Simulations of gas flow in gas atomisation of liquid metals and validation experiments
Bibliographic record
Abstract
The production of metal powder by gas atomisation generates feedstock for many manufacturing techniques, including hot isostatic pressing, laser cladding and, of current interest, powder bed additive manufacturing.While many thousands of alloys are commercially available in bulk form, fewer than fifty are widely available as powders suitable for additive manufacturing.This is due to difficulties in controlling droplet size distribution and avoiding particle defects.Indeed, a typical gas atomisation process that attempts to make powder for powder bed additive manufacturing achieves a yield of suitable powder below 50%.Several studies have simulated the gas atomisation process or part of it, but few validate the models directly.The current work aims to take a first step towards making a holistic and fully validated model for gas atomisation.The gas flow from de Laval nozzles under conditions similar to those used in gas atomisation has been simulated using computational fluid dynamics and experiments to validate these simulations have been performed using shadowgraphy.A validated model can be used as part of a larger model to predict the phenomena that occur in gas atomisation and thereby refine the process to improve the production yield of powders, especially for additive manufacturing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".