A New Concept of the Application of Shock Wave Technology in the Cold Gas Dynamic Spray Process
Bibliographic record
Abstract
Abstract A new cold spray process for a combustion-free spraying is studied experimentally and by modeling and simulation. The high particle velocity at the front of the substrate is achieved by using the shock tube technology. The particles have been injected downstream of the nozzle throat into a supersonic nozzle flow. The shock tube of 6.5 m length and 56 mm inner diameter provides the necessary reservoir conditions for the nozzle flow. The measurements of the particle velocity made by a laser Doppler anemometry (LDA) setup showed that the maximum velocity amounts to 1220 m/s for stainless steel particles of 15 µm diameter. The CFD-Code (Fluent) is first verified by a comparison with available numerical and experimental data for gas and gas-particle flow fields in a long Laval-nozzle. The good agreement implied the great potential of the new dynamic process concept for cold gas coating applications. Then the flow fields in the short Laval nozzle designed and realized by the Shock Wave Laboratory (SWL) have been investigated. The gas flow for experimentally obtained stagnation conditions has been simulated. The gas-particle flow without and with the influence of the particles on the gas flow has been calculated by the Surface Engineering Institute (IOT) and compared with experiments. The influence of the injection parameters on the particle velocities has been investigated as well.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".