Shape Parameterization and Efficient Optimization Design Method for the Ray-like Underwater Gliders.

Area of Science:

• Fluid Dynamics and Hydrodynamics
• Computational Science and Engineering
• Bio-inspired Robotics

Background:

• High-precision optimization of underwater gliders faces challenges with computational cost and design cycle length.
• Bio-inspired designs, like ray-like gliders, offer potential for enhanced maneuverability and efficiency.
• Accurate hydrodynamic modeling is crucial for optimizing underwater vehicle performance.

Purpose of the Study:

• To develop a high-accuracy, low-cost parametric modeling and optimization method for ray-like underwater gliders.
• To enhance the lift-to-drag ratio and improve flow distribution in bio-inspired underwater vehicles.
• To reduce the computational expense and design time associated with high-precision optimization.

Main Methods:

• Parametric geometry reconstruction using Class-Shape Transformation (CST) based on manta ray contours.
• High-fidelity Computational Fluid Dynamics (CFD) simulations for hydrodynamic performance evaluation.
• Kriging-based sequential optimization framework with adaptive surrogate model refinement using Mean Squared Prediction (MSP) and Expected Improvement (EI).

Main Results:

• Achieved a 116% improvement in the lift-to-drag ratio.
• Demonstrated a more uniform flow distribution around the glider.
• Validated the effectiveness of the parametric modeling and optimization approach through comparative case studies.

Conclusions:

• The proposed method offers a practical and efficient tool for parametric design and hydrodynamic optimization of bio-inspired underwater vehicles.
• The approach successfully balances high accuracy with reduced computational cost and design cycle time.
• The findings contribute to advancing the design and performance of autonomous underwater vehicles.