Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Concentrating Solar Power (CSP) plants are a key technology in the transition
toward sustainable energy. A critical factor for their safe and efficient
operation is the distribution of concentrated solar flux on the receiver.
However, flux distributions from individual heliostats are sensitive to surface
imperfections. Measuring these surfaces across many heliostats remains
impractical in real-world deployments. As a result, control systems often
assume idealized heliostat surfaces, leading to suboptimal performance and
potential safety risks. To address this, inverse Deep Learning Raytracing
(iDLR) has been introduced as a novel method for inferring heliostat surface
profiles from target images recorded during standard calibration procedures. In
this work, we present the first successful Sim-to-Real transfer of iDLR,
enabling accurate surface predictions directly from real-world target images.
We evaluate our method on 63 heliostats under real operational conditions. iDLR
surface predictions achieve a median mean absolute error (MAE) of 0.17 mm and
show good agreement with deflectometry ground truth in 84% of cases. When used
in raytracing simulations, it enables flux density predictions with a mean
accuracy of 90% compared to deflectometry over our dataset, and outperforms the
commonly used ideal heliostat surface assumption by 26%. We tested this
approach in a challenging double-extrapolation scenario-involving unseen sun
positions and receiver projection-and found that iDLR maintains high predictive
accuracy, highlighting its generalization capabilities. Our results demonstrate
that iDLR is a scalable, automated, and cost-effective solution for integrating
realistic heliostat surface models into digital twins. This opens the door to
improved flux control, more precise performance modeling, and ultimately,
enhanced efficiency and safety in future CSP plants.
