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LyceanEM

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LyceanEM was conceived to enable rapid assesments of the suitability of difference antenna apertures for a wide range of platforms. This is based upon the use of ray tracing to determine the field of view of points of interest on the platform, whether building, train, plane, or mobile phone handset. Allowing the application of Wheelers formulation of the gain of an aperture.

This has been developed further since that point to include a frequency domain propagation model, allowing for antenna arrays and aperture antennas to be simulated with environment scattering.

Further development is planned for time domain modelling, computational efficiency, and eventually a Finite-Difference Time-Domain algorithm may be implemented to allow for modelling of a wider range of situations, or possibly hybrid modelling. This would use the FDTD algorithm for near field calculations, while using the ray tracing for more sparse situations.

Further documentation can be found here.

If you use LyceanEM in an academic project, please cite our paper:

LyceanEM: A python package for virtual prototyping of antenna arrays, time and frequency domain channel modelling

Core Features

  • 3D Visualization of Platform and Antenna Arrays
  • Aperture Projection
  • Raycasting
  • Frequency Domain Electromagnetics Modelling for scattering, antennas, and antenna array patterns
  • Time Domain Electromagnetics Modelling for scattering, antennas, and antenna array patterns
  • GPU acceleration of core operations

Supported Platforms

The package has been tested on:

  • Ubuntu and Mint 18.04 and 20.04
  • Windows 10 64-bit

With Python versions:

  • 3.7
  • 3.8
  • 3.9
  • 3.10

Installation

LyceanEM uses CUDA for GPU acceleration. The advised installation method is to use Conda to setup a virtual environment, and installing both cudatoolkit and cupy.


   $ conda install -c conda-forge cudatoolkit
   $ conda install -c conda-forge cupy
   $ pip install lyceanem

Assuming the cudatoolkit and cupy are already installed, then LyceanEM can also be installed via pip.

 pip install LyceanEM

Development Roadmap

LyceanEM is electromagnetics simulation software that is used by researchers and engineers in a variety of fields. The software is currently under development, and the developers have outlined a roadmap for future changes. The roadmap includes three key areas:

  • Computational efficiency and scalability: The developers plan to improve the computational efficiency of LyceanEM so that it can be used on a wider range of hardware platforms, including desktop computers and high-performance computing (HPC) clusters. This will make LyceanEM more accessible to a wider range of users. It is the intention of the developers to support antenna arrays with multiple billon antenna elements, Giga-scale antenna arrays.
  • Core propagation engine: The developers plan to improve the core propagation engine of LyceanEM to include more realistic models of lossy propagation, atmospheric effects for each layer of the atmosphere, and dynamic environments. This will make LyceanEM more accurate and versatile for a wider range of applications.
  • Modelling fidelity: The developers plan to add new features to LyceanEM that will allow users to model electromagnetic systems with greater fidelity. This includes support for importing antenna patterns and time domain sources, as well as the development of open source standards for antenna array designs, antenna patterns and field sources, and wireless power transfer.

Here are some specific ways that users can contribute to the development of LyceanEM:

  • Report bugs: If you find a bug in LyceanEM, please report it to the developers so that they can fix it.
  • Submit patches: If you know how to fix a bug or add a new feature, please submit a pull request to the developers.
  • Donate: If you would like to support the development of LyceanEM, you can make a donation to the developers.

Your contributions will help to make LyceanEM the best possible electromagnetics simulation software for a wide range of users. Thank you for your support!

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  • Python 78.5%
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