Effect of flow compressibility in convergent-divergent nozzle
Bibliographic record
Abstract
: Nozzles come in a range of shapes and sizes based on a purpose such as De Laval nozzle also known as the converging diverging nozzle. Convergent- divergent nozzle is commonly used in modern rocket engines that accelerates at high velocity till the supersonic region Ma>1. This paper aims to investigate numerically the effect of area ratio of the convergent divergent throat and the Mach number from the incompressible flow to compressible flow. The software that were used to simulate cases is ANSYS Computational Fluid Dynamics (CFD) code FLUENT. The area ratio throat (AR= 5mm, 6mm, 7mm, and 8mm) and Mach number (Ma = 0.2, 1.0 and 1.8) was varied to obtain more specific result. The result from this paper has shown that the most effective Mach number and area ratio are at the 0.2 Ma with the AR=5mm According to the findings of this research, the most effective Mach number and area ratio are at 0.2 Ma with AR=5mm. According to the calculations performed in this research, the percentage variation of velocity at 0.2Ma was larger than at 1.0 Ma and 1.8 Ma. This happen cause at the sonic and supersonic flow there is factor that disturb the flow such as the back pressure and normal shock
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".