Measurement of Flow Field in Waterjet Nozzles with Different Structures
Bibliographic record
Abstract
This paper attempts to measure the jet flow field of different nozzles accurately, and optimize the effect of the jetting devices.For this purpose, four nozzles with different structures were physically and mathematically modelled, including conical-cylindrical nozzle, conical nozzle, cylindrical nozzle and streamlined nozzle.The turbulence was described by the improved standard k-epsilon (k-ε) turbulence model.Then, the measurement space was meshed into unstructured triangular grids, with high grid density in key areas.Next, the flow field inside and outside each nozzle was simulated on ANSYS Fluent, and the jet velocity field distribution at different positions, velocity fields and kinetic turbulence were analyzed based on the simulation results.The analysis shows that the streamlined nozzle achieved the highest flow velocity and lowest turbulent energy at the centerline of the flow field, and should be prioritized in actual applications.The conical-cylindrical nozzle strikes a good balance between efficiency and cost, providing a good option for jetting operations.By contrast, conical nozzle and cylindrical nozzle should not be adopted.The cylindrical segment can stabilize the shape of the jet by increasing the jet acceleration and peak velocity.The nozzle structure should be optimized to reduce the energy loss in each phase of energy conversion, thereby lowering the energy required to destroy a unit volume of the target.The simulation results shed important new light on the selection of nozzle for jetting devices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".