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Record W2526908394 · doi:10.11159/htff16.136

Dynamic Stall Simulation with Direct-Forcing Immersed Boundary Method

2016· article· en· W2526908394 on OpenAlexvenueno aff
Nima Vaziri, Ming‐Jyh Chern, Tzyy‐Leng Horng

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStall (fluid mechanics)Immersed boundary methodForcing (mathematics)Computer scienceControl theory (sociology)Boundary (topology)MechanicsPhysicsMathematicsArtificial intelligenceAtmospheric sciencesMathematical analysisControl (management)

Abstract

fetched live from OpenAlex

To predict fluid-structure interactions accurately, a variety of computational methods have been proposed.The most common method to simulate the flow with a complicated solid boundary is to use a body-fitted technique with grids fitting and clustering along the complex boundary.The immersed boundary method is becoming popular since 1972 due to its capability to handle simulations for a moving complex boundary with lower computational cost and memory requirements than the conventional body-fitted method.This method can be categorized as a continuous forcing method in which a forcing term is added to the continuous Navier-Stokes equations before they are discretized.The direct-forcing immersed boundary method (DFIB) is one of the immersed boundary methods.This method uses a virtual forcing term determined by the difference between the interpolated velocities at the boundary points and the desired boundary velocities.The DFIB method with both virtual force and heat source is developed by [1] to solve Navier-Stokes and the associated energy transport equations to study some thermal flow problems caused by a moving rigid solid object within.The present study is the extended version of [1] for arbitrary geometries.We use point-in-polygon (PIP) to exclude the solid geometry from the fluid.In computational geometry, the PIP problem asks whether a given point in the plane lies inside, outside, or on the boundary of a polygon.It is a special case of point location problems and finds applications in areas that deal with processing geometrical data.A stall is a reduction in the lift coefficient generated by a foil as angle of attack increases.This occurs when the critical angle of attack of the foil is exceeded.Dynamic stall is a non-linear unsteady aerodynamic effect that occurs when airfoils rapidly change the angle of attack.The rapid change can cause a strong vortex to be shed from the leading edge of the aerofoil, and travel backwards above the wing.The vortex, containing high-velocity airflows, briefly increases the lift produced by the wing.In the present study a dynamic stall case on a NACA 0012 is considered.The Reynolds number is 1.35*10 5 .The mean angle of attack is 10 o and the amplitude of ocillation is 15 o .The reduced frequency is set to 0.1.Lift and drag coefficients are compared with the former studies and they show that the DFIB method can simulated the flow over a airfoil in the stall case well.Some differences are seen at the maximum (25 o ) and the minumum (-5 o ) angles; but at the others angles the values are very close.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.006
GPT teacher head0.225
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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