SCCS Colloquium - Oct 24, 2019
|Date:||October 24, 2019|
|Time:||15:00 - 16:00|
Ayman Noureldin: A Master-Slave Approach for Multi-Phase Fluid-Fluid Coupling of OpenFOAM and ATHLET
A Semi-Implicit coupling approach is used to couple two completely disparate simulation packages to solve complex fluid dynamic problems. The first package is ATHLET, a reactor system simulation code that solves the thermal-fluid-dynamical behavior of the system in 1D, and it predicts the consequences of transients or accident scenarios in the reactor core. ATHLET uses the lumped parameter method, which is based on the FV method on a staggered grid, along with other approximations for the two-phase flow modelling. The second package is OpenFOAM, a CFD framework that contains an enormous amount of solvers and numerical schemes. OpenFOAM treats complex 3D problems and applies different schemes for time marching, linearlization, and interpolation. In addition, it can simulate 3D effects and demonstrate the existing local phenomena when applied on the reactor core environment. In this work, we try to apply Semi-implicit coupling between these two packages for two-phase flow problems, and we focus our calculations on fluid mixtures constituting of liquid and non-condensable gases. In addition, we tried to enforce conservation of the flow parameters through the interface while controlling the stability of both solvers and maintaining a reasonable conversion in the calculations.
Kilian Glas: Exploitation of component grid symmetries for sparse grid density estimation with the combination method
Bachelor's thesis submission talk. Kilian is advised by Kilian Röhner.
In this bachelor thesis, a method is introduced to improve the offline step of sparse grid density estimation with the combination technique. The developed approach exploits the geometrical properties of the subgrids in the combination scheme, to transform already decomposed corresponding system matrices. The transformation consists of a symmetric permutation of the system matrix, as well as the elementwise multiplication of a dimension blow-up factor. The former can be applied when two subgrids have level vectors, that are permutations of each other, while the latter yields an embedding into higher dimensions. The applicability is examined for the orthogonal decomposition into hessenberg form and the cholesky decomposition. For the orthogonal decomposition, the method has been implemented. Compared to the current implementation, it provides a speed up from cubic to quadratic time, for suitable component grids.
Keywords: Sparse Grids, sparse grid density estimation, combination technique