Reference : Development of a Data-Driven Approach based on Kalman filtering for CFD Reactor Analysis
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Energy
Computational Sciences
http://hdl.handle.net/10993/36125
Development of a Data-Driven Approach based on Kalman filtering for CFD Reactor Analysis
English
Introini, Carolina []
Cammi, Antonio []
Lorenzi, Stefano []
Baroli, Davide mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Peters, Bernhard mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2018
PHYSOR 2018
Yes
International
PHYSOR 2018: Reactor Physics paving the way towards more efficient systems
from 22 - 04-2018 to 24-04-2018
[en] CFD ; Data-Assimilation
[en] In the last several years, computer-based simulation has become an important analysis
and design tool in many engineering fields. The common practice involves the use of
low-fidelity models, which in most cases are able to provide fairly accurate results while
maintaining a low computational cost. However, for complex systems such as nuclear
reactors, more detailed models are required for the in-depth analysis of the problem at
hand, due for example to the complex geometries of the physical domain. Nevertheless,
such models are affected by potentially critical uncertainties and inaccuracies. In this
context, the use of data assimilation methods such as the Kalman filter to integrate local
experimental data witihin the numerical model looks very promising as a high-fidelity
analysis tool. In this work, the focus is the application of such methods to the problem of
fluid-dynamics analysis of the reactor. Indeed, in terms of nuclear reactor investigation,
a detailed characterization of the coolant behaviour within the reactor core is of manda-
tory importance in order to understand, among others, the operating conditions of the
system, and the potential occurrence of accident scenarios. In this context, the use of data
assimilation methods allows the extraction of information of the thermo-dynamics state
of the system in a benchmarked transitory in order to increase the fidelity of the com-
putational model. Conversely to the current application of control-oriented black-box in
the nuclear energy community, in this work the integration of the data-driven paradigm
into the numerical formulation of the CFD problem is proposed. In particular, the al-
gorithm outlined embeds the Kalman filter into a segregated predictor-corrector formu-
lation, commonly adopted for CFD analysis. Due to the construction of the developed
method, one of the main challenges achieved is the preservation of mass-conservation for
the thermo-dynamics state during each time instant. As a preliminary verification, the
proposed methodology is validated on a benchmark of the lid-driven cavity. The obtained
results highlight the efficiency of the proposed method with respect to the state-of-art low
fidelity approach.
Researchers ; Professionals
http://hdl.handle.net/10993/36125
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