Genetic design of solids possessing a random-particulate microstructure.

Area of Science:

• Materials Science and Engineering
• Computational Materials Design
• Solid Mechanics

Background:

• Designing macroscopic solid material properties by doping homogeneous matrices with distinct particles presents significant computational challenges.
• Key difficulties include a high number of microdesign variables leading to non-convex objective functions, non-differentiability issues due to constraints, and noise amplification from sample variations.

Purpose of the Study:

• To develop a novel statistical genetic algorithm capable of addressing the complexities in computational material design.
• The algorithm aims to overcome challenges related to non-convexity, lack of regularity, and size effects in material property optimization.

Main Methods:

• Development and theoretical investigation of a statistical genetic algorithm tailored for microdesign optimization.
• Application of finite-element type discretizations for large-scale numerical simulations.
• Utilizing semi-analytical approaches to complement numerical examples.

Main Results:

• The proposed statistical genetic algorithm demonstrates the capability to handle non-convex objective functions and non-differentiable design spaces.
• The method effectively mitigates noise arising from random particle distributions and finite-size effects in material property prediction.
• Validation through semi-analytical and large-scale numerical examples showcases practical applicability.

Conclusions:

• The developed statistical genetic algorithm offers a robust solution for the computational design of heterogeneous solid materials.
• This approach enhances the efficiency and reliability of optimizing material properties with complex microstructures.
• The findings provide a valuable tool for advancing the field of computational materials science.