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Record W2966063290 · doi:10.4050/f-0075-2019-14500

Comparison of Rotor - Fuselage Flow Fields and Unsteady Tail Interactions between Two CFD Codes and Experiment

2019· article· en· W2966063290 on OpenAlexaff
Peter F. Lorber, Byung-Young Min

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsFuselageComputational fluid dynamicsRotor (electric)MechanicsTurbulenceSolverFlow (mathematics)Stabilizer (aeronautics)PhysicsAerospace engineeringEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Experimental measurements of the unsteady flow fields generated by a scale model rotor, hub, and fuselage, plus the unsteady loads generated on a horizontal stabilizer, have been used as the basis for comparison to two computational fluid dynamics (CFD) simulations. The STAR-CCM+ commercial solver and CREATETM-AV HELIOS using the KCFD and SAMCART solver were applied to a series of seven test cases. The configurations were fuselage and hub with blades-on and blades-off for velocity fields, as well as the stabilizer in two locations for unsteady normal forces. The quantities examined included time averaged rotor, hub, fuselage, and tail forces and moments, time averaged, unsteady, and periodic velocities, and stabilizer forces. Overall for the forces and velocities, both codes did well for the time averages, and captured the trends and qualitative features of the unsteady quantities. Cases driven by a strong tip vortex – stabilizer interaction were modelled well, the key issue being rotor tip path plane trim. Cases driven by combined wakes from the hub, fuselage, and forward pointing blades were more challenging, and the codes often under-predicted the unsteady amplitudes or differed in the distribution of frequencies. Since this was accompanied by higher than measured Reynolds stresses, one cure may be resolved smaller scales in the solutions, increasing the order, or improving the turbulence modelling to better preserve the unsteady flow structures. However, application for industrial design still requires computational efficiency.

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: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.317

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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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