Computer Science – Computational Engineering – Finance – and Science
Scientific paper
2011-02-15
Computer Science
Computational Engineering, Finance, and Science
To appear in Advances in Water Resources
Scientific paper
10.1016/j.advwatres.2011.02.013
Finding an appropriate turbulence model for a given flow case usually calls for extensive experimentation with both models and numerical solution methods. This work presents the design and implementation of a flexible, programmable software framework for assisting with numerical experiments in computational turbulence. The framework targets Reynolds-averaged Navier-Stokes models, discretized by finite element methods. The novel implementation makes use of Python and the FEniCS package, the combination of which leads to compact and reusable code, where model- and solver-specific code resemble closely the mathematical formulation of equations and algorithms. The presented ideas and programming techniques are also applicable to other fields that involve systems of nonlinear partial differential equations. We demonstrate the framework in two applications and investigate the impact of various linearizations on the convergence properties of nonlinear solvers for a Reynolds-averaged Navier-Stokes model.
Langtangen Hans Petter
Mortensen Mikael
Wells Garth N.
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