Quasi-DNS capabilities of OpenFOAM for different mesh types

Abstract Experimental limitations for certain nuclear reactor safety applications have pushed forward the demand for high fidelity DNS reference solutions for complex geometric configurations such as a T-junction or a spherical pebble bed. The application of traditional high-order DNS codes is limited to simple flow domains such as a periodic box or channel. As a possibility to create reference DNS solutions for more complex geometries, we have assessed the (quasi-)DNS capabilities of the OpenFOAM finite volume CFD solver for both structured hexahedral meshes and arbitrary polyhedral meshes. The feasibility of (quasi-)DNS analyses on polyhedral grids is of main interest, since this may offer the possibility to significantly expand the availability of (quasi-)DNS-quality data on arbitrarily complex geometries. In order to have a basis for the considered assessment, the mutual differences between generally recognized reference DNS data bases for turbulent channel and pipe flows are determined first. Subsequently, the differences between these reference DNS solutions and the present OpenFOAM (quasi-)DNS solutions are quantified for the considered mesh types. We use an existing finite volume CFD method and well known turbulent channel and pipe flow DNS reference cases for the assessments in this paper. New in this paper are the application of this CFD method to (quasi-)DNS analyses using arbitrary polyhedral meshes, and the quantification of respectively the mutual differences between generally recognized reference DNS data bases and the differences between the obtained OpenFOAM (quasi-)DNS data and these reference DNS data bases. Based on the presented assessment, it is observed that the differences between the OpenFOAM solutions and the considered reference DNS solutions are practically the same as the mutual differences between these reference DNS solutions when structured hexahedral meshes are used. Furthermore, it is observed that the differences as obtained by OpenFOAM on extruded polyhedral meshes are practically the same as those obtained for the structured hexahedral meshes. In contrast, the full polyhedral mesh shows somewhat larger differences near the peaks in the rms velocity profiles, whereas the differences in the bulk flow are again practically the same as those for the hexahedral grids.

[1]  Adrien Toutant,et al.  Large Eddy Simulations of a turbulent periodic channel with conjugate heat transfer at low Prandtl number , 2012 .

[2]  Patrick J. Roache,et al.  Verification and Validation in Computational Science and Engineering , 1998 .

[3]  J. Hooper,et al.  Large-scale structural effects in developed turbulent flow through closely-spaced rod arrays , 1984, Journal of Fluid Mechanics.

[4]  Leon Cizelj,et al.  DNS of turbulent channel flow with conjugate heat transfer at Prandtl number 0.01 , 2012 .

[5]  Bengt Fornberg,et al.  A practical guide to pseudospectral methods: Introduction , 1996 .

[6]  J. C. Vassilicos,et al.  A numerical strategy to combine high-order schemes, complex geometry and parallel computing for high resolution DNS of fractal generated turbulence , 2010 .

[7]  Hrvoje Jasak,et al.  Error analysis and estimation for the finite volume method with applications to fluid flows , 1996 .

[8]  E.M.J. Komen,et al.  Application of large-eddy simulation to pressurized thermal shock: Assessment of the accuracy , 2011 .

[9]  Ferry Roelofs,et al.  Inter fuel-assembly thermal-hydraulics for the ELSY square open reactor core design , 2010 .

[10]  Mats Henriksson,et al.  High-Cycle Thermal Fatigue in Mixing Tees: Large-Eddy Simulations Compared to a New Validation Experiment , 2008 .

[11]  Richard J. A. Howard,et al.  Large Eddy Simulation and the effect of the turbulent inlet conditions in the mixing Tee , 2011 .

[12]  E.M.J. Komen,et al.  Suitability of wall-functions in Large Eddy Simulation for thermal fatigue in a T-junction , 2010 .

[13]  L. Meyer,et al.  Experimental investigation of turbulent transport of momentum and energy in a heated rod bundle , 1998 .

[14]  Frank-Peter Weiss,et al.  Experiments on slug mixing under natural circulation conditions at the ROCOM test facility using high-resolution measurement techniques and numerical modeling , 2010 .

[15]  Javier Jiménez,et al.  Scaling of the velocity fluctuations in turbulent channels up to Reτ=2003 , 2006 .

[16]  Christophe Pe´niguel,et al.  Presentation of a Numerical 3D Approach to Tackle Thermal Striping in a PWR Nuclear T-Junction , 2003 .

[17]  Ylva Odemark,et al.  High-Cycle Thermal Fatigue in Mixing Tees: New Large-Eddy Simulations Validated Against New Data Obtained by PIV in the Vattenfall Experiment , 2009 .

[18]  E.M.J. Komen,et al.  Large-Eddy Simulation study of turbulent mixing in a T-junction , 2010 .

[19]  Hrvoje Jasak,et al.  A tensorial approach to computational continuum mechanics using object-oriented techniques , 1998 .

[20]  J. P. Magnaud,et al.  Hydro-thermal-mechanical analysis of thermal fatigue in a mixing tee , 2005 .

[21]  Javier Jiménez,et al.  Spectra of the very large anisotropic scales in turbulent channels , 2003 .

[22]  Elia Merzari,et al.  Numerical simulation of flows in tight-lattice fuel bundles , 2008 .

[23]  A. K. Kuczaj,et al.  An Assessment of Large-Eddy Simulation Toward Thermal Fatigue Prediction , 2010 .

[24]  Alexandre Chatelain.  Simulation des Grandes Echelles d'écoulements turbulents avec transferts de chaleur , 2004 .

[25]  E.M.J. Komen,et al.  Optimization of a pebble bed configuration for quasi-direct numerical simulation , 2012 .

[26]  Hisashi Ninokata,et al.  CFD and DNS methodologies development for fuel bundle simulations , 2006 .

[27]  Yassin A. Hassan,et al.  Large eddy simulation in pebble bed gas cooled core reactors , 2008 .

[28]  T. Kajishima,et al.  Analysis of dynamical flow structure in a square arrayed rod bundle , 2010 .

[29]  Borut Mavko,et al.  DNS of Turbulent Heat Transfer in Channel Flow With Heat Conduction in the Solid Wall , 2001 .

[30]  Hiroshi Kawamura,et al.  DNS of turbulent heat transfer in channel flow with low to medium-high Prandtl number fluid , 1998 .

[31]  Soeren Kliem,et al.  Experiments at the mixing test facility ROCOM for benchmarking of CFD codes , 2008 .

[32]  Ferry Roelofs,et al.  A stepwise development and validation of a RANS based CFD modelling approach for the hydraulic and thermal-hydraulic analyses of liquid metal flow in a fuel assembly , 2009 .

[33]  E.M.J. Komen,et al.  Quasi-direct numerical simulation of a pebble bed configuration, Part-II: Temperature field analysis , 2013 .