HiFlow3 – Technical Report on Release 2.0

  • Simon Gawlok (Author)
  • Philipp Gerstner (Author)
  • Saskia Haupt (Author)
  • Vincent Heuveline (Author)
  • Jonas Kratzke (Author)
  • Philipp Lösel (Author)
  • Katrin Mang (Author)
  • Mareike Schmidtobreick (Author)
  • Nicolai Schoch (Author)
  • Nils Schween (Author)
  • Jonathan Schwegler (Author)
  • Chen Song (Author)
  • Martin Wlotzka (Author)

Abstract

HiFlow3 Version 2.0 continues the path as a multi-purpose finite element software, which provides powerful tools for efficient and accurate solution of a wide range of problems modeled by partial differen- tial equations (PDEs). New features and functionalities, which allow to run numerical simulations with more advanced solution algorithms and discretizations in comparison to previous releases of HiFlow3, have been implemented. These comprise Uncertainty Quantification (UQ), energy-efficient multigrid techniques, Schur complement solvers for saddle-point problems, extended third-party library support, and adaptive local mesh refinement in a parallel computing environment. Furthermore, HiFlow3 has been successfully integrated into advanced and state-of-the-art simulation environments by means of the Medical Simulation Markup Language (MSML), for example.

The presented new algorithms and features as well as general under-the-hood improvements have enabled excellent and relevant research activities in the fields of both medical engineering and meteorol- ogy and environmental sciences. The described show cases demonstrate the potential and advantages, which HiFlow3 can offer in performing a numerical simulation by means of finite element methods. Especially, the high performance computing capabilities of HiFlow3 – not only in the mentioned fields of applications, but also in general – have been significantly improved in Version 2.0.

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Published
2017-11-22
Language
en
Academic discipline and sub-disciplines
Applied Mathematics; Numerics; Scientific Computing
Keywords
Applied Mathematics; Scientific Computing; Optimization; Uncertainty Quantification; High Performance Computing