. Suspension plasma spraying (SPS) is a branch of thermal spraying where the coating material is suspended in a base liquid. The suspension is injected to a plasma that melts the particles. These molten particles strike the substrate, solidify and form the coating. One of the main challenges in SPS is clogging the suspension in the feeding line. Interaction of the particles with the tube wall may result in sedimentation (fouling) and reduction in the passing area through the tube and finally blockage of the suspension flow. In this case, the spraying process should be stopped and further actions need to be taken for cleaning the suspension line and the injector. The other challenge is the speed of the spraying. SPS is considered as a slow process compared to other conventional thermal spraying methods. Typically, suspensions have concentration of about 10-25 wt. % which means only 10-25 % of the total mass of suspension is made of the particles that actually contribute to the coating. In this study, visco-plastic lubrication is introduced to avoid clogging while increasing the concentration of the suspension up to 70 wt. %. The key element in visco-plastic lubrication is utilizing a yield stress fluid as the lubricant. Yield stress materials behave like fluids if they are submitted to a stress higher than a threshold called yield stress. If the stress is less than that, they behave like solids. In visco-plastic lubrication, a solid protective layer around the core fluid is formed that keeps the core fluid at the center and does not let it touch the tube wall. In this combined experimental and numerical study, the conditions to establish a stable core-annular flow of suspension-yield stress fluids were determined to transport high concentration suspensions.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".