Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A Bell type nozzle is most commonly used shape for rocket nozzles. This type of nozzle not only offers significant advantages in terms of size and performance over the conical nozzle but also reduces complexity compared to annular nozzles. The nozzle uses the stagnation temperature (To) and stagnation pressure (Po) generated in the combustion chamber to create thrust by accelerating the combustion gases to a high supersonic velocity. The nozzle expansion ratio was governed by the exit velocity. During flight, the jet flow is ideally expanded and adapted to the surrounding flow only during a short period. The rest of the time, the rocket engine operates in off-design conditions. The present work incorporates 2D axisymmetric flow analysis within the bell type nozzle, at design and off-design conditions, by using computational fluid dynamic software GAMBIT 2.4.6 and FLUENT 6.3.26. A computer code, with the use of the method of characteristics and stream function, is developed to define the higher efficiency nozzle contours for analysis. Simulation has been carried out separately for two different flow conditions i.e. cold and hot. Shear Stress Transport k- turbulence model has been chosen for flow analysis. The converged solutions captured asymmetric lambda shock in the nozzles at higher nozzle pressure ratios (NPR) for viscous flows. It also predicted aftershock and flow separation depending upon NPR. The strength of the normal shock, at Mach stem in viscous prediction, generally increases with an increase in NPR. Good agreement is observed between predicted simulation and analytical results in terms of shock structure, shock location, the size of normal shock, aftershock, and asymmetric lambda shocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it