Multiphysics simulations: challenges and opportunities.

Keyes, D; McInnes, L C; Woodward, C; Gropp, W; Myra, E; Pernice, M

doi:10.2172/1034263

Cited by 91 publications

(149 citation statements)

References 292 publications

(363 reference statements)

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“…As an example, renormalization group theory [29,30] has been proposed as a general framework for turbulence and other multiscale physical modelling, although revolutionary advances have not materialized specifically for turbulence modelling. Nevertheless, advances in multiscale modelling such as systematic upscaling (SU) [31,32] offer the possibility for step changes in physical modelling capability and should be pursued in a measured manner.…”

Section: (I) High-performance Computingmentioning

confidence: 99%

Enabling the environmentally clean air transportation of the future: a vision of computational fluid dynamics in 2030

Slotnick

Khodadoust

Alonso

et al. 2014

Phil. Trans. R. Soc. A.

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As global air travel expands rapidly to meet demand generated by economic growth, it is essential to continue to improve the efficiency of air transportation to reduce its carbon emissions and address concerns about climate change. Future transports must be ‘cleaner’ and designed to include technologies that will continue to lower engine emissions and reduce community noise. The use of computational fluid dynamics (CFD) will be critical to enable the design of these new concepts. In general, the ability to simulate aerodynamic and reactive flows using CFD has progressed rapidly during the past several decades and has fundamentally changed the aerospace design process. Advanced simulation capabilities not only enable reductions in ground-based and flight-testing requirements, but also provide added physical insight, and enable superior designs at reduced cost and risk. In spite of considerable success, reliable use of CFD has remained confined to a small region of the operating envelope due, in part, to the inability of current methods to reliably predict turbulent, separated flows. Fortunately, the advent of much more powerful computing platforms provides an opportunity to overcome a number of these challenges. This paper summarizes the findings and recommendations from a recent NASA-funded study that provides a vision for CFD in the year 2030, including an assessment of critical technology gaps and needed development, and identifies the key CFD technology advancements that will enable the design and development of much cleaner aircraft in the future.

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Section: (I) High-performance Computingmentioning

confidence: 99%

Enabling the environmentally clean air transportation of the future: a vision of computational fluid dynamics in 2030

Slotnick

Khodadoust

Alonso

et al. 2014

Phil. Trans. R. Soc. A.

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show abstract

“…However, the complexity of fully coupled global ESM has prevented a comprehensive and integrated approach to all aspects of credibility. A good fraction of ideas papers submitted to AXICCS concerned the many facets of creating a more integrated approach to model credibility including, for example, strategies emphasizing verification, validation, and uncertainty quantification [Papers 14,20,25,26,34,38,51], new ideas for exploiting scientific or computational insights to allow greater efficiencies in sampling sources of uncertainty [Papers 4,8,9,36,37,38,46,55], and ideas for leveraging code design to more easily synthesize models and observations [Papers 2,3,15,35]. The topic that most undermines efforts to assess model credibility is the challenge to quantify the effects of model biases on model predictions, particularly when extrapolating into regimes for which we do not have observational data [27].…”

Section: Grand Challengementioning

confidence: 99%

“…Focused on complete algorithmic descriptions of all processes, couplings would be synchronized based on their degree of strong-versus-weak and tight-versus-loose connections as defined by the computational science community [20].…”

Section: Opportunities and Potential Solutionsmentioning

confidence: 99%

Advances in Cross-Cutting Ideas for Computational Climate Science

Ng¹,

Evans²,

Caldwell³

et al. 2017

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Rockville, MDCover Clockwise, beginning with the top of the cube:Description: Mesh representation of the coastlines of Greenland within the CISM-Albany model using a new algebraic multigrid solver, an ice sheet component option within ACME.Courtesy of Ray Tuminaro, Mauro Perego, Irina Tezaur, Andrew Salinger, Sandia National Laboratories, and Stephen Price, Los Alamos National Laboratory.Description: Monthly averaged Sea Surface Temperature from years 71-100 of a test simulation of the beta v1 ACME model with active ocean, sea ice, atmosphere, and land surface components.Courtesy of Milena Veneziani, Los Alamos National Laboratory, and the DOE ACME ocean model development team.Description: Snapshot of instantaneous integrated water vapor from a development version of the ACME model atmosphere component.Courtesy of Kate Evans, Oak Ridge National Laboratory, and the DOE ACME atmosphere model development team.

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“…Equation 1 is a simplified schema of systems described by first principles in, e.g., [8] for porous media applications or [21] for reacting flows. In turn, such systems may be regarded as embedded in multiphysics applications for which computational modelers increasingly prefer fully implicit solvers [13] for reasons of numerical efficiency, stability, and/or robustness. Past generations of modelers lacking powerful high performance solvers have tended to employ operator splitting to solve such systems in a series of steps that leave behind first-order temporal splitting errors and potentially destabilizing mechanisms.…”

Section: Multi-component Applicationsmentioning

confidence: 99%

High Performance Multi-GPU SpMV for Multi-component PDE-Based Applications

Abdelfattah

Ltaief

Keyes

2015

Lecture Notes in Computer Science

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Abstract. Leveraging optimization techniques (e.g., register blocking and double buffering) introduced in the context of KBLAS, a Level 2 BLAS high performance library on GPUs, the authors implement dense matrix-vector multiplications within a sparse-block structure. While these optimizations are important for high performance dense kernel executions, they are even more critical when dealing with sparse linear algebra operations. The most time-consuming phase of many multicomponent applications, such as models of reacting flows or petroleum reservoirs, is the solution at each implicit time step of large, sparse spatially structured or unstructured linear systems. The standard method is a preconditioned Krylov solver. The Sparse Matrix-Vector multiplication (SpMV) is, in turn, one of the most time-consuming operations in such solvers. Because there is no data reuse of the elements of the matrix within a single SpMV, kernel performance is limited by the speed at which data can be transferred from memory to registers, making the bus bandwidth the major bottleneck. On the other hand, in case of a multi-species model, the resulting Jacobian has a dense block structure. For contemporary petroleum reservoir simulations, the block size typically ranges from three to a few dozen among different models, and still larger blocks are relevant within adaptively model-refined regions of the domain, though generally the size of the blocks, related to the number of conserved species, is constant over large regions within a given model. This structure can be exploited beyond the convenience of a block compressed row data format, because it offers opportunities to hide the data motion with useful computations. The new SpMV kernel outperforms existing state-of-the-art implementations on single and multi-GPUs using matrices with dense block structure representative of porous media applications with both structured and unstructured multi-component grids.

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Multiphysics simulations: challenges and opportunities.

Cited by 91 publications

References 292 publications

Enabling the environmentally clean air transportation of the future: a vision of computational fluid dynamics in 2030

Enabling the environmentally clean air transportation of the future: a vision of computational fluid dynamics in 2030

Advances in Cross-Cutting Ideas for Computational Climate Science

High Performance Multi-GPU SpMV for Multi-component PDE-Based Applications

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