Improved production implicit continuous‐fluid Eulerian method for compressible flow problems in Uintah

Tran, L.T.; Berzins, Martin

doi:10.1002/fld.2620

Cited by 3 publications

(3 citation statements)

References 40 publications

(117 reference statements)

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“…Uintah also uses an implicit formulation of MPM [40]. Considerable improvements in MPM and its analysis [41][42][43][44] have resulted from work connected to the Uintah code. Each particle carries state information (e.g., mass, volume, velocity, and stress) about the portion of the volume that it represents.…”

Section: The Materials Point Methodsmentioning

confidence: 99%

“…Because a common multifield reference frame is used for interactions among materials, typical problems with convergence and stability of solutions for separate domains communicating only through boundary conditions are alleviated. Considerable improvements in MPM and its analysis [41][42][43][44] have resulted from work connected to the Uintah code.…”

Section: The Materials Point Methodsmentioning

confidence: 99%

“…This problem exercises all of the main features of ICE, MPM, and AMR and amounts to solving eight partial differential equations, along with two pointwise solves and one iterative solve as described in [10,13]. The ICE method [32,33,41] is used to model the gas, and the MPM [8] method is used to model the solid. The interactions between the gas and the solid that cause the block of material to move are modeled using the algorithm described in Section 3 [6,7,34].…”

Section: A Motivating Problem and The Challenges Of Scaling On Fluid-mentioning

confidence: 99%

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Scalable large‐scale fluid–structure interaction solvers in the Uintah framework via hybrid task‐based parallelism algorithms

Meng

Berzins

2013

Concurrency and Computation

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Uintah is a software framework that provides an environment for solving fluid-structure interaction problems on structured adaptive grids for large-scale science and engineering problems involving the solution of partial differential equations. Uintah uses a combination of fluid flow solvers and particle-based methods for solids, together with adaptive meshing and a novel asynchronous task-based approach with fully automated load balancing. When applying Uintah to fluid-structure interaction problems, the combination of adaptive meshing and the movement of structures through space present a formidable challenge in terms of achieving scalability on large-scale parallel computers. The Uintah approach to the growth of the number of core counts per socket together with the prospect of less memory per core is to adopt a model that uses MPI to communicate between nodes and a shared memory model on-node so as to achieve scalability on large-scale systems. For this approach to be successful, it is necessary to design data structures that large numbers of cores can simultaneously access without contention. This scalability challenge is addressed here for Uintah, by the development of new hybrid runtime and scheduling algorithms combined with novel lock-free data structures, making it possible for Uintah to achieve excellent scalability for a challenging fluid-structure problem with mesh refinement on as many as 260K cores. ‡ Titan is a parallel computer under construction at Oak Ridge National Laboratory with about 299K CPU cores now with a large number of attached GPUs to be added in 2012. § Stampede is a parallel computer under construction at Texas Advanced Computing Center with two petaflops of CPU performance and eight petaflops of accelerator performance. 1389This work will start to address some of these challenges by developing scheduling algorithms and associated software that will be used to tackle fluid-structure interaction algorithms in which mesh refinement is used to resolve the structure. The vehicle for this work is the Uintah Computational Framework [2][3][4]. The broad range and challenging nature of Uintah applications and the software itself are described in [5]. The Uintah code was designed to solve fluid-structure interaction problems arising from a number of physical scenarios. Uintah uses a combination of computational fluid dynamics (CFD) algorithms in its implicit continuous-fluid Eulerian (ICE) solver [6,7] and couples ICE to a particle-based solver for solids known as the material point method (MPM) [8,9]. The adaptive mesh refinement (AMR) approach used in Uintah is that of multiple levels of regularly refined mesh patches. This mesh is also used as a scratch pad for the MPM calculation of the movement and deformation of the solid [10,11]. Uintah has been shown to scale successfully with many fluid and solid problems with adaptive meshes [12,13]; however, the scalability of fluid-structure interaction problems has proven to be somewhat more challenging. This is at least partly because the part...

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Section: The Materials Point Methodsmentioning

confidence: 99%

Section: The Materials Point Methodsmentioning

confidence: 99%

Section: A Motivating Problem and The Challenges Of Scaling On Fluid-mentioning

confidence: 99%

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Scalable large‐scale fluid–structure interaction solvers in the Uintah framework via hybrid task‐based parallelism algorithms

Meng

Berzins

2013

Concurrency and Computation

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Thermal-hydraulics analysis of flow blockage events for fuel assembly in a sodium-cooled fast reactor

Shan

Zhang

et al. 2019

International Journal of Heat and Mass Transfer

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An effective limiting algorithm for particle-based numerical simulations of compressible flows

Mason

Chen

et al. 2011

International Journal of Computational Fluid Dynamics

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Eulerian computational fluid dynamics (CFD) and Lagrangian computational structural dynamics (CSD) are used extensively in the aerospace industry. Combined mesh-based Eulerian and particle-based Lagrangian algorithms are very effective for modelling and simulation due to the increased efficiency of combining the two numerical simulations. However, when compressible flows are simulated using a particle-based algorithm, calculations of strong discontinuity, such as a shock wave, may become unstable. In the present study, a numerical limiter is integrated with a particle-based CFD code to remedy this instability. The limiting algorithm incorporates an 'averaging' technique which calculates average values using the properties of neighbouring particles (also known as material points), including mass, momentum and energy. These averaged values are then input to a min-mode limiter to eliminate numerical noise and incur dissipation in the flow in areas with steep property gradients. The results of this algorithm show very stable solutions with minimal oscillations when applied to the one-dimensional shock tube problem and an increased accuracy with reduced oscillations for a two-dimensional cylinder cross-flow problem.

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Improved production implicit continuous‐fluid Eulerian method for compressible flow problems in Uintah

Cited by 3 publications

References 40 publications

Scalable large‐scale fluid–structure interaction solvers in the Uintah framework via hybrid task‐based parallelism algorithms

Scalable large‐scale fluid–structure interaction solvers in the Uintah framework via hybrid task‐based parallelism algorithms

Thermal-hydraulics analysis of flow blockage events for fuel assembly in a sodium-cooled fast reactor

An effective limiting algorithm for particle-based numerical simulations of compressible flows

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