Physics Models: P-EM-UQS
Unstructured Quasi-Static EM Solvers
The Unstructured Quasi-Static Electromagnetic Solvers (P-EM-UQS) are implemented to model static and quasi-static EM regimes efficiently by applying the finite element method on unstructured meshes. The solvers complement the established Sim4Life P-EM-QS solvers and support message passing interface (MPI) parallelization.
Unstructured meshing and FEM simulation to model the neuroelectric response of complex nerves.
The solvers offer a substantial increase in speed and accuracy in multi-scale applications and supports unstructured meshes (e.g., support for localized refinement, or conformal adaptivity)
The benefits of the solvers include unique semi-insulating layers, anisotropic tensorial inhomogeneous electrical conductivity distributions (e.g., image-based).
All solvers come with a dedicated Python API that allows power-users to further automatize processes or perform advanced analyses.
- Transcranial Magnetic or Current Stimulation
- LF Hyperthermia (e.g., with nanoparticles)
- Magneto Hemo-Dynamics
- Sound Exposure (e.g., in MRI)
- Neuro-Prosthetics (retina, cochlea, vestibular, motor)
- Neuro-Motoric Incapacitation
- EM Neuro-Stimulation
- High LF EM Field Safety Assessment (e.g., MR Gradient Coils)
- Physiotherapy Heating
- Magnetic Navigation
- Defibrilator Analysis
- Pacemakers
- Tetrahedral volume mesher
- Robust octree-based adaptive mesher
- Unique semi-insulating layers
- Anisotropic tensorial inhomogeneous electrical conductivity distributions (e.g., image-based)
- Specialized visualization and analysis tools
- Handling and visualization of unstructured result fields
- Dedicated Python API
- MPI parallelization
Simulation of transmit/receive WPT coil system in use for charging an electric vehicle.
Assessment of nerve excitation during transcranial
magnetic stimulation.
Vagus Nerve Stimulation with the electrode wrapped around nerve.
