Optimization design of hydrogen Laval nozzle based on response surface methodology (RSM)
Bibliographic record
Abstract
Abstract To conduct an in-depth study of the injection characteristics of gas fuels, the structural parameters of the nozzle are optimized. Utilizing the Response Surface Method (RSM), this study selects an inlet radius R1 , outlet radius R2 , throat straight radius R0 , expansion half angle θ, and contraction half angle α as design parameters. Based on the validated numerical model, a response surface prediction model for outlet velocity v and mass flow rate Q is established. Using the derived expressions, the contribution and interactive effects of design variables on response variables are analyzed. The findings indicate that the outlet radius and throat radius significantly affect the outlet velocity. The outlet radius positively correlates with the outlet velocity, while the throat radius negatively correlates with it. The mass flow rate is most significantly influenced by the throat radius, increasing with its increase. With outlet velocity and mass flow rate as optimization objectives, the MOGA algorithm is applied for multi-objective optimization. The optimization results indicate that the optimized structural parameters increased the centrifugal nozzle’s atomization cone angle and mass flow rate by 1.86% and 27.4%, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".