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Augmented method to improve thermal data for the figure drift thermal distortion predictions of the JWST OTIS cryogenic vacuum test

Authors :
Cherie B. Congedo
Lester M. Cohen
Wes Ousley
Sang C. Park
Michael J. Eisenhower
Andrew Weaver
Kan Yang
Timothy M. Carnahan
Source :
Optical Modeling and Performance Predictions IX.
Publication Year :
2017
Publisher :
SPIE, 2017.

Abstract

The JWST Optical Telescope Element (OTE) assembly is the largest optically stable infrared-optimized telescope currently being manufactured and assembled, and is scheduled for launch in 2018. The JWST OTE, including the 18 segment primary mirror, secondary mirror, and the Aft Optics Subsystem (AOS) are designed to be passively cooled and operate near 45K. These optical elements are supported by a complex composite backplane structure. As a part of the structural distortion model validation efforts, a series of tests are planned during the cryogenic vacuum test of the fully integrated flight hardware at NASA JSC Chamber A. The successful ends to the thermal-distortion phases are heavily dependent on the accurate temperature knowledge of the OTE structural members. However, the current temperature sensor allocations during the cryo-vac test may not have sufficient fidelity to provide accurate knowledge of the temperature distributions within the composite structure. A method based on an inverse distance relationship among the sensors and thermal model nodes was developed to improve the thermal data provided for the nanometer scale WaveFront Error (WFE) predictions. The Linear Distance Weighted Interpolation (LDWI) method was developed to augment the thermal model predictions based on the sparse sensor information. This paper will encompass the development of the LDWI method using the test data from the earlier pathfinder cryo-vac tests, and the results of the notional and as tested WFE predictions from the structural finite element model cases to characterize the accuracies of this LDWI method.

Details

Database :
OpenAIRE
Journal :
Optical Modeling and Performance Predictions IX
Accession number :
edsair.doi...........0d6f32f39b917013be469213fac2e74e
Full Text :
https://doi.org/10.1117/12.2277116