mlhp 0.2.4

About

Mlhp is a C++ library with python bindings that implements multi-level hp- and other finite element methods efficiently also for dimensions > 3. A particular strength of mlhp is the support for non-conforming finite elements (e.g. the Finite Cell Method). It provides the technology to directly simulate with implicitly defined geometries, STLs, and voxel-based data without the need to create a conforming mesh. The project is actively developed and documents mostly through examples and comments. Full backward compatibility may occasionally be broken when a refactor or new feature requires API changes.

Python package

We provide a substantial subset of mlhp's functionality through pre-compiled wheels available on PyPI:

pip install mlhp

These wheels are available for most common platforms and for recent Python versions. We require no additional dependencies besides those already baked into the wheels. As a result, you get a fully functioning finite element kernel that can be interfaced with numpy/scipy/cupy relatively easily and provides C-interfaces for extensions similar to Ansys/Abaqus user subroutines that can be compiled directly in Python using cffi/numba.

Use the Python examples as guidance; they are shipped with our wheels (try mlhp.examples()), but you can also access them via this repo (examples/). In addition, help() provides more granular information and documents overload resolution.

Submodules

The header-only libraries pybind11, Catch2, and vtu11 are included as git submodules. To clone them recursively, use:

git clone --recursive https://gitlab.com/hpfem/code/mlhp.git

Getting started

• Examples
• Compiler and platform support
• Setting up an executable that uses mlhp.
• Core class structure
• Doxygen documentation

References and publications

If you use our code for your scientific research, please acknowledge this by referring to the following publication:

P. Kopp, E. Rank, V. M. Calo, S. Kollmannsberger, 2022: Efficient multi-level hp-finite elements in arbitrary dimensions, Computer Methods in Applied Mechanics and Engineering, Volume 401, Part B, 115575, DOI: 10.1016/j.cma.2022.115575

Here are some of the publications using this project:

• V. Holla, P. Kopp, J. Grünewald; P. Praegla, C. Meier, K. Wudy, S. Kollmannsberger, 2023: Laser beam shape optimization: Exploring alternative profiles to Gaussian-shaped laser beams in powder bed fusion of metals, 2023 International Solid Freeform Fabrication Symposium, DOI: 10.26153/TSW/50986
• V. Holla, P. Kopp, J. Grünewald, K. Wudy, S. Kollmannsberger, 2023: Laser beam shape optimization in powder bed fusion of metals, Additive Manufacturing, Volume 72, 103609, DOI: 10.1016/j.addma.2023.103609
• P. Kopp, V. M. Calo, E. Rank, S. Kollmannsberger, 2022: Space-time hp-finite elements for heat evolution in laser powder bed fusion additive manufacturing, Engineering with Computers, Volume 38, pages 4879–4893, DOI: 10.1007/s00366-022-01719-1
• S. Kollmannsberger; P. Kopp, 2021: On accurate time integration for temperature evolutions in additive manufacturing, GAMM-Mitteilungen, Volume 44, Issue 4, DOI: 10.1002/gamm.202100019