An Adaptive Hybrid Correlation Kriging Approach for Uncertainty Dynamic Optimization of Spherical-Conical Shell...

Area of Science:

• Structural Engineering
• Computational Mechanics
• Materials Science

Background:

• Laminated spherical-conical shells are critical components in various engineering applications.
• Optimizing ply angles is essential for their vibration characteristics and performance.
• Uncertainty in parameters poses significant challenges for traditional optimization methods.

Purpose of the Study:

• To propose an uncertainty optimization method for laminated spherical-conical shells.
• To develop an adaptive hybrid correlation Kriging surrogate model for accurate prediction.
• To enhance the computational efficiency and applicability of optimization techniques.

Main Methods:

• Construction of equations of motion for vibration analysis.
• Development and validation of an adaptive hybrid correlation Kriging surrogate model.
• Application of an Improved Multi-objective Salp Swarm Algorithm for uncertainty optimization.

Main Results:

• The Kriging surrogate model demonstrated high accuracy in predicting vibration characteristics.
• The adaptive hybrid correlation Kriging model effectively analyzed weight distribution under uncertainty.
• The developed optimization algorithm showed significant efficacy in addressing uncertainty.

Conclusions:

• The proposed uncertainty optimization method is applicable and computationally efficient for laminated spherical-conical shells.
• The enhanced Kriging modeling strategy improves the analysis of complex shell structures.
• This research provides a robust framework for optimizing composite shell structures under uncertainty.