The analysis of the influence of the turbulence model selection on the parameters of interaction of a supersonic jet with an obstacle
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".