Space launch vehicle transient particle redistribution modeling and implications for optically sensitive payloads

John R. Anderson; William Hoey; John M. Alred; Carlos Soares

doi:10.1117/12.2567765

24 August 2020 Space launch vehicle transient particle redistribution modeling and implications for optically sensitive payloads

John R. Anderson, William Hoey, John M. Alred, Carlos Soares

Author Affiliations +

Proceedings Volume 11489, Systems Contamination: Prediction, Control, and Performance 2020; 114890D (2020) https://doi.org/10.1117/12.2567765
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

The performance of contamination sensitive components—such as optical components—can be degraded by particulate matter depositing on the surfaces. Particles can accumulate during manufacturing, handling and operation. For a space-based system, particles can shed from the fairing and redistribute onto sensitive surfaces during launch. An engineering modeling approach has been developed for modeling particle migration during launch. The approach involves particle detachment from the fairing, particle transport through the venting atmosphere inside the fairing, and attachment to the receiving surface. Particle size and amounts on the fairing surface can be modeled using distributions from standards, such as IEST-STDCC1246E, as well as from empirical data obtained from tape lifts. Surface interactions are modeled using theoretical as well as empirical data. Commercial computational fluid dynamics codes are used to calculate the gas flow in the fairing during depressurization during launch. This approach not only provides insight into particle redistribution during launch but also can be used to establish fairing cleanliness requirements.

Conference Presentation

Citation Download Citation

John R. Anderson, William Hoey, John M. Alred, and Carlos Soares "Space launch vehicle transient particle redistribution modeling and implications for optically sensitive payloads", Proc. SPIE 11489, Systems Contamination: Prediction, Control, and Performance 2020, 114890D (24 August 2020); https://doi.org/10.1117/12.2567765

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