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Record W7025019912

Turbulent unsteady separated flows in multidisciplinary computational fluid dynamics applications

2015· dissertation· en· W7025019912 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaKing Abdullah University of Science and TechnologyCompute Canada
KeywordsAirfoilTurbulenceBoundary layerComputational fluid dynamicsDomain decomposition methodsTurbulence modelingVortexLarge eddy simulationAerodynamicsScalar (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Employing the advection–diffusion–reaction equation as a model problem, a multiscale stable finite element formulation is presented for the Spalart–Allmaras turbulence model, and for the Zonal Detached-Eddy Simulation (ZDES) with a vorticity-based subgrid length scale. The multiscale method arises from a decomposition of the scalar field into coarse (resolved) and fine (unresolved) scales. Modeling of the unresolved scales corrects the lack of stability of the standard Galerkin formulation. The proposed method possesses superior properties like those of the Streamline Upwind/Petrov-Galerkin method and the Galerkin/Least-Squares method. The stabilization terms appear naturally and are effective for turbulent computations in which reaction-dominated effects strongly influence the boundary layer prediction.Validation of the method is accomplished via a two-dimensional problem in which skewed advection goes through a unit square, and a three-dimensional problem where turbulent unsteady flow passes over tandem cylinders. The boundary layer separation, free shear layer roll-up, vortex shedding from the upstream cylinder, and interaction with the downstream cylinder, are well reproduced. Good agreement with experimental measurements gives credence to the accuracy of ZDES in modeling turbulent separated flows.Following validation, two problems related to turbulent flows in aerospace and wind engineering applications are investigated. The first study is the evaluation of the performance degradation of ice-contaminated airfoils, including a NACA 23012 airfoil with a spanwise ice ridge, and a GLC-305 airfoil with a leading edge horn-shape glaze ice. Appropriate spanwise domain size and sufficient grid density are determined to enhance the reliability of the simulations. A comparison of lift coefficient and flow field variables demonstrates the added advantage of the ZDES model for the standard Spalart–Allmaras turbulence model, as illustrated by massively separated flows at pre-stall conditions of an icing airfoil. The vorticity-based subgrid length scale in ZDES prevents the delayed development of instabilities in the shear layer and a subsequent late transition to a fully turbulent flow. Spectral analysis and instantaneous visualization of turbulent structures are also highlighted.Another application of interest is wind-induced vibrations of tall buildings. In this numerical procedure, the natural unsteady wind in the atmospheric boundary layer is modeled with artificial inflow turbulence generation. The turbulent flow is simulated by the stabilized ZDES model, and a conservative quadrature-projection scheme is adopted to transfer unsteady loads from fluid to structural nodes. The aerodynamic damping that represents the fluid-structure interaction mechanism is determined by empirical functions extracted from wind tunnel experiments. Eventually, the unsteady motion of the building is solved via a structural modal analysis. The flow solutions and the structural responses in terms of mean and root mean squared quantities are compared with experimental measurements, over a wide range of reduced velocities. The significance of turbulent inflow conditions and aeroelastic effects is highlighted. This numerical procedure provides predictions of good accuracy and can be considered a preliminary design tool to evaluate unsteady wind effects on tall buildings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.105
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.014
GPT teacher head0.253
Teacher spread0.240 · 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.

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

Citations1
Published2015
Admission routes2
Has abstractyes

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