Validation Results for a Diverse Set of Turbomachinery Cases Using a Density Based OpenFOAM® Solver
Bibliographic record
Abstract
A validation study of a variety of compressible flow turbomachinery cases is presented with comparisons to test data using OpenFOAM. OpenFOAM is open-source code consisting of various solvers and computational libraries focused on CFD. The study used a particular solver version with a density based approach that was derived from the “extended” branch of OpenFOAM. The example cases all consisted of single blade row designs at steady state and were run fully viscous (unless noted otherwise) with various turbulence models. The results showed a definite superiority of the density based solver over other OpenFOAM solvers in a test suite of simplified cases as well as in more complex examples in actual turbomachinery designs. A typical Laval nozzle case and transonic bump case are presented demonstrating the basic ability of the solver to capture shocks and to handle transonic flow in general. Actual turbomachinery applications consisted of a two-dimensional transonic compressor cascade, a moderately supersonic two-dimensional turbine cascade, two radial compressor cases, and a radial inflow turbine. The results showed the solver to be very capable of capturing pressure distributions and, most importantly, aerodynamic loss through the machines. The ability of the solver to accurately model performance in a wide range of different designs and across the entire performance map was demonstrated. Detailed comparisons to highly regarded test data are shown. Special examination was made of the computational costs of the solver which were quite high with run times coming in at about 10 times longer than other commercial compressible flow solvers. Several acceleration methods are discussed which significantly improved run time performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".