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Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow
Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow
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Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow
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Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow
Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow

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Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow
Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow
Paper

Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow

2024
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Overview
Reductions of the self-consistent mean field theory model of amphiphilic molecules in solvent can lead to a singular family of functionalized Cahn-Hilliard energies. We modify these energies, mollifying the singularities to stabilize the computation of the gradient flows and develop a series of benchmark problems that emulate the \"morphological complexity\" observed in experiments. These benchmarks investigate the delicate balance between the rate of absorption of amphiphilic material onto an interface and a least energy mechanism to disperse the arriving mass. The result is a trichotomy of responses in which two-dimensional interfaces either lengthen by a regularized motion against curvature, undergo pearling bifurcations, or split directly into networks of interfaces. We evaluate a number of schemes that use second order BDF2-type time stepping coupled with Fourier pseudo-spectral spatial discretization. The BDF2-type schemes are either based on a fully implicit time discretization with a PSD nonlinear solver, or upon IMEX, SAV, ETD approaches. All schemes use a fixed local truncation error target with adaptive time-stepping to achieve the error target. Each scheme requires proper \"preconditioning\" to achieve robust performance that can enhance efficiency by several orders of magnitude.

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