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@ARTICLE{Pargmann:1037639,
      author       = {Pargmann, Max and Ebert, Jan and Götz, Markus and
                      Maldonado Quinto, Daniel and Pitz-Paal, Robert and
                      Kesselheim, Stefan},
      title        = {{A}utomatic heliostat learning for in situ concentrating
                      solar power plant metrology with differentiable ray tracing},
      journal      = {Nature Communications},
      volume       = {15},
      number       = {1},
      issn         = {2041-1723},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2025-00804},
      pages        = {6997},
      year         = {2024},
      abstract     = {Concentrating solar power plants are a clean energy source
                      capable of competitive electricity generation even during
                      night time, as well as the production of carbon-neutral
                      fuels, offering a complementary role alongside photovoltaic
                      plants. In these power plants, thousands of mirrors
                      (heliostats) redirect sunlight onto a receiver, potentially
                      generating temperatures exceeding 1000°C. Practically, such
                      efficient temperatures are never attained. Several unknown,
                      yet operationally crucial parameters, e.g., misalignment in
                      sun-tracking and surface deformations can cause dangerous
                      temperature spikes, necessitating high safety margins. For
                      competitive levelized cost of energy and large-scale
                      deployment, in-situ error measurements are an essential, yet
                      unattained factor. To tackle this, we introduce a
                      differentiable ray tracing machine learning approach that
                      can derive the irradiance distribution of heliostats in a
                      data-driven manner from a small number of calibration images
                      already collected in most solar towers. By applying
                      gradient-based optimization and a learning non-uniform
                      rational B-spline heliostat model, our approach is able to
                      determine sub-millimeter imperfections in a real-world
                      setting and predict heliostat-specific irradiance profiles,
                      exceeding the precision of the state-of-the-art and
                      establishing full automatization. The new optimization
                      pipeline enables concurrent training of physical and
                      data-driven models, representing a pioneering effort in
                      unifying both paradigms for concentrating solar power plants
                      and can be a blueprint for other domains.},
      cin          = {JSC},
      ddc          = {500},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / Helmholtz AI - Helmholtz
                      Artificial Intelligence Coordination Unit – Local Unit FZJ
                      (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {39143091},
      UT           = {WOS:001291857100021},
      doi          = {10.1038/s41467-024-51019-z},
      url          = {https://juser.fz-juelich.de/record/1037639},
}