Uncertainty quantification in numerical simulation of particle-laden flows

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

doi:10.1007/s10596-016-9563-6

Cited by 8 publications

(12 citation statements)

References 41 publications

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“…In this section, we evaluate A-Chiron for extracting and indexing raw data during the execution of data-intensive scientific workflows. Our experiments are based on a scientific workflow in Computational Fluid Dynamics (CFD) domain [17] (see Section 6.1). Although our experimental evaluation considers a specific CFD workflow, workflows from other domains, like those in astronomy [16], geophysics [17] and bioinformatics [40], can also benefit from the raw data analysis support of ARMFUL.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Our experiments are based on a scientific workflow in Computational Fluid Dynamics (CFD) domain [17] (see Section 6.1). Although our experimental evaluation considers a specific CFD workflow, workflows from other domains, like those in astronomy [16], geophysics [17] and bioinformatics [40], can also benefit from the raw data analysis support of ARMFUL. In the following experimental evaluation, we compare the cost with and without raw data file analysis, considering two strategies that apply indexing techniques, and present the analytical capabilities enhanced using our proposed approach.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The nonlinearities due to the convective term on the Navier-Stokes equation (describes the motion of viscous flow) are treated by an Inexact Newton-GMRES algorithm [17]. In this solution algorithm, at the beginning of the nonlinear iterations in each time step, the algorithm computes large linear tolerances, producing fast nonlinear steps.…”

Section: Case Study: Computational Fluid Dynamics Workflowmentioning

confidence: 99%

“…In its current version, it is implemented in an extended version of Chiron and evaluated in an HPC environment with a real numerical simulation workflow. Experiments with a finite element solver for fluid dynamics [17] workflow show the performance improvements in runtime queries obtained by the ARMFUL indexing techniques. The results present relevant costs/benefits considering the overheads of managing and indexing raw data, with further gains from powerful runtime data analyses obtained by queries accessing data directly.…”

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confidence: 99%

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Raw data queries during data-intensive parallel workflow execution

Silva

Leite

Camata

et al. 2017

Future Generation Computer Systems

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Computer simulations consume and produce huge amounts of raw data files presented in different formats, e.g., HDF5 in computational fluid dynamics simulations. Users often need to analyze domain-specific data based on related data elements from multiple files during the execution of computer simulations. In a raw data analysis, one should identify regions of interest in the data space and retrieve the content of specific related raw data files. Existing solutions, such as FastBit and RAW, are limited to a single raw data file analysis and can only be used after the execution of computer simulations. Scientific Workflow Management Systems (SWMS) can manage the dataflow of computer simulations and register related raw data files at a provenance database. This paper aims to combine the advantages of a dataflow-aware SWMS and the raw data file analysis techniques to allow for queries on raw data file elements that are related, but reside in separate files. We propose a component-based architecture, named as ARMFUL (Analysis of Raw data from Multiple Files) with raw data extraction and indexing techniques, which allows for a direct access to specific elements or regions of raw data space. ARMFUL innovates by using a SWMS provenance database to add a dataflow access path to raw data files. ARMFUL facilitates the invocation of ad-hoc programs and third party tools (e.g., FastBit tool) for raw data analyses. In our experiments, a real parallel computational fluid dynamics is executed, exploring different alternatives of raw data extraction, indexing and analysis.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Case Study: Computational Fluid Dynamics Workflowmentioning

confidence: 99%

mentioning

confidence: 99%

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Raw data queries during data-intensive parallel workflow execution

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Leite

Camata

et al. 2017

Future Generation Computer Systems

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“…Finite element methods have been used successfully [13][14][15][16] and more recent studies include uncertainty quantification. 17,18 This type of model has found its way into the study of geophysical problems such as atmospheric 19 and ocean 20 modeling, where large areas need to be examined. In addition, shallow water models have been derived for compressible flows, such as for gas motion 21 and magnetohydrodynamics.…”

Section: Introductionmentioning

confidence: 99%

A shallow water event‐driven approach to simulate turbidity currents at stratigraphic scale

Santos

Lopes²,

Coutinho

2020

Numerical Methods in Fluids

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Summary We present a new event‐driven approach that combines a shallow water flow model with a practical sedimentation technique to simulate the formation of turbidite depositional systems at a stratigraphic scale. The equations that govern turbidity currents dynamics are solved using a new finite element flux‐corrected transport scheme. In this sense, the low‐order formulation is built by adding a novel Rusanov‐like scalar dissipation scaled by a shock‐capturing operator to standard Galerkin equations. From it, the high‐order system is obtained by including antidiffusive fluxes linearized around the low‐order solution and limited with the Zalesak's algorithm, following a minmod prelimiter. Implicit time integration with adaptive time steps is performed with an iterative nonlinear scheme that linearizes source terms. Sedimentation is implemented by carrying five granulometric fractions (clay, silt, and fine, medium, and coarse sands) along evolved streaklines and radially scattering sediments that deposit filling the available depositional space and compacting the underneath sediment layers. The flow is computed while an event discharge into an area of interest is active, or the inflow current has not reached an equilibrium state. Afterward, the event deposition step is executed. Numerical results of our flow solver presented a good agreement with available exact and literature solutions, and the simulated sediment deposits suggest that our approach is well suited for stratigraphic scale simulations.

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In situ visualization and data analysis for turbidity currents simulation

Camata

Silva

Valduriez

et al. 2018

Computers & Geosciences

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International audienceTurbidity currents are underflows responsible for sediment deposits that generate geological formations of interest for the oil and gas industry. libMesh-sedimentation is an application built upon the libMesh library to simulate turbidity currents. In this work, we present the integration of libMesh-sedimentation with in situ visualization and in transit data analysis tools. DfAnalyzer is a solution based on provenance data to extract and relate strategic simulation data in transit from multiple data for online queries. We integrate libMesh-sedimentation and ParaView Catalyst to perform in situ data analysis and visualization. We present a parallel performance analysis for two turbidity currents simulations showing that the overhead for both in situ visualization and in transit data analysis is negligible. We show that our tools enable monitoring the sediments appearance at runtime and steer the simulation based on the solver convergence and visual information on the sediment deposits, thus enhancing the analytical power of turbidity currents simulations

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Uncertainty quantification in numerical simulation of particle-laden flows

Cited by 8 publications

References 41 publications

Raw data queries during data-intensive parallel workflow execution

Raw data queries during data-intensive parallel workflow execution

A shallow water event‐driven approach to simulate turbidity currents at stratigraphic scale

In situ visualization and data analysis for turbidity currents simulation

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