Modelling of Supersonic and Subsonic Flows Using Hybrid PressureBased Solver in Openfoam
Bibliographic record
Abstract
In this paper, validation of the hybrid central solver based on PIMPLE algorithm and KT scheme (rhoPimpleCentralFoamsolver) for high and low speed flows has been presented.Two different test cases were considered for the present study; a supersonic flow over a backward facing step with Mach number 2 and non-reacting subsonic flow separated by a bluff body with a Mach number 0.18.Backward facing step test case was studied using SST k-ω turbulence model, whereas performance of three different RANS based turbulence models, i.e.SST k-ω model, standard k-ε model and modified k-ε models were assessed for the flow over a bluff body.Results obtained from hybrid central solver for backward facing step using SST k-ω turbulence model showed good agreement with the experimental data.The mean flow features such as expansion fan, recirculation zone, boundary layers, shear layer and reattachment shock were also captured accurately.Whereas, the performance of the modified k-ε turbulence model was found to better as compared to other turbulence models in the predictions of flow structure of flow over a bluff-body.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".