Uncertainty quantification in numerical simulation of particle-laden flows

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• 作者：Gabriel M. Guerra ; Souleymane Zio ; Jose J. Camata ; Jonas Dias…
• 关键词：Particle ; laden flows ; Uncertainty quantification ; Scientific workflows ; High performance computing
• 刊名：Computational Geosciences
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：20
• 期：1
• 页码：265-281
• 全文大小：3,775 KB
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• 作者单位：Gabriel M. Guerra (1)
  Souleymane Zio (1)
  Jose J. Camata (3)
  Jonas Dias (4)
  Renato N. Elias (3)
  Marta Mattoso (4)
  Paulo L. B. Paraizo (2)
  Alvaro L. G. A. Coutinho (3)
  Fernando A. Rochinha (1)
  
  1. Mechanical Engineering Department, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
  3. High Performance Computing Center and Department of Civil Engineering, COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
  4. Department of Computer Science, COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
  2. Petrobras UO-SEAL Sergipe Operational Unity, Aracaju, Sergipe, Brazil
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Mathematical Modeling and IndustrialMathematics
  Geotechnical Engineering
  Hydrogeology
  Soil Science and Conservation
  
• 出版者：Springer Netherlands
• ISSN：1573-1499

文摘

Numerical models can help to push forward the knowledge about complex dynamic physical systems. Modern approaches employ detailed mathematical models, taking into consideration inherent uncertainties on input parameters (phenomenological parameters or boundary and initial conditions, among others). Particle-laden flows are complex physical systems found in nature, generated due to the (possible small) spatial variation on the fluid density promoted by the carried particles. They are one of the main mechanisms responsible for the deposition of sediments on the seabed. A detailed understanding of particle-laden flows, often referred to as turbidity currents, helps geologists to understand the mechanisms that give rise to reservoirs, strategic in oil exploration. Uncertainty quantification (UQ) provides a rational framework to assist in this task, by combining sophisticated computational models with a probabilistic perspective in order to deepen the knowledge about the physics of the problem and to access the reliability of the results obtained with numerical simulations. This work presents a stochastic analysis of sediment deposition resulting from a turbidity current considering uncertainties on the initial sediment concentrations and particles settling velocities. The statistical moments of the deposition mapping, like other important features of the currents, are approximated by a Sparse Grid Stochastic Collocation method that employ a parallel flow solver for the solution of the deterministic problems associated to the grid points. The whole procedure is supported and steered by a scientific workflow management engine designed for high performance computer applications.