Johan Jansson
Computational Science and Technology
CSC
KTH
Stockholm
and
CFD Computational Technology
Basque Center for Applied Mathematics
Bilbao, Spain
In our research we develop the general adaptive stabilized G2 General
Galerkin / Direct FEM methodology [1] and the FEniCS [2] framework,
specifically focusing on HPC [3, 4] and the Unicorn solver targeting
turbulent continuum models. In this seminar we will present an
overview of the Direct FEM methodology and recent new developments
toward multi-phase flow with applications in marine energy, and a
general do-nothing approach to adaptive error control. The methodology
and software is the basis for several application projects based both
at BCAM and KTH, for example the MSO4SC H2020 project where we develop
an HPC cloud infrastructure, the EUNISON FP7 project on simulating the
human voice based on fundamental mechanics, several projects on
simulating marine energy generation, and aeronautics projects based on
simulating jet engines and flight.
The methodology has several unique aspects which we hope
can advance the field in new directions:
1. The incompressible Navier-Stokes Equations (NSE) are discretized
directly, without applying any filter. Thus, the method does not
approximate any Large Eddy Simulation (LES) filtered solution, but is
instead an approximation of a weak solution, satisfying the weak form
of the NSE.
2. For this method, we have a posteriori error estimates of quantities
of interest with respect to a weak solution, which form the basis for
our adaptive mesh refinement algorithm. The a posteriori error
estimates are based on the solution of an associated adjoint problem
with a goal quantity (such as a drag coefficient) as data.
3. We model turbulent boundary layers by a slip boundary condition
which is a good approximation for small skin friction stress, which
gives enormous savings in computational cost by not having to resolve
a very thin boundary layer.
[1] J. Hoffman, J. Jansson, N. Jansson, R. V. de Abreu, and
C. Johnson. Computability and Adaptivity in CFD. Encyclopedia of
Computational Mechanics (2016).
[2] http://fenicsproject.org
[3] J. Hoffman, J. Jansson, and N. Jansson. FEniCS-HPC: Automated
predictive high-performance finite element computing with applications
in aerodynamics. Proceedings of PPAM 2015. Lecture Notes in Computer
Science (2015)
[4] PRACE Tier-0 Call 8, FEniCS-HPC, J. Hoffman, J. Jansson,
N. Jansson, C. Degirmenci, A. Larcher (2014-2015)
[5] J. Hoffman, J. Jansson, C. Johnson, New Theory of Flight, Journal of Mathematical Fluid Mechanics, 2015