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Record W4243390904 · doi:10.35940/ijrte.f7334.038620

Parametric Output of Penetration Length in De-Laval Nozzle using Computational Fluid Dynamics

2020· article· en· W4243390904 on OpenAlexaboutno aff
Rishab Kumar Agrawal, Sivaram Munagala

Bibliographic record

VenueInternational Journal of Recent Technology and Engineering (IJRTE) · 2020
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleComputational fluid dynamicsDischarge coefficientRocket engine nozzleMach numberMechanicsConical surfaceAngle of attackMechanical engineeringAerospace engineeringAerodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.268
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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