Evaluation of Discrete Element Models for Prediction of Abrasive Mass Flow Rate in Abrasive Jet Machining Systems
Bibliographic record
Abstract
<p>The flow of abrasive particles in the pressurized feed system of an abrasive jet micro machining (AJM) apparatus was investigated both experimentally and computationally. The feasibility of predicting the abrasive mass flow rate using discrete element methods implemented in two commercial software packages, i.e. LS-DYNA and EDEM, was studied. A method to simulate applied pressure was developed, and computer codes to extract relative data during postprocessing were implemented. For a simplified system using spherical particles, the EDEM results better agreed with measured mass flow rates, while LS-DYNA software grossly underestimated the measured mass flow rate. The effect of pressure on mass flow rate was studied numerically using EDEM and the trends largely agreed with the experiments. Overall, it is possible to use discrete element methods to simulate powder flow in AJM systems, but only at an exceptionally high computational cost, and with considerable effort to extract relevant data.</p>
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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 | 0.006 | 0.000 |
| 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 | 0.001 |
| 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".