Publications

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Concentrating Solar Power (CSP) requires precision mirrors, and these in turn require metrology systems to measure their optical slope. In this project we studied a color-based approach to the correspondence problem, which is the association of points on an optical target with their corresponding points seen in a reflection. This is a core problem in deflectometry-based metrology, and a color solution would enable important new capabilities. We modeled color as a vector in the [R,G,B] space measured by a digital camera, and explored a dual-image approach to compensate for inevitable changes in illumination color. Through a series of experiments including color target design and dual-image setups both indoors and outdoors, we collected reference/measurement image pairs for a variety of configurations and light conditions. We then analyzed the resulting image pairs by selecting example [R,G,B] pixels in the reference image, and seeking matching [R,G,B] pixels in the measurement image. Modulating a tolerance threshold enabled us to assess both match reliability and match ambiguity, and for some configurations, orthorectification enabled us to assess match accuracy. Using direct-direct imaging, we demonstrated color correspondence achieving average match accuracy values of 0.004 h, where h is the height of the color pattern. We found that wide-area two-dimensional and linear one-dimensional color targets outperformed hybrid linear/lateral gradient targets in the cases studied. Introducing a mirror degraded performance under our current techniques, and we did not have time to evaluate whether matches could be reliably achieved despite varying light conditions. Nonetheless, our results thus far are promising.

In the framework of SFERA-III WP10 Task3, ENEA has organized the 3D-shape round-robin (RR); the purpose is to compare the main geometrical parameters of 3D shape measurement of parabolic-trough (PT) reflective panels evaluated with the instruments adopted by each participant among: ENEA, DLR, F-ISE, NREL, and SANDIA. The last two institutions are outside of the EU, but benefited from the Transnational Access institute to visit several European laboratories, including the ENEA Casaccia research center where they accomplished some measurements with a portable experimental set-up. RR is based on the inter-laboratory circulation of 3 inner plus 3 outer PT panels. The start of the RR was delayed by the covid pandemic, then the circulation of the specimen-set and their measurement took more than one year. At the time of drafting this deliverable at the end of SFERA-III project, NREL has not yet completed the analysis of the measurements, making available only the deviations of the slopes. Therefore here will be reported only the preliminary results. The full comparison will be published as soon as possible, maybe in the open access venue Open Research Europe.

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The Sandia Optical Fringe Analysis Slope Tool (SOFAST) is a tool that has been developed at Sandia to measure the surface slope of concentrating solar power optics. This tool has largely remained of research quality over the past few years. Since SOFAST is important to ongoing tests happening at Sandia as well as an interest to others outside Sandia, there is a desire to bring SOFAST up to professional software standards. The goal of this effort was to make progress in several broad areas including: code quality, sample data collection, and validation and testing. During the course of this effort, much progress was made in these areas. SOFAST is now a much more professional grade tool. There are, however, some areas of improvement that could not be addressed in the timeframe of this work and will be addressed in the continuation of this effort.

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Spectral matched filtering and its variants (e.g. Adaptive Coherence Estimator or ACE) rely on strong assumptions about target and background distributions. For instance, ACE assumes a Gaussian distribution of background and additive target model. In practice, natural spectral variation, due to effects such as material Bidirectional Reflectance Distribution Function, non-linear mixing with surrounding materials, or material impurities, degrade the performance of matched filter techniques and require an ever-increasing library of target templates measured under different conditions. In this work, we employ the contrastive loss function and paired neural networks to create data-driven target detectors that do not rely on strong assumptions about target and background distribution. Furthermore, by matching spectra to templates in a highly nonlinear fashion via neural networks, our target detectors exhibit improved performance and greater resiliency to natural spectral variation; this performance improvement comes with no increase in target template library size. We evaluate and compare our paired neural network detector to matched filter-based target detectors on a synthetic hyperspectral scene and the well-known Indian Pines AVIRIS hyperspectral image.

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