AN INVESTIGATION OF EFFECT OF CONTROL JETS LOCATION AND BLOWING PRESSURE RATIO TO CONTROL BASE PRESSURE IN SUDDENLY EXPANDED FLOWS
Bibliographic record
Abstract
The drag force is an essential factor in any projectile, from road vehicles to rocket or aircraft. The total drag includes skin friction drag, wave drag, and base drag. The base drag is the drag due to low pressure in the base region of the projectile. In the case of suddenly expanded flows, due to the sudden expansion of flow from the nozzle into the enlarged duct, the low pressure is created in the base region of the enlarged tube, which results in base drag and hence overall thrust reduced. In this paper, Computational Fluid Dynamic (CFD) analysis is used to analyze the effect of secondary air blowing jets called control jets to control base pressure in the base region of suddenly enlarged duct. These control jets are placed at different Pitch Circle Diameters (PCD) on the base face of the enlarged pipe. The objective of this work is to increase the base pressure up to atmospheric pressure and hence reduces the base drag. Mach number 3.0 is considered for analysis. The CFD analysis is done for different combinations of Area Ratios (AR) (2, 5 and 8), Nozzle Pressure Ratios (NPR) (2, 5 and 8), and PCD (d1, d2, and d3). Further analysis is done for different air blowing pressure ratios (BPR) to optimize air blowing pressure. The analysis results are plotted for different area ratios, nozzle pressure ratios, and PCD of control jets. By observing results, it can be concluded that the base pressure is strongly influenced by AR, NPR, and PCD of control jets. The air blowing pressure should be optimum to save energy, and the optimum values can be selected from the results.
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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) | 0.001 | 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".