A parallel solution-adaptive scheme for multi-phase core flows in solid propellant rocket motors

Sachdev, Jai; Groth, C. P. T.; Gottlieb, J. J.

doi:10.1080/10618560410001729135

Cited by 63 publications

(60 citation statements)

References 56 publications

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“…The main distinction between different orders of accuracy is in the requirements on the number of ghost cell layers attached to each solution block. As in [15] and the previous work of Groth et al, e.g., [5,7,46,55,56], an efficient domain partitioning is achieved in our implementation by distributing the active solution blocks equally among available processor cores, with more than one block permitted per processor core. This approach efficiently exploits the self-similar nature of the solution blocks and readily produces an effective load balancing.…”

Section: Parallelization With Uniform Treatment Of Sector Boundaries mentioning

confidence: 88%

High-order central ENO finite-volume scheme for ideal MHD

Susanto

Ivan

Sterck

et al. 2013

Journal of Computational Physics

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A high-order central essentially non-oscillatory (CENO) finite-volume scheme is developed for the compressible ideal magnetohydrodynamics (MHD) equations solved on threedimensional (3D) cubed-sphere grids. The proposed formulation is an extension to 3D geometries of a recent high-order MHD CENO scheme developed on two-dimensional (2D) grids. The main technical challenge in extending the 2D method to 3D cubed-sphere grids is to properly handle the nonplanar cell faces that arise in cubed-sphere grids. This difficulty is solved by considering general hexahedral cells with trilinear faces, which allow us to compute fluxes, areas and volumes with high-order accuracy by transforming to a reference cubic cell. The 3D CENO scheme is implemented within a flexible multi-block cubed-sphere grid framework to fourth-order accuracy, resulting in a high-order solution method for cubed-sphere grids with unique capabilities in terms of adaptive refinement and parallel scalability. The high-order method is applicable to the solution of general hyperbolic conservation laws with, in principle, arbitrary order. The CENO scheme is based on a hybrid solution reconstruction procedure that provides high-order accuracy in smooth regions, even for smooth extrema, and non-oscillatory transitions at discontinuities. The scheme is applied herein to MHD in combination with a GLM divergence correction technique to control the solenoidal condition for the magnetic field while preserving the high-order accuracy of the numerical procedure. The cubed-sphere simulation framework features a flexible design based on a genuine multi-block implementation, leading to high-order accuracy, flux calculation, adaptivity and parallelism that are fully transparent to the boundaries between the six sectors of the cubedsphere grid. The proposed 3D MHD CENO scheme is shown to achieve uniform fourth-order accuracy on cubed-sphere grids. Parallel domain partitioning and grid adaptivity are achieved on the 3D cubed-sphere grids using a hierarchical block-based division strategy with blocks of equal size. Numerical results to demonstrate the high-order accuracy, robustness and capability of the proposed high-order framework are presented and discussed for several test problems.

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Section: Parallelization With Uniform Treatment Of Sector Boundaries mentioning

confidence: 88%

High-order central ENO finite-volume scheme for ideal MHD

Susanto

Ivan

Sterck

et al. 2013

Journal of Computational Physics

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“…There were already some other attempts to use the AMR technique for body-fitted multiblock meshes [21,31], where curved geometries were allowed to be transformed into and simulated using a uniform computational domain. This can be achieved by using the coordinate transformation, which represents a transformation from the physical to the computational coordinates.…”

Section: B Adaptive Mesh Refinementmentioning

confidence: 99%

“…The computational efficiency is improved by reducing the required number of computational cells. The existing AMR applications [21,22] generally employ a blockstructured AMR algorithm. It involves 1) representing the 2-D/3-D hierarchical computational domain in blocks; 2) connecting the generated blocks in a quadtree/octree data structure; 3) estimating local truncation errors at all grid points and identifying blocks with excessive errors; 4) regridding the identified blocks by superimposing or removing blocks to accommodate changes in flow physics; and 5) redistributing the computational load between processors to maintain dynamic load balancing.…”

Section: = @Wmentioning

confidence: 99%

Efficient Computation of Spinning Modal Radiation Through an Engine Bypass Duct

Huang

Chen

et al. 2008

AIAA Journal

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The aim of this work is to accurately and efficiently predict sound radiation out of a duct with flow. The sound propagation inside a generic engine bypass duct, refractions by the shear layer of the exhaust flow, and propagation in the near field are the main focus of the study. The prediction uses either a modified form of linearized Euler equations or an alternative model based on acoustic perturbation equations, which were extended to cylindrical coordinates. The two models were compared on a canonical case of sound propagation out of a semi-infinite duct with flow. Good agreements between the predictions were achieved. The more general case of a generic aircraft engine bypass duct with flow was then investigated with the technique of adaptive mesh refinement to increase the computational efficiency. The results show that both linearized Euler equations and acoustic perturbation equations models can predict the near-field sound propagation and far-field directivity. The acoustic perturbation equations model, however, is more adaptive for its suitability to an arbitrary background mean flow.

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“…Impressive results have been obtained for both steady (Aftosmis and Berger, 2002) and unsteady (Murman et al, 2003) flows over highly complex geometries. Sachdev et al (2003) present an extension of the AMR approach to curvilinear meshes.…”

Section: Spatial Discretizationmentioning

confidence: 99%