Centrifugal blower optimization using gradient-free approaches and RANS simulation
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3313.vid In this study, an optimization approach based solely on open-source libraries is employed to optimize a centrifugal blower (both impeller and housing). Salome, OpenFoam, and Dakota are used for geometry and mesh generation, Computational Fluid Dynamics (CFD) simulation, and optimization, respectively. For the numerical analysis, Reynolds-averaged Navier-Stokes equations with the k-�� SST turbulence model are used. The Latin Hypercube Sampling (LHS) method is used to determine the design points while eleven design variables related to both the impeller and housing are chosen. Then different metamodels, including Artificial Neural Networks (ANN), Efficient Global Optimization (EGO), Gaussian Process (GP), and Multivariate Adaptive Regression Spline (MARS) are built to obtain optimal design. The findings show that GP proposes the best design among metamodels, with maximum efficiency (57.62%) and negligible error (0.12%) when compared to CFD calculation. In order to validate the optimal design from GP, a centrifugal blower is finally fabricated, demonstrating an acceptable agreement between experimental and numerical results
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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".