Application of Singular Value Decomposition and Autoencoder for Supersonic Flow over Backward Facing Step
Bibliographic record
Abstract
This paper explores the integration of PythonFOAM with the hybrid pressure-based solver rhoPimpleCentralFoam to simulate supersonic flow over a backward facing step, aiming to validate the coupling of these solvers.Flow field simulation was performed using Reynolds averaged Navier Stokes (RANS)-based turbulence model with the hybrid solver.Streaming singular value decomposition (SVD) was applied to identify coherent flow structures, capturing essential features such as boundary layer separation and shock wave formation.The SVD modes were then utilized to reconstruct the velocity field, with the mean flow field obtained showed a close match to the original computational fluid dynamics (CFD) results, highlighting the effectiveness of this approach.Furthermore, a deep neural network autoencoder was applied to compress the flow field data, further demonstrating the integration of PythonFOAM with the solver.The autoencoder learns a compact representation of the velocity field, and the reconstructed field from this compressed representation aligns closely with the CFD results, confirming the model's ability to approximate complex flow dynamics.The results obtained demonstrated the successful coupling of the solvers and underscored the potential of reduced-order modelling techniques for solving complex flow problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".