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International Journal for Uncertainty Quantification
Импакт фактор: 3.259 5-летний Импакт фактор: 2.547 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

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International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.v2.i1.50
pages 53-71

UNCERTAINTY QUANTIFICATION IN COMPUTATIONAL PREDICTIVE MODELS FOR FLUID DYNAMICS USING A WORKFLOW MANAGEMENT ENGINE

Gabriel Guerra
Mechanical Engineering Department, Federal University of Rio de Janeiro, Brazil
Fernando A. Rochinha
COPPE, Universidade Federal do Rio de Janeiro Brazil
Renato Elias
High Performance Computing Center, Federal University of Rio de Janeiro, Brazil
Daniel de Oliveira
Systems Engineering and Computer Science Department, Federal University of Rio de Janeiro, Brazil
Eduardo Ogasawara
Systems Engineering and Computer Science Department, Federal University of Rio de Janeiro, Brazil
Jonas Furtado Dias
Systems Engineering and Computer Science Department, Federal University of Rio de Janeiro, Brazil
Marta Mattoso
Systems Engineering and Computer Science Department, Federal University of Rio de Janeiro, Brazil
Alvaro L. G. A. Coutinho
High Performance Computing Center

Краткое описание

Computational simulation of complex engineered systems requires intensive computation and a significant amount of data management. Today, this management is often carried out on a case-by-case basis and requires great effort to track it. This is due to the complexity of controlling a large amount of data flowing along a chain of simulations. Moreover, many times there is a need to explore parameter variability for the same set of data. On a case-by-case basis, there is no register of data involved in the simulation, making this process prone to errors. In addition, if the user wants to analyze the behavior of a simulation sample, then he/she must wait until the end of the whole simulation. In this context, techniques and methodologies of scientific workflows can improve the management of simulations. Parameter variability can be put in the general context of uncertainty quantification (UQ), which provides a rational perspective for analysts and decision makers. The objective of this work is to use scientific workflows to provide a systematic approach in: (i) modeling UQ numerical experiments as scientific workflows, (ii) offering query tools to evaluate UQ processes at runtime, (iii) managing the UQ analysis, and (iv) managing UQ in parallel executions. When using scientific workflow engines, one can collect data in a transparent manner, allowing execution steering, the postassessment of results, and providing the information for reexecuting the experiment, thereby ensuring reproducibility, an essential characteristic in a scientific or engineering computational experiment.


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