Reliability analysis of the solidification cooling of solid rocket motor grain material

The reliability of solid rocket motor grain structure during solidification cooling is analyzed. First, a three-dimensional parametric modeling of the grain is carried out by ANSYS finite element software. The dangerous point and dangerous moment can be obtained based on the transient and dynamic thermo-structure coupling under the cooling condition. Moreover, the maximum equivalent strain and temperature values are extracted. Second, a dual neural network model is established based on the probability distribution of the copula function and specific parameters. Finally, the instantaneous reliability during the solidification cooling process of the grain is calculated. Then, the dynamic reliability analysis is realized. The proposed method reduces the computational cost of dynamic reliability of grain structure, demonstrating its applicability in practical engineering problems. Furthermore, comparing the results of the proposed method with the MCS method demonstrates that the proposed method has high computational accuracy.

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Publication Details

Journal
PLoS ONE
Published
2024-08-20
DOI
https://doi.org/10.1371/journal.pone.0306208
Citations
2
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
0.92

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article

Reliability analysis of the solidification cooling of solid rocket motor grain material

Yangtian Li, Yun He, Juan Du
2 citations
PLoS ONE
Probabilistic and Robust Engineering Design
0.92
article

Reliability analysis of the solidification cooling of solid rocket motor grain material

Yangtian Li, Yun He, Juan Du
article en
2 citations

Abstract

The reliability of solid rocket motor grain structure during solidification cooling is analyzed. First, a three-dimensional parametric modeling of the grain is carried out by ANSYS finite element software. The dangerous point and dangerous moment can be obtained based on the transient and dynamic thermo-structure coupling under the cooling condition. Moreover, the maximum equivalent strain and temperature values are extracted. Second, a dual neural network model is established based on the probability distribution of the copula function and specific parameters. Finally, the instantaneous reliability during the solidification cooling process of the grain is calculated. Then, the dynamic reliability analysis is realized. The proposed method reduces the computational cost of dynamic reliability of grain structure, demonstrating its applicability in practical engineering problems. Furthermore, comparing the results of the proposed method with the MCS method demonstrates that the proposed method has high computational accuracy.

PLoS ONEVol. 19(8)
Inner Mongolia Agricultural University (CN), Inner Mongolia University of Finance and Economics (CN)
Natural Science Foundation of Inner Mongolia
Affordable and clean energy
Openalex Percentile: Top 21%
Probabilistic and Robust Engineering Design
0.92
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Reliability analysis of the solidification cooling of solid rocket motor grain material — Yangtian Li, Yun He, et al. · PLoS ONE (2024) | TGRS Research Map | TGRS