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Research Papers: Techniques and Procedures

Benchmarking of Computational Fluid Methodologies in Resolving Shear-Driven Flow Fields

[+] Author and Article Information
Brandon Horton, Yangkun Song, Jeffrey Feaster

CRashworthiness for Aerospace Structures
and Hybrids (CRASH) Lab,
Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061

Javid Bayandor

Fellow ASME
CRashworthiness for Aerospace Structures
and Hybrids (CRASH) Lab,
Department of Mechanical Engineering,
Virginia Tech,
Blacksburg, VA 24061
e-mail: bayandorj@asme.org

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received September 22, 2016; final manuscript received March 9, 2017; published online August 11, 2017. Assoc. Editor: Praveen Ramaprabhu.

J. Fluids Eng 139(11), 111402 (Aug 11, 2017) (12 pages) Paper No: FE-16-1623; doi: 10.1115/1.4036590 History: Received September 22, 2016; Revised March 09, 2017

Despite recent interests in complex fluid–structure interaction (FSI) problems, little work has been conducted to establish baseline multidisciplinary FSI modeling capabilities for research and commercial activities across computational platforms. The current work investigates the fluid modules of contemporary FSI methodologies by solving a purely fluid problem at low Reynolds numbers to improve understanding of the fluid dynamic capabilities of each solver. By incorporating both monolithic and partitioned solvers, a holistic comparison of computational accuracy and time-expense is presented between lattice-Boltzmann methods (LBM), coupled Lagrangian–Eulerian (CLE), and smoothed particle hydrodynamics (SPH). These explicit methodologies are assessed using the classical square lid-driven cavity for low Reynolds numbers (100–3200) and are validated against an implicit Navier–Stokes solution in addition to established literature. From an investigation of numerical error associated with grid resolution, the Navier–Stokes solution, LBM, and CLE were all relatively mesh independent. However, SPH displayed a significant dependence on grid resolution and required the greatest computational expense. Throughout the range of Reynolds numbers investigated, both LBM and CLE closely matched the Navier–Stokes solution and literature, with the average velocity profile error along the generated cavity centerlines at 1% and 4%, respectively, at Re = 3200. SPH did not provide accurate results whereby the average error for the centerline velocity profiles was 31% for Re = 3200, and the methodology was unable to represent vorticity in the cavity corners. Results indicate that while both LBM and CLE show promise for modeling complex fluid flows, commercial implementations of SPH demand further development.

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References

Figures

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Fig. 1

Graphical representation of the D2Q9 lattice velocities

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Fig. 2

Example of CLE advection from the Lagrangian step onto the ambient Eulerian mesh

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Fig. 3

Representation of particle influence on surrounding neighbors in SPH

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Fig. 4

Illustration of a 2D lid-driven cavity with a single driven wall and three fixed boundaries

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Fig. 5

Schematic of boundary conditions for SPH formulation

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Fig. 6

(Left) U- and (right) V-profiles for Re = 1000 comparing each computational methodology: (a) fluent, (b) LBM, (c) CLE, and (d) SPH

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Fig. 7

U-centerline profiles using 512 × 512 cells for: (a) LBM, (b), CLE, and (c) SPH. Error bars are based on the uncertainty between the 256/512 grid resolution.

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Fig. 8

Computational time for all time explicit methodologies at Re = 1000

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Fig. 9

Velocity magnitude contour plots with streamlines at Re = 1000 for (a) fluent, (b) LBM, (c) CLE, and (d) SPH

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Fig. 10

(Left) U- and (right) V-profiles for Re = 3200 comparing each computational methodology with the existing literature benchmark [14,15]

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Fig. 11

Velocity magnitude contour plots with streamlines at Re = 3200 for (a) fluent, (b) LBM, (c) CLE, and (d) SPH

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