Nonlinear Methods for Shape Optimization Problems in Liquid Crystal Tactoids

TL;DR

This paper develops a finite element-based nonlinear shape optimization method for predicting equilibrium shapes of liquid crystal tactoids, combining shape and order parameters within the Landau--de Gennes framework.

Contribution

It introduces an efficient nested iteration algorithm for constrained energy minimization in shape and order optimization of liquid crystal tactoids.

Findings

Nested iteration improves computational efficiency and convergence.

The method accurately predicts tactoid shapes consistent with experimental data.

Shape and order parameters can be optimized separately to understand their roles.

Abstract

Anisotropic fluids, such as nematic liquid crystals, can form non-spherical equilibrium shapes known as tactoids. Predicting the shape of these structures as a function of material parameters is challenging and paradigmatic of a broader class of problems that combine shape and order. Here, we consider a discrete shape optimization approach with finite elements to find the configuration of two-dimensional and three-dimensional tactoids using the Landau--de Gennes framework and a Q-tensor representation. Efficient solution of the resulting constrained energy minimization problem is achieved using a quasi-Newton and nested iteration algorithm. Numerical validation is performed with benchmark solutions and compared against experimental data and earlier work. We explore physically motivated subproblems, whereby the shape and order are separately held fixed, respectively, to explore the role of both and examine material parameter dependence of the convergence. Nested iteration significantly improves both the computational cost and convergence of numerical solutions of these highly deformable materials.