Comparison of Erosive Wear on Composite and Metallic Propellers in Slurry
Bibliographic record
Abstract
The aim of this study was to investigate the influence of material, geometry, and particle concentration on the wear behaviour of propellers.Particular attention was given to the performance of a modified polyamide material called "Physinit" in comparison to stainless steel.Four test series were carried out using a half factorial design of experiments without repetition.The propellers were rotated in an aqueous slurry containing silicon carbide particles at a constant speed of 1493 rpm.The varied factors included propeller material (stainless steel vs. polyamide), geometry (Phantom vs. three-blade), and particle concentration (80 g/l vs. 200 g/l).The primary measured parameters were relative mass loss and change in diameter.Stainless steel showed higher mass loss and greater diameter reduction compared to the Physinit material.Increasing particle concentration led to increased wear.Wear rates were higher for stainless steel across both geometries.Diameter reduction in stainless steel occurred continuously, while Physinit exhibited delayed and lower reduction.Geometry had a noticeable effect on the uniformity of wear, though its overall influence was less pronounced than that of the material.The experimental data indicate that Physinit demonstrates a distinctly different wear behaviour under abrasive conditions compared to stainless steel.Under the given test conditions, the wear rate of the Physinit propeller was significantly lower than that of the stainless-steel propeller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".