Experimental Validation of Numerically Optimized Short Annular Diffusers
Bibliographic record
Abstract
Short annular diffuser systems consisting of a conical expansion section with negative wall angles and a solid diffusing section were tested experimentally and numerically. Three centre bodies with different wall angles and three outer walls with area ratios with respect to the annular diffuser inlet of 1.65, 1.91, and 2.74 were manufactured. The designs were selected from a set of ideal solutions determined by a numerical multiobjective optimization study completed by Cerantola and Birk (2012). Results presented in this paper are Reynolds number independent and are based on tests completed with an inlet Reynolds number of Ret ≈ 1.4 × 105 and Mach number Mt ≈ 0.16. Through considering the various centre body and outer wall configurations, an initial flow angle of 14° provides the best performance and the larger diameter outer wall generates more static pressure recovery at the expense of a reduction in outlet velocity uniformity and greater total pressure loss. The three selected designs were found to be in agreement with their optimum design qualities. When compared to computational solutions, the realizable k-ε turbulence model on coarse grids delivered the best relative comparison whereas the SST turbulence model predicted the best absolute agreement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".