The main limitation of Computational Fluid Dynamics (CFD) lies in the unacceptable computational capability needed for performing accurate simulations for most real-time applications, and in the reduced accuracy of computationally more efficient low fidelity models for security-related applications. One example is the treatment of complex turbulent flows, where low-fidelity models introduce simplifications and source of uncertainties. A promising solution to improve accuracy is to use additional information about the actual flow field, such as experimental data taken on the system. The dynamic data-driven paradigm allows the direct incorporation of the knowledge coming from the measurements within the simulation, thus improving the model estimate itself by minimising its misfit with the data. In this work, the Kalman filter algorithm for data assimilation is combined with the segregated method for CFD modelling to get an integrated algorithm for resolving the incompressible Navier-Stokes equations along with the temperature one. The main novelty lies because such an integrated approach allows preserving mass conservation. This algorithm is herein validated regarding an instrumented cooling channel of the TRIGA Mark II reactor at the University of Pavia, using experimental data on temperature. The main takeaway of this validation is that, despite having only measurements on one quantity, also the prediction on velocity is improved regarding the standard segregated CFD algorithm. The prediction of the state also improves in domain locations where experimental data are not available. The increase in computational time is still lower than the one needed for a more accurate simulation.

Assessment of the integrated mass conservative Kalman filter algorithm for Computational Thermo-Fluid Dynamics on the TRIGA Mark II reactor

Introini C.;Lorenzi S.;Antonio Cammi
2021

Abstract

The main limitation of Computational Fluid Dynamics (CFD) lies in the unacceptable computational capability needed for performing accurate simulations for most real-time applications, and in the reduced accuracy of computationally more efficient low fidelity models for security-related applications. One example is the treatment of complex turbulent flows, where low-fidelity models introduce simplifications and source of uncertainties. A promising solution to improve accuracy is to use additional information about the actual flow field, such as experimental data taken on the system. The dynamic data-driven paradigm allows the direct incorporation of the knowledge coming from the measurements within the simulation, thus improving the model estimate itself by minimising its misfit with the data. In this work, the Kalman filter algorithm for data assimilation is combined with the segregated method for CFD modelling to get an integrated algorithm for resolving the incompressible Navier-Stokes equations along with the temperature one. The main novelty lies because such an integrated approach allows preserving mass conservation. This algorithm is herein validated regarding an instrumented cooling channel of the TRIGA Mark II reactor at the University of Pavia, using experimental data on temperature. The main takeaway of this validation is that, despite having only measurements on one quantity, also the prediction on velocity is improved regarding the standard segregated CFD algorithm. The prediction of the state also improves in domain locations where experimental data are not available. The increase in computational time is still lower than the one needed for a more accurate simulation.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0029549321003836-main.pdf

Accesso riservato

Descrizione: Articolo principale
: Publisher’s version
Dimensione 4.33 MB
Formato Adobe PDF
4.33 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1193930
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact