$Q$-tensor gradient flow with quasi-entropy and discretizations preserving physical constraints

TL;DR

This paper develops and analyzes numerical schemes for the gradient flow of $Q$-tensor models using quasi-entropy, ensuring physical eigenvalue constraints and energy dissipation, with proven stability and error estimates.

Contribution

It introduces new numerical schemes based on quasi-entropy for $Q$-tensor gradient flows that preserve physical constraints and energy dissipation unconditionally.

Findings

First-order scheme is uniquely solvable and preserves constraints.

Second-order scheme maintains stability and energy dissipation unconditionally.

Numerical examples validate theoretical error estimates and defect pattern simulations.

Abstract

We propose and analyze numerical schemes for the gradient flow of $Q$-tensor with the quasi-entropy. The quasi-entropy is a strictly convex, rotationally invariant elementary function, giving a singular potential constraining the eigenvalues of $Q$ within the physical range $(-1/3,2/3)$. Compared with the potential derived from the Bingham distribution, the quasi-entropy has the same asymptotic behavior and underlying physics. Meanwhile, it is very easy to evaluate because of its simple expression. For the elastic energy, we include all the rotationally invariant terms. The numerical schemes for the gradient flow are built on the nice properties of the quasi-entropy. The first-order time discretization is uniquely solvable, keeping the physical constraints and energy dissipation, which are all independent of the time step. The second-order time discretization keeps the first two properties unconditionally, and the third with an $O(1)$ restriction on the time step. These results also hold when we further incorporate a second-order discretization in space. Error estimates are also established for time discretization and full discretization. Numerical examples about defect patterns are presented to validate the theoretical results.