Parametric Output of Penetration Length in De-Laval Nozzle using Computational Fluid Dynamics
Bibliographic record
Abstract
A Contour shaped rocket nozzle, commonly known as Bell nozzle, is a more efficient form of a De-Laval nozzle useful for operation at different altitudes resulting in variable pressure ratio across the nozzle. Here the penetration length during the flow is calculated in a De-Laval nozzle using Computational fluid dynamics (CFD). The TOP nozzle] by G.V.R. Rao is modeled and simulated using CFD analysis by varying the divergent half angle of the bell nozzle. The geometry of different divergent angle of the bell nozzle is modeled using MATLAB code. The modeled geometry and mesh of the nozzle is simulated in ANSYS-FLUENT software. The simulation shows the variation in different flow parameter at different divergent half angles. The analysis so far for the same is done in a conical shaped rocket nozzle at the cost of varying the divergent length for changing the divergent half angle of the nozzle. This project simulates bell nozzle with constant divergent length. The simulation model is verified and validated using experimental and computational data at an NPR (Nozzle pressure ratio) of 1000. As the Rao Nozzle is used currently in rocket, missile, and satellite control systems worldwide. This research work to show how the penetration length effect the Mass-Weighted average Mach number in a particular designed bell nozzle with different divergent half angle. Based on the results obtained, the discussion is done about the parameters and the conclusion is given.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".