• Length:
    7 Weeks
  • Effort:
    5–6 hours per week
  • Price:

    FREE
    Add a Verified Certificate for $55 USD

  • Institution
  • Subject:
  • Level:
    Advanced
  • Language:
    English
  • Video Transcript:
    English

Prerequisites

Basic knowledge of linear algebra and calculus is a prerequisite to understand the content and therefore strongly recommended. General knowledge of HPFEM01 is strongly recommended.

About this course

Engineering simulations are rapidly becoming fundamental in virtually all industrial sectors; from medicine to energy, aerospace and beyond. In this course, you will learn the breakthrough general adaptive finite element methods (AFEM) and open source FEniCS software that will enable you to solve the grand challenges in science and engineering.

In this second course in the series, you will carry out advanced, time-resolved parallel simulations of aerodynamics, allowing you to understand the mechanism of flight.

What you'll learn

  • How to describe the Direct FEM Simulation (DFS) methodology, including adaptive error control, slip boundary condition, and turbulent dissipation
  • Methods for deriving stability estimates for the cG(1)cG(1) FEM applied to Navier-Stokes equations
  • How to account for general FEM-algorithms such as assembly, adaptvity, and local mesh refinement and have a basic understanding of their implementation in FEniCS-HPC
  • How to account for parallel data structures and algorithms for distributed memory architectures in a general FEM-framework and inspect their implementation in FEniCS-HPC: distributed computational mesh, ghost entities, distributed sparse linear and non-linear algebra, local mesh refinement by bisection for a distributed computational mesh, and general goal-oriented adaptive error control
  • Ways to estimate the performance of different parallel algorithms
  • How to use a general framework, such as FEniCS-HPC, to model and solve general PDE on a supercomputer, and specifically aerodynamics problems with DFS

Meet your instructors

Johan Jansson
Associate Professor
KTH Royal Institute of Technology
Johan Hoffman
Professor
KTH Royal Institute of Technology
Massimiliano Leoni
PhD Candidate
KTH Royal Institute of Technology
Laura Saavedra
Lecturer, UPM
Universidad Politécnica de Madrid Madrid
Margarida Moragues
Postdoc
BCAM, Basque Center for Applied Mathematics
Rahul Kumar
Postdoc
BCAM, Basque Center for Applied Mathematics
Frida Svelander
PhD student
KTH Royal Institute of Technology
Cem Degirmenci
PhD student and Visiting Fellow BCAM
KTH Royal Institute of Technology

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