Design and Numerical Simulation of Convergent Divergent Nozzle
Bibliographic record
Abstract
This works centers on the design of a De Laval (convergent - Divergent) nozzle to accelerate the flow to supersonic or hypersonic speeds and computational analysis of the same. An initial design of the nozzle is made from the method of characteristics. The coding was done in Matlab to obtain the contour of the divergent section for seven different exit Mach numbers viz. 2.5,3,3.5,4,4.5,5 and 5.5.To quantify variation in the minimum length of the nozzle divergent section with respect to the exit mach number, a throat of constant height (0.005m) and width (0.05m) was chosen for all the design. The area exit required for each mach no varying from 1 to 5.5 was plotted using isentropic relations and was also used to verify the exit area of the nozzle for each of those mach numbers. An estimate of the exit pressure ratio is obtained by using isentropic and normal shock relations. With this exit pressure ratio, a more refined verification is done by computational analysis using ANSYS Fluent software for a contour nozzle with exit Mach number 5.5. The spalart Allmaras and k-epsilon model were used for turbulence modeling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".