Evolution prediction of HVI debris based on CVAE model

ZHOU Hao; LI Yi; ZHANG Hao; CHEN Hong; REN Leisheng

doi:10.11729/syltlx20200058

Volume 35 Issue 5

Nov. 2021

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2021 > 35(5): 40-46

ZHOU H，LI Y，ZHANG H，et al. Evolution prediction of HVI debris based on CVAE model[J]. Journal of Experiments in Fluid Mechanics, 2021，35（5）：40-46. doi: 10.11729/syltlx20200058

Citation:

ZHOU H，LI Y，ZHANG H，et al. Evolution prediction of HVI debris based on CVAE model[J]. Journal of Experiments in Fluid Mechanics, 2021，35（5）：40-46. doi: 10.11729/syltlx20200058

Citation:

ZHOU H，LI Y，ZHANG H，et al. Evolution prediction of HVI debris based on CVAE model[J]. Journal of Experiments in Fluid Mechanics, 2021，35（5）：40-46. doi: 10.11729/syltlx20200058

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Evolution prediction of HVI debris based on CVAE model

doi: 10.11729/syltlx20200058

ZHOU Hao^1
,,
LI Yi^{1
,
,},
ZHANG Hao²,
CHEN Hong¹,
REN Leisheng¹

1.
Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang Sichuan　621000, China
2.
Institute of Computer Application, China Academy of Engineering Physics, Mianyang Sichuan　621000, China

Received Date: 2020-04-17
Rev Recd Date: 2020-09-13

Available Online: 2021-11-15

Publish Date: 2021-11-05

Abstract

Abstract

Efficiently and accurately predicting the evolution of hypervelocity impact debris is crucial in the design of spacecraft protective structures. To this end a deep learning model is constructed. The model is based on the conditional variation auto encoder (CVAE) and massive smoothed particle hydrodynamics (SPH) simulations. The model includes four controllable labels, namely projectile velocity (3.00-8.00 km/s) radius (2.00-8.00 mm), target plate thickness (1.000-4.000 mm), and time instant (1.0-12.0 μs). The model uses 200 parameters to describe the debris mass distribution, resulting in an average error less than 0.6%. It takes less than 7 micro seconds to predict one debris mass distribution.
- HVI,
- debris,
- deep learning,
- CVAE,
- protective structures

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References(20)

References

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