Optimization of Laval nozzle structure for gas mixture of NH3–O2 combustion products and CO2–H2O by response surface methodology
Bibliographic record
Abstract
Ammonia is a promising alternative fuel, and the nozzle plays a critical role as a core component in ammonia-fueled hypersonic propulsion systems. This study combines response surface methodology (RSM) with computational fluid dynamics (CFD) simulations to optimize the geometric configuration and combustion performance of the ammonia-fueled Laval nozzle. The RSM is adopted for optimizing the thrust by adjusting the nozzle's geometric parameters and the inlet parameters of the Laval nozzle. The CFD's results demonstrate that the quadratic effect of the throat radius and the interaction between the inlet and throat radii are critical for thrust optimization. The response surface models have demonstrated significant potential in enhancing nozzle thrust. The thrust is increased by 2% through adjusting the geometric parameter of the nozzle. Optimizing inlet parameters indicates that higher inlet temperatures (1073.15 K) and higher oxygen concentrations (12%) enhance the chemical kinetics efficiency of ammonia combustion, thereby improving the conversion of thermal energy into kinetic energy. The thrust increased by 37% under the optimal inlet parameters obtained through response surface optimization. The effectiveness of RSM in multi-variable optimization is verified, and the mechanism of multi-parameter coupling effects on hypersonic ammonia combustion dynamics is revealed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".