Computations of Wall Distances Based on Differential Equations

Tucker, Paul G.; Rumsey, Chris; Spalart, Philippe R.; Bartels, Randy A.; Biedron, Robert T.

doi:10.2514/1.8626

Cited by 67 publications

(47 citation statements)

References 9 publications

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“…The smooth transition of the eddy viscosity field between the RANS and implicit LES zones seems more reasonable and helps maintain good numerical stability. Also, as outlined in [33], smoother fields give potentially improved RANS model behavior around singularities and convex corners.…”

Section: Methodsmentioning

confidence: 99%

Turbulent jet characteristics for axisymmetric and serrated nozzles

Xia

2015

Computers & Fluids

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Turbulent jet large eddy simulations (LES) are performed at Mach 0.9 and Reynolds number around 10 6 . Implicit large-eddy simulation (ILES) is employed, namely omitting explicit subgrid scale models. The Reynolds-averaged Navier-Stokes (RANS) solution is blended into the near wall region. This makes an overall hybrid LES-RANS approach. A Hamilton-Jacobi equation is applied to remove the disparate turbulence length scales implied by hybridization. Computations are contrasted for a baseline axisymmetric (round) nozzle and a serrated (or chevron) nozzle with high bending and penetration. Jet characteristics for both nozzles are studied in detail with well documented experimental data compared. The chevron effects are demonstrated by comparing both solutions using the same mesh resolution and flow conditions. Higher order velocity moments with potential for aeroacoustic modeling and noise prediction, such as the two-point velocity spatial correlations, are also explored. Numerical simulations presented in this study utilize an in-house flow solver with improved parallel scalability and efficiency by means of data packeting and a scheduling algorithm similar to the Round Robin scheduling.

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Section: Methodsmentioning

confidence: 99%

Turbulent jet characteristics for axisymmetric and serrated nozzles

Xia

2015

Computers & Fluids

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“…This can be O(n v √ n s ) and O(n v log n s ) operations, however, for complex geometries such specialized approaches are difficult to apply [28]. Differential equation-based methods have been discussed in detail in References [29,28,30]. Advantageously, they are naturally compatible with vector and parallel computer architectures.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in Figure 19, the biased medial axes may provide more sensible information for domain division. We also note that to further sculpt the medial axis (see Reference [30]) other d dependent functions can be added to the differential Eikonal type equation. This sculpting can provide a d−MAT for high quality hexehedral meshing.…”

Section: Solution Superpositionmentioning

confidence: 99%

“…(2) can be solved by numerical schemes with just enough dissipation to gain an entropy (physical sensible) solutions [25,28]. As shown by Tucker [28,30], the right hand side Laplacian is useful and often employed to control the front propagation velocity. However, this potential is not explored here.…”

Section: H-j/eikonal Equation For Dmentioning

confidence: 99%

“…Such examples include explosive front, multiphase flow, and electrostatic particle force modeling. Also, in grid generation the near-wall isovalues of d can be used to form the boundary layer mesh [25,30], while the far-field d contours can be used as a rapid means of evaluating computational interfaces on unstructured overset meshes with relative movements. Not only being useful in traditional physics, distance field has also been important in computer vision, modeling and computational physics.…”

Section: Introductionmentioning

confidence: 99%

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Distance Solutions for Medial Axis Transform

Xia

Tucker

2009

Proceedings of the 18th International Meshing Roundtable

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Abstract.A method towards robust and efficient medial axis transform (MAT) of arbitrary domains using distance solutions is presented. The distance field, d, is calculated by solving the hyperbolic-natured Eikonal (or Level Set) equation. The solution is obtained on Cartesian grids. Both the fast-marching method and fast-sweeping method are used to calculate d. Medial axis point clouds are then extracted based on the distance solution via a simple criteria: the Laplacian or the Hessian determinant of d. These point clouds in 2D-pixel and 3D-voxel space are further thinned to curves and surfaces through binary image thinning algorithms. This results in an overall hybrid approach. As an alternative to other methods, the current d−MAT procedure bypasses difficulties that are usually encountered by pure geometric methods (e.g. the Voronoi approach), especially in 3D, and provides better accuracy than pure thinning methods. It is also shown that the d−MAT approach provides the potential to sculpt/control the MAT form for specialized solution purposes. Various examples are given to demonstrate the current approach.

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Fast computation of the wall distance in unsteady Eulerian fluid‐structure computations

Grimberg

Farhat

2018

Numerical Methods in Fluids

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Summary Highly nonlinear, turbulent, dynamic, fluid‐structure interaction problems characterized by large structural displacements and deformations, as well as self‐contact and topological changes, are encountered in many applications. For such problems, the Eulerian computational framework, which is often equipped with an embedded (or immersed) boundary method for computational fluid dynamics, is often the most appropriate framework. In many circumstances, it requires the computation of the time‐dependent distance from each active mesh vertex of the embedding mesh to the nearest embedded discrete surface. Such circumstances include, for example, modeling turbulence using the Spalart‐Allmaras or detached eddy simulation turbulence models and performing adaptive mesh refinement in order to track the boundary layer. Evaluating at each time step the distance to the wall is computationally prohibitive, particularly in the context of explicit‐explicit fluid‐structure time‐integration schemes. Hence, this paper presents two complementary approaches for reducing this computational cost. The first one recognizes that many quantities depending on the wall distance are relatively insensitive to its inaccurate evaluation in the far field. Therefore, it simplifies a state‐of‐the‐art algorithm for computing the wall distance accordingly. The second approach relies on an effective wall distance error estimator to update the evaluation of the wall distance function only when otherwise, a quantity of interest that depends on it would become tainted by an unacceptable level of error. The potential of combining both approaches for dramatically accelerating the computation of the wall distance is demonstrated with the Eulerian simulation of the inflation of a disk‐gap‐band parachute system in a supersonic airstream.

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Computations of Wall Distances Based on Differential Equations

Cited by 67 publications

References 9 publications

Turbulent jet characteristics for axisymmetric and serrated nozzles

Turbulent jet characteristics for axisymmetric and serrated nozzles

Distance Solutions for Medial Axis Transform

Fast computation of the wall distance in unsteady Eulerian fluid‐structure computations

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