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

Area of Science:

• Materials Science
• Mechanical Engineering
• Aerospace Engineering

Background:

• Solid rocket motors are critical propulsion systems.
• Grain structure integrity during solidification cooling is vital for reliability.
• Accurate reliability analysis is essential for safe and efficient operation.

Purpose of the Study:

• To develop an efficient and accurate method for analyzing the dynamic reliability of solid rocket motor grain structures during solidification cooling.
• To reduce the computational cost associated with traditional reliability analysis methods.
• To validate the proposed method's accuracy against established techniques.

Main Methods:

• Three-dimensional parametric modeling of the grain structure using ANSYS finite element software.
• Transient and dynamic thermo-structure coupling analysis to identify critical points and moments.
• Development of a dual neural network model incorporating copula functions for reliability calculation.
• Comparison with the Monte Carlo Simulation (MCS) method for validation.

Main Results:

• Identification of dangerous points and moments during the solidification cooling process.
• Extraction of maximum equivalent strain and temperature values.
• Calculation of instantaneous and dynamic reliability of the grain structure.
• Demonstration of reduced computational cost compared to traditional methods.

Conclusions:

• The proposed dual neural network method provides an accurate and computationally efficient approach for dynamic reliability analysis of solid rocket motor grains.
• The method is applicable to practical engineering problems.
• The results show high computational accuracy when compared to the MCS method.