Comparison of predictive techniques for spacecraft shock environment

Comparison of predictive techniques for spacecraft shock environment

Ada Ranieri

Abstract. Shock loads are very high amplitude and short duration transient loads. They are produced in space structures by pyrotechnic devices placed in the launchers initiating the stage or fairing separations. While the spacecraft structure is not susceptible to this range of frequencies, electrical units could be seriously damaged during the launch phase. Shock tests are performed on the electrical units to verify if they withstand the transient loads, thus their compliance to the requirements. To understand how the input force evolves from the launcher-spacecraft interface to the equipment of interest, a model of the dynamical behaviour of the spacecraft at high frequencies has to be developed. An initial approach constitutes the implementation of a mathematical model through the use of Statistical Energy Analysis (SEA). The results of a 4 degrees of freedom model using SEA will be shown. The model can be further developed by the integration of data-driven techniques. In this work a description of two different approaches is presented, that include model-based and data-driven methods. Finally, a cross-cutting potential solution is briefly introduced; it will combine experimental data with a mathematical model as to convey them in the training database of an Artificial Neural Network algorithm. The hybrid solution will possibly turn out as a reliable and efficient way to break down time and costs of the shock test campaign.

Keywords
Shock Prediction, Model-Based, Data-Driven, Statistical Energy Analysis, Artificial Neural Network

Published online 9/1/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Ada Ranieri, Comparison of predictive techniques for spacecraft shock environment, Materials Research Proceedings, Vol. 33, pp 163-170, 2023

DOI: https://doi.org/10.21741/9781644902677-24

The article was published as article 24 of the book Aerospace Science and Engineering

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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[1] M. Gherlone, D. Lomario, M. Mattone, and R. Ruotolo, “Application of wave propagation to pyroshock analysis,” IOS Press, 2004.
[2] H. Zhao et al., “The shock environment prediction of satellite in the process of satellite-rocket separation,” Acta Astronaut, vol. 159, pp. 112–122, Jun. 2019. https://doi.org/10.1016/j.actaastro.2019.03.017
[3] E. Sarradj, “Energy-based vibroacoustics: SEA and beyond.”
[4] X. Wang, W. Liu, X. Li, and Y. Sun, “The shock response prediction of spacecraft structure based on hybrid fe-sea method,” Applied Sciences (Switzerland), vol. 11, no. 18, Sep. 2021. https://doi.org/10.3390/app11188490
[5] E. C. Dalton and B. S. Chambers, “Analysis and validation testing of impulsive load response in complex, multi-compartmented structures,” in Collection of Technical Papers – AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1995, vol. 2, pp. 759–767. https://doi.org/10.2514/6.1995-1243
[6] D.-O. Lee, J.-H. Han, H.-W. Jang, S.-H. Woo, and K.-W. Kim, “Shock Response Prediction of a Low Altitude Earth Observation Satellite During Launch Vehicle Separation,” International Journal of Aeronautical and Space Sciences, vol. 11, no. 1, pp. 49–57, Mar. 2010. https://doi.org/10.5139/ijass.2010.11.1.049
[7] “Space engineering Mechanical shock design and verification handbook ECSS Secretariat ESA-ESTEC Requirements & Standards Division Noordwijk, The Netherlands,” 2015.
[8] R. S. Kenett, A. Zonnenshain, and G. Fortuna, “A road map for applied data sciences supporting sustainability in advanced manufacturing: The information quality dimensions,” Procedia Manuf, vol. 21, pp. 141–148, 2018. https://doi.org/10.1016/J.PROMFG.2018.02.104
[9] J. Ma, S. Dong, G. Chen, P. Peng, and L. Qian, “A data-driven normal contact force model based on artificial neural network for complex contacting surfaces,” Mech Syst Signal Process, vol. 156, Jul. 2021, doi: 10.1016/J.YMSSP.2021.107612.
[10] I. Flood and N. Kartam, “Neural Networks in Civil Engineering. II: Systems and Application,” Journal of Computing in Civil Engineering, vol. 8, no. 2, pp. 149–162, Apr. 1994 https://doi.org/10.1061/(asce)0887-3801(1994)8:2(149)
[11] I. E. Lagaris, A. Likas, and D. I. Fotiadis, “Artificial neural networks for solving ordinary and partial differential equations,” IEEE Trans Neural Netw, vol. 9, no. 5, pp. 987–1000, 1998. https://doi.org/10.1109/72.712178
[12] R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud, “Neural Ordinary Differential Equations,” 2018