Ducted Turbine Blade Optimization Using Numerical Simulation
Bibliographic record
Abstract
This paper presents a combined blade element (BE), computational fluid dynamics (CFD) method for performance analysis and optimization of ducted turbines. The model is similar to standard blade element momentum theory, except that CFD replaces the momentum equation for determining the induction factors. This eliminates many assumptions used in applying the typical blade element momentum (BEM) theory to a turbine and provides much richer flow information. It is also required for ducted turbines, since there is no fundamental momentum theory model that includes the impact of the duct on the flow field. The simulations use an axi-symmetric domain, which is computationally efficient since only a thin wedge of the entire geometry needs to be represented. A simple algorithm was developed to determine the optimum rotor loading and tip speed ratio for a given duct geometry within a user-defined level of granularity. This paper also demonstrates that for certain ducts, a non-uniform loading over the disk can improve overall performance by limiting flow separation within the duct.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".