INVESTIGATION OF FLOW PARAMETERS FOR TITANIUM COLD SPRAYING USING CFD SIMULATION
Bibliographic record
Abstract
A comprehensive study of cold gas dynamic spray technology is required for optimising performance and gun design for spraying various materials. Cold spraying technology is a new technique in industry and very limited data is available. This thesis focuses on the investigation of cold spray parameters for spraying ductile titanium alloys through a de-Laval convergent-divergent nozzle and optimisation of the nozzle dimensions. This work describes a detailed study of the various parameters, namely applied gas pressure, gas temperature, size of titanium particles and dimensions of the nozzle on the outlet velocity of the titanium particles. A model of a two-dimensional axisymmetric nozzle was used to generate the flow field of titanium particles with the help of a gas stream flowing at supersonic speed. ANSYS FLUENT software was used for the simulation of a cold spray nozzle. A standard k-ɛ model has been used to account for the turbulence produced due to the very high velocity flow. Differences in the velocity of titanium particles were modelled over the range of applied gas pressure, gas temperature and size of titanium particles. From the CFD simulation results optimum values of gas pressure and temperature were found for making a successful coating of titanium particles. The optimum nozzle dimensions were also found as the diverging length and exit diameter of the nozzle were found to affect the outlet velocity of titanium particles. The simulation results show good agreement with previous cold spray work using different spraying materials. Validation of the CFD model was done by referring to the experimental work and CFD work done for a similar kind of flow field. The grid quality of the model was investigated to get the results to converge and be independent of the grid size to give good agreement between the accuracy of results and the computational time.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".