In Silico Studies on Pintle Nozzle with Lucrative SITVC by Creating Sanal Flow Choking and Unchoking Conditions
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0991.vid The theoretical discoveries of the Sanal flow choking (V.R.S.Kumar et al., Global Challenges, Vol.4, No.9, 2020, PMCID: PMC7267099) and streamtube flow choking (V.R.S.Kumar et al., Physics of Fluids, 34(4), 2022, https://aip.scitation.org/doi/10.1063/5.0086638) achieved significant contemplation in all branches of science and engineering for resolving various unanswered scientific questions brought onward from the beginning of this era (V.R.S.Kumar et al., Physics of Fluids, 34(10), 2022, https://aip.scitation.org/doi/10.1063/5.0105407). The applications of these flow choking phenomena are more significant in aerospace industries particularly in fluidic secondary-injection-thrust-vector-control (SITVC) research. In this paper in silico studies have been carried out for the design optimization of a lucrative variable thrust and steering system for future single-stage-to-orbit (SSTO) vehicles. Variable thrust is achieved through the pintle movement and flexible steering is achieved by creating Sanal flow choking and the unchoking conditions in the secondary bypass duct, which is facilitated in the pintle. Note that at the Sanal flow choking condition secondary jet will be supersonic and at the unchoked flow condition secondary jet will come down to subsonic, which can be achieved by altering the total-to-static pressure ratio in the secondary bypass duct. In silico studies have been carried out using validated 2D density based, SST k-ω turbulence model with finite volume scheme. The credibility of the code is verified using the closed-form analytical model capable to predict the boundary layer blockage at the diabatic flow condition (V.R.S.Kumar et al., Physics of Fluids, 34(10), 2022). Parametric analytical studies reveal that by altering the pressure ratio we could produce subsonic and supersonic secondary jet for creating variable shock strength for meeting the mission specific steering at variable altitudes lucratively. We concluded that the geometry optimization of a pintle nozzle with a secondary bypass duct having the shape of a funnel is a meaningful objective for an efficient SITVC. It can be achieved by invoking Sanal flow choking and the unchoking conditions in the bypass duct for controllable steering and altitude compensation of SSTO vehicles lucratively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".