AI-enhanced differentiable Ray Tracer for Irradiation Prediction in Solar Tower Digital Twins

What ARTIST can do for you

The ARTIST package provides an implementation of a differentiable ray tracer using the PyTorch machine-learning framework in Python. Leveraging automatic differentiation and GPU computation, it facilitates the optimization of heliostats, towers, and camera parameters within a solar field by combining gradient-based optimization methods with smooth parametric descriptions of heliostats.

Our contributions include:

• Efficient heliostat calibration: We develop a parallelized geometric kinematic model that enables efficient calibration via either ray tracing-based or motor position data. This offers a flexible and robust approach to heliostat calibration.

• Surface reconstruction and flux density prediction: Leveraging learning Non-Uniform Rational B-Splines (NURBS), ARTIST reconstructs heliostat surfaces accurately using calibration images commonly available in solar thermal power plants. Thus, we can achieve sub-millimeter accuracy in mirror reconstruction from focal spot images, contributing to improved operational safety and efficiency. The reconstructed surfaces can be used for predicting unique heliostat flux densities with state-of-the-art accuracy. Check out this paper for more details.

• Immediate deployment: ARTIST enables deployment at the beginning of a solar thermal plant's operation, allowing for in situ calibration and subsequent improvements in energy efficiencies and cost reductions.

• Optimized flux density: Coming soon so stay tuned :rocket:!

Installation

We heavily recommend installing the ARTIST package in a dedicated Python3.10+ virtual environment. You can install ARTIST directly from the GitHub repository via: bash pip install git+https://github.com/ARTIST-Association/ARTIST Alternatively, you can install ARTIST locally. To achieve this, there are two steps you need to follow: 1. Clone the ARTIST repository: bash git clone https://github.com/ARTIST-Association/ARTIST.git 2. Install the package from the main branch. There are multiple installation options available: - Install basic dependencies: pip install . - Install with optional dependencies to run the tutorials: pip install ."[tutorials]" - Install an editable version with developer dependencies: pip install -e ."[dev]"

Structure

The ARTIST repository is structured as shown below: . ├── artist # Parent package │ ├── core # Core functionality of ARTIST, e.g. raytracing, optimizers etc. │ ├── data_loader # Deals with loading data into ARTIST from different sources. │ ├── field # Objects in the field, e.g. heliostats and target areas like receivers and calibration targets. │ ├── scenario # Functionality to create and load scenarios in ARTIST. │ ├── scene # Light sources and factors influencing the surroundings. │ └── util ├── tests │ ├── data │ │ ├── field_data # Real measurements from the PAINT database and STRAL that can be used in ARTIST. │ │ ├── scenarios # Scenarios describing an environment that can be loaded by ARTIST. │ │ └── ... │ ├── core │ ├── data_loader │ └── ... └── tutorials # Tutorials to help you get started with ARTIST. ├── data # Data accessed in the tutorials. │ ├── paint # Real measurements from the PAINT database. │ ├── scenarios # Scenarios describing an environment that can be loaded by ARTIST. │ └── stral Real # Measurements from STRAL. └── ...

Documentation

You can check out the full ARTIST documentation at https://artist.readthedocs.io/en/latest/index.html :rocket:! The ARTIST documentation includes: - Installation instructions - Tutorials - Some theoretical background information - API reference

How to contribute

Check out our contribution guidelines if you are interested in contributing to the ARTIST project :fire:. Please also carefully check our code of conduct :blue_heart:.

Acknowledgments

This work is supported by the Helmholtz AI platform grant.