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Record W2514025384

Ducted Turbine Blade Optimization Using Numerical Simulation

2011· article· en· W2514025384 on OpenAlexaff
Michael Shive, Curran Crawford

Bibliographic record

VenueThe Twenty-first International Offshore and Polar Engineering Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBlade element momentum theoryComputational fluid dynamicsDuct (anatomy)TurbineTurbine bladeWedge (geometry)MechanicsFinite element methodEngineeringMechanical engineeringComputer scienceStructural engineeringMathematicsPhysicsGeometry
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.221
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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