Numerical simulation of particle‐laden flows by the residual‐based variational multiscale method

Guerra, Gabriel M.; Zio, Souleymane; Camata, José J.; Rochinha, Fernando A.; Elias, Renato N.; Paraizo, Paulo L.B.; Coutinho, Álvaro L. G. A.

doi:10.1002/fld.3820

Cited by 16 publications

(11 citation statements)

References 55 publications

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“…Next, we consider two fluid dynamics applications where the governing equations for both cases are advection-dominated. To circumvent the LBB condition and spurious oscillations regarding dominant advection, these equations consider the residual-based variational multiscale (RBVMS) formulation [ 1 , 8 , 19 , 35 , 39 , 53 ] on a finite element discretization. We consider the use of DMD on a 2D density-driven gravity flow and a 3D bubble rising simulation.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Dynamic mode decomposition in adaptive mesh refinement and coarsening simulations

Barros

Grave

Viguerie

et al. 2021

Engineering with Computers

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Dynamic mode decomposition (DMD) is a powerful data-driven method used to extract spatio-temporal coherent structures that dictate a given dynamical system. The method consists of stacking collected temporal snapshots into a matrix and mapping the nonlinear dynamics using a linear operator. The classical procedure considers that snapshots possess the same dimensionality for all the observable data. However, this often does not occur in numerical simulations with adaptive mesh refinement/coarsening schemes (AMR/C). This paper proposes a strategy to enable DMD to extract features from observations with different mesh topologies and dimensions, such as those found in AMR/C simulations. For this purpose, the adaptive snapshots are projected onto the same reference function space, enabling the use of snapshot-based methods such as DMD. The present strategy is applied to challenging AMR/C simulations: a continuous diffusion–reaction epidemiological model for COVID-19, a density-driven gravity current simulation, and a bubble rising problem. We also evaluate the DMD efficiency to reconstruct the dynamics and some relevant quantities of interest. In particular, for the SEIRD model and the bubble rising problem, we evaluate DMD’s ability to extrapolate in time (short-time future estimates).

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Section: Numerical Experimentsmentioning

confidence: 99%

Dynamic mode decomposition in adaptive mesh refinement and coarsening simulations

Barros

Grave

Viguerie

et al. 2021

Engineering with Computers

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“…An alternative approach for representing complex seafloor topography involves the use of finite element methods. This line of research is pursued by Coutinho and colleagues [99][100][101][102], who apply parallel stabilized finite element methods with adaptive meshes to lockexchange type gravity and turbidity currents. A spectral element approach toward simulating the propagation of gravity currents over complex seafloor topography is employed by € Ozg€ okmen et al [103].…”

Section: -10 / Vol 67 July 2015mentioning

confidence: 99%

Modeling Gravity and Turbidity Currents: Computational Approaches and Challenges

Meiburg

Radhakrishnan

Nasr-Azadani

2015

Applied Mechanics Reviews

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In this review article, we discuss recent progress with regard to modeling gravity-driven, high Reynolds number currents, with the emphasis on depth-resolving, high-resolution simulations. The initial sections describe new developments in the conceptual modeling of such currents for the purpose of identifying the Froude number–current height rela-tionship, in the spirit of the pioneering work by von Karman and Benjamin. A brief intro-duction to depth-averaged approaches follows, including box models and shallow water equations. Subsequently, we provide a detailed review of depth-resolving modeling strat-egies, including direct numerical simulations (DNS), large-eddy simulations (LES), and Reynolds-averaged Navier–Stokes (RANS) simulations. The strengths and challenges associated with these respective approaches are discussed by highlighting representative computational results obtained in recent years. [DOI: 10.1115/1.4031040]

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“…Turbidity current experiments may be improved considering the stochasticity of fluid properties and boundary and initial conditions. The numerical methods used in the experiments of the present work are described in detail in [15].…”

Section: Uncertainty Quantification Workflowmentioning

confidence: 99%

Data-centric iteration in dynamic workflows

Dias

Guerra

Rochinha

et al. 2015

Future Generation Computer Systems

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SummaryDynamic workflows are scientific workflows to support computational science simulations, typically using dynamic processes based on runtime scientific data analyses. They require the ability of adapting the workflow, at runtime, based on user input and dynamic steering. Supporting data-centric iteration is an important step towards dynamic workflows because user interaction with workflows is iterative. However, current support for iteration in scientific workflows is static and does not allow for changing data at runtime. In this paper, we propose a solution based on algebraic operators and a dynamic execution model to enable workflow adaptation based on user input and dynamic steering. We introduce the concept of iteration lineage that makes provenance data management consistent with dynamic iterative workflow changes. Lineage enables scientists to interact with workflow data and configuration at runtime through an API that triggers steering. We evaluate our approach using a novel and real large-scale workflow for uncertainty quantification on a 640-core cluster. The results show impressive execution time savings from 2.5 to 24 days, compared to noniterative workflow execution. We verify that the maximum overhead introduced by our iterative model is less than 5% of execution time. Also, our proposed steering algorithms are very efficient and run in less than 1 millisecond, in the worst-case scenario.

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Numerical simulation of particle‐laden flows by the residual‐based variational multiscale method

Cited by 16 publications

References 55 publications

Dynamic mode decomposition in adaptive mesh refinement and coarsening simulations

Dynamic mode decomposition in adaptive mesh refinement and coarsening simulations

Modeling Gravity and Turbidity Currents: Computational Approaches and Challenges

Data-centric iteration in dynamic workflows

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