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The analysis of the influence of the turbulence model selection on the parameters of interaction of a supersonic jet with an obstacle

2024· article· en· W4404160637 on OpenAlexaboutno aff
Р. А. Пешков, A. S. Shmetkova, O. V. Ispravnikova, Ju. L. Suskina

Bibliographic record

VenueOmsk Scientific Bulletin Series Aviation-Rocket and Power Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceSupersonic speedJet (fluid)ObstacleMechanicsSelection (genetic algorithm)PhysicsAerospace engineeringComputer scienceEngineeringGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Due to the intense loading of the elements of launch structures when exposed to rocket engine jets, it is obvious that it is necessary to determine the gas-dynamic, thermal and other loads that occur during the launch of the launch vehicle. Numerical modeling using application programs is one of the widely used methods of their calculation, since physical modeling requires significant resources. The study analyzes the case of interaction of a single supersonic gas jet with a flat barrier oriented perpendicular to the direction of the jet. Differential equations describing the motion of a compressible viscous heatconducting gas (Navier-Stokes equations) are presented, and a method for averaging them by Reynolds is described. Some one- and two-parameter turbulence models based on Reynolds equations are considered. A numerical simulation of the flow of a supersonic jet of air from a Laval nozzle onto a flat aluminum barrier located perpendicular to the axis of the jet is carried out. The ANSYS Fluent software package is used to analyze the effect of choosing a turbulence model on the distribution of the Mach number and pressure on the barrier. A comparison of the results of the study with experimental data showed that the most accurate results are obtained using the k-ω SST turbulence model.

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 score0.013
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueOmsk Scientific Bulletin Series Aviation-Rocket and Power EngineeringSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207