Advancements in reflected target nonintrusive assessment (ReTNA) for large optical surface measurement
Reflected computer vision targets are a powerful tool for measurement of mirror surface shape, with several important advantages over traditional fringe deflectometry methods. This method was first presented in 2021 and has undergone significant improvement and demonstration since. We describe a new baseline system using reflected computer vision targets, and present results from a large-scale measurement campaign conducted on both commercial heliostats and test mirrors in the laboratory. Calibration of the measurement system with photogrammetry allows for accurate measurement without careful control of target shape or camera position. Overall, the results show that a baseline setup using this method achieves measurement uncertainties in the slope error root-mean-square less than ±0.11 milliradian due to a series of repeatability conditions, varying sample position, rotation, lighting, camera settings, and system rebuild and recalibration. We present a detailed description of the setup, the results generated by this measurement tool, repeated measurement results, and the strengths and limitations of this metrology system.
- Research Organization:
- National Laboratory of the Rockies (NLR), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Grant/Contract Number:
- AC36-08GO28308
- OSTI ID:
- 3023067
- Report Number(s):
- NLR/JA--5700-94562
- Journal Information:
- Solar Energy, Journal Name: Solar Energy Vol. 309; ISSN 0038-092X
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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