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001025767 0247_ $$2doi$$a10.52825/solarpaces.v1i.642
001025767 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-03135
001025767 0247_ $$2WOS$$aWOS:001324829700026
001025767 037__ $$aFZJ-2024-03135
001025767 1001_ $$00000-0002-4705-6285$$aPargmann, Max$$b0
001025767 1112_ $$a28th International Conference on Concentrating Solar Power and Chemical Energy Systemsf CSP and Hybridized Systems$$cAlbuquerque$$d2022-09-27 - 2022-09-30$$gSolarPACES 2022$$wUSA
001025767 245__ $$aIn Situ Enhancement of Heliostat Calibration Using Differentiable Ray Tracing and Artificial Intelligence
001025767 260__ $$c2023
001025767 300__ $$a10 p.
001025767 3367_ $$2ORCID$$aCONFERENCE_PAPER
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001025767 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1714546206_354
001025767 520__ $$aThe camera target method is the most commonly used calibration method for heliostats at solar tower power plants to minimize their sun tracking errors. In this method, individual heliostats are moved to a white surface and their deviation from the targeted position is measured. A regression is used to calculate errors in a geometry model from the tabular data obtained in this way. For modern aim point strategies, or simply heliostats in the rearmost end of the field, extremely high accuracies are needed, which can only be achieved by many degrees of freedom in the geometry model. The problem here is that the camera target method produces only a very small data set per heliostat, which limits the number of free variables and thus the accuracy. In this work, we extend existing ray tracing methods for solar towers with a differentiable description, allowing for the first time a data-driven optimization of object parameters within the ray tracing environment. Therefore, the heliostat calibration can take place directly within the ray tracing environment. Thus, the image data acquired during the measurement can be processed directly and more information about the orientation of the heliostat can be obtained. Within a simple example we show the advantages of the method, which converges faster and corrects errors that could not be considered before. Without any disadvantages or additional costs, the state-of-the-art calibration method can be improved.
001025767 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001025767 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001025767 7001_ $$0P:(DE-Juel1)187002$$aEbert, Jan$$b1
001025767 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b2
001025767 7001_ $$0P:(DE-HGF)0$$aMaldonado Quinto, Daniel$$b3
001025767 7001_ $$00000-0002-3542-3391$$aPitz-Paal, Robert$$b4
001025767 773__ $$a10.52825/solarpaces.v1i.642$$gVol. 1$$v1$$y2023
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001025767 9141_ $$y2024
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