Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
Bibliographic record
Abstract
A numerical study was performed to analyze the particle deposition of a cold spray process for the preparation of nickel electrocatalysts used in the Hydrogen Evolution Reaction (HER). The study focused on the creation of fin-shaped geometries with an optimal porosity on the electrode surface using a mask located between the nozzle exit and the substrate. Computational Fluid Dynamics (CFD) was performed on a three-dimensional high-pressure nozzle, with nickel powder used as the injection feedstock. The behavior of particles was effectively modeled through a two-way coupled Eulerian–Lagrangian approach. As per the parametric study, four masks of varying wire thicknesses and opening sizes were investigated. The masks were placed at 4 mm increments from the nozzle exit, with the substrate placed at standoff distances (SODs) of 10 mm and 20 mm. To capture the effects of the gas inlet operating conditions, two different nozzle inlet conditions were analyzed (2 MPa and 400 °C, 4 MPa and 800 °C). It was found that the nozzle inlet operating condition had the most significant impact, as it relates to the particle velocity and powder deposition. The high-pressure operating condition resulted in a deposition efficiency (DE) greater than 99.9% for all the test cases, with nearly all the impacted particles depositing on the substrate. For the medium-pressure operating condition, the DE increased linearly as the mask SOD was increased, due to the increase in the particle velocity upon impact.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".