Algorithm for rapidly predicting the worst surface accuracy of deployable mesh reflectors

Wu, Xiaoshun, Cheng, Runhui, , Liu, Guihai, & Xia, Juwei (2021) Algorithm for rapidly predicting the worst surface accuracy of deployable mesh reflectors. Applied Mathematical Modelling, 98, pp. 229-244.

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Description

It is crucial to predict the worst surface accuracy of deployable mesh reflectors considering uncertainties at the design stage. The traditional method is computationally expensive because a form-finding process which needs many iterations is required in each Monte Carlo simulation. A quick method is adopted to rapidly compute the worst surface accuracy of deployable mesh reflectors. The sensitivity relationships between nodal coordinate deviations, cable force deviations and cable length errors are primarily derived. Instead of the time-consuming form-finding process, the derived sensitivity relationships are utilized to carry out the Monte Carlo simulations in the quick method. Ultimately, both the symmetric and asymmetric AstroMesh reflectors are numerically analyzed. The results show that the derived sensitivity relationships are effective, and the quick method can predict the worst surface accuracy as precise as the traditional method but with far less time consumption.

Impact and interest:

6 citations in Scopus
4 citations in Web of Science®
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ID Code: 226467
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
ORCID iD:
Chan, Tommy H.T.orcid.org/0000-0002-5410-8362
Additional Information: Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Number 51868026 ), the Natural Science Foundation of Jiangxi Province (CN) (Grant Number 20202BAB204028 ), as well as the Doctoral Scientific Research Foundation of Jiangxi University of Science and Technology. The authors also gratefully acknowledge the financial support from China Scholarship Council.
Measurements or Duration: 16 pages
Keywords: AstroMesh, Cable net, Deployable antenna, Manufacturing error, Surface accuracy
DOI: 10.1016/j.apm.2021.05.006
ISSN: 0307-904X
Pure ID: 102000538
Divisions: Current > Research Centres > Centre for Materials Science
Current > QUT Faculties and Divisions > Faculty of Science
Current > QUT Faculties and Divisions > Faculty of Engineering
Current > Schools > School of Civil & Environmental Engineering
Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Number 51868026 ), the Natural Science Foundation of Jiangxi Province (CN) (Grant Number 20202BAB204028 ), as well as the Doctoral Scientific Research Foundation of Jiangxi University of Science and Technology. The authors also gratefully acknowledge the financial support from China Scholarship Council.
Copyright Owner: 2021 Elsevier Inc.
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Deposited On: 26 Nov 2021 02:04
Last Modified: 12 Apr 2024 20:07