University of Dundee

The liquid crystal (LC) flow model is a coupling between

orientation (director field) of LC molecules and a flow field.

The model may probably be one of simplest complex fluids and

is very similar to a Allen-Cahn phase field model for

multiphase flows if the orientation variable is replaced by a

phase function. There are a few large or small parameters

involved in the model (e.g. the small penalty parameter for

the unit length LC molecule or the small phase-change

parameter, possibly large Reynolds number of the flow field,

etc.). We propose a C^0 finite element formulation in space

and a modified midpoint scheme in time which accurately

preserves the inherent energy law of the model. We use C^0

elements because they are simpler than existing C^1 element

and mixed element methods. We emphasise the energy law

preservation because from the PDE analysis point of view the

energy law is very important to correctly catch the evolution

of singularities in the LC molecule orientation. In addition

we will see numerical examples that the energy law preserving

scheme performs better under some choices of parameters. We

shall apply the same idea to a Cahn-Hilliard phase field model

where the biharmonic operator is decomposed into two Laplacian

operators. But we find that under our scheme non-physical

oscillation near the interface occurs. We figure out the

reason from the viewpoint of differential algebraic equations

and then remove the non-physical oscillation by doing only one

step of a modified backward Euler scheme at the initial time.

A number of numerical examples demonstrate the good

performance of the method. At the end of the talk we will show

how to apply the method to compute a superconductivity model,

especially at the regime of Hc2 or beyond. The talk is based

on a few joint papers with Chun Liu, Qi Wang, Xingbin Pan and

Roland Glowinski, etc.

Current methods for globalizing Newton's Method for solving systems of nonlinear equations fall back on steps biased towards the steepest descent direction (e.g. Levenberg/Marquardt, Trust regions, Cauchy point dog-legs etc.), when there is difficulty in making progress. This can occasionally lead to very slow convergence when short steps are repeatedly taken.

This talk looks at alternative strategies based on searching curved arcs related to Davidenko trajectories. Near to manifolds on which the Jacobian matrix is singular, certain conjugate steps are also interleaved, based on identifying a Pareto optimal solution.

Preliminary limited numerical experiments indicate that this approach is very effective, with rapid and ultimately second order convergence in almost all cases. It is hoped to present more detailed numerical evidence when the talk is given. The new ideas can also be incorporated with more recent ideas such as multifilters or nonmonotonic line searches without difficulty, although it may be that there is no longer much to gain by doing this.

When modelling a physical or biological system, it has to be decided

what framework best captures the underlying properties of the system

under investigation. Usually, either a continuous or a discrete

approach is adopted and the evolution of the system variables can then

be described by ordinary or partial differential equations or

difference equations, as appropriate. It is sometimes the case,

however, that the model variables evolve in space or time in a way

which involves both discrete and continuous elements. This is best

illustrated by a simple example. Suppose that the life span of a

species of insect is one time unit and at the end of its life span,

the insect mates, lays eggs and then dies. Suppose the eggs lie

dormant for a further 1 time unit before hatching. The `time-scale' on

which the insect population evolves is therefore best represented by a

set of continuous intervals separated by discrete gaps. This concept

of `time-scale' (or measure chain as it is referred to in a slightly

wider context) can be extended to sets consisting of almost arbitrary

combinations of intervals, discrete points and accumulation points,

and `time-scale analysis' defines a calculus, on such sets. The

standard `continuous' and `discrete' calculus then simply form special

cases of this more general time scale calculus.

In this talk, we will outline some of the basic properties of time

scales and time scale calculus before discussing some if the

technical problems that arise in deriving and analysing boundary

value problems on time scales.

In St John's College.

Oxford Life Sciences Modelling Colloquia Series