Modeling study of solid-particle erosion with consideration of particle velocity dependent model parameters
Bibliographic record
Abstract
An advanced erosion model that correlates two model parameters—the energies required to remove unit mass of target material during cutting wear and deformation wear, respectively, with particle velocity, particle size and density, as well as target material properties, is proposed. This model is capable of predicting the erosion rates for a material under solid-particle impact over a specific range of particle velocity at the impingement angle between [Formula: see text] and [Formula: see text], provided that the experimental data of erosion rate for the material at a particle velocity within this range and at impingement angles between [Formula: see text] and [Formula: see text] are available. The proposed model is applied on three distinct types of materials: aluminum, perspex and graphite, to investigate the dependence behavior of the model parameters on particle velocity for ductile and brittle materials. The predicted model parameters obtained from the model are validated by the experimental data of aluminum plate under Al2O3 particle impact. The significance and limitation of the model are discussed; possible improvements on the model are suggested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".