NDLI: Uncertainty quantification in numerical simulation of particle-laden flows

Content Provider	Springer Nature Link
Author	Guerra, Gabriel M. Zio, Souleymane Camata, Jose J. Dias, Jonas Elias, Renato N. Mattoso, Marta B. Paraizo, Paulo L. G. A. Coutinho, Alvaro L. Rochinha, Fernando A.
Copyright Year	2016
Abstract	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.
Starting Page	265
Ending Page	281
Page Count	17
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	20
Issue Number	1
e-ISSN	15731499
Language	English
Publisher	Springer International Publishing
Publisher Date	2016-02-24
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Particle-laden flows Uncertainty quantification Scientific workflows High performance computing Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

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