activity
20182026
most citedSimplex Space-Time Meshes in Compressible Flow Simulations

38 citations · 145 across the 19 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

math.NA2022

A Data-driven Reduced Order Modeling Approach Applied In Context Of Numerical Analysis And Optimization Of Plastic Profile Extrusion

Daniel Hilger, Norbert Hosters

In course of this work, we examine the process of plastic profile extrusion, where a polymer melt is shaped inside the so-called extrusion die and fixed in its shape by solidificat…

math.NA2022★ 16 cited

Time-Continuous and Time-Discontinuous Space-Time Finite Elements for Advection-Diffusion Problems

Max von Danwitz, Igor Voulis, Norbert Hosters +1

We construct four variants of space-time finite element discretizations based on linear tensor-product and simplex-type finite elements. The resulting discretizations are continuou…

physics.flu-dyn2022

Numerical Prediction and Post-Test Numerical Analysis of the ASDMAD Wind Tunnel Tests in ETW

Manuel Brüderlin, Norbert Hosters, Bae-Hong Chen +3

This paper presents numerical results in comparison with experimental data of nominally static aeroelastic polars of the second ASDMAD (Aero-Structural Dynamics Methods for Airplan…

cs.CE2022★ 9 cited

A Robin-Neumann Scheme with Quasi-Newton Acceleration for Partitioned Fluid-Structure Interaction

Thomas Spenke, Michel Make, Norbert Hosters

The Dirichlet-Neumann scheme is the most common partitioned algorithm for fluid-structure interaction (FSI) and offers high flexibility concerning the solvers employed for the two…

cs.CE2022

Spline-Based Space-Time Finite Element Approach for Fluid-Structure Interaction Problems With a Focus on Fully Enclosed Domains

Michel Make, Thomas Spenke, Norbert Hosters +1

Non-Uniform Rational B-Spline (NURBS) surfaces are commonly used within Computer-Aided Design (CAD) tools to represent geometric objects. When using isogeometric analysis (IGA), it…

cs.LG2022★ 1 cited

Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data

Philip Heger, Markus Full, Daniel Hilger +1

The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicles th…