[ont.events] Adaptive Methods for Time-Dependent Partial Differential Equations.

ylfink@water.waterloo.edu (ylfink) (11/01/88)

DEPARTMENT OF COMPUTER SCIENCE
UNIVERSITY OF WATERLOO
SEMINAR ACTIVITIES

SCIENTIFIC COMPUTATION SEMINAR

                    - Thursday, November 3, 1988

Dr.  Joseph  E.  Flaherty, Rensselaer Polytechnic Institute,
Troy,  New  York, will speak on ``Adaptive Methods for Time-
Dependent Partial Differential Equations''.

TIME:                4:00 PM

ROOM:              DC 1304

ABSTRACT

We  discuss adaptive local refinement procedures for solving
vector   systems   of  time-dependent  partial  differential
equations  in  one  and two space dimensions.  Each adaptive
algorithm  contains  a basic finite element or finite volume
procedure   to   compute   approximate   solutions   of  the
differential  system  on  a  given mesh, an error indication
procedure  to  decide  which  regions  of the problem domain
require   greater   resolution,  and  an  adaptive  feedback
strategy  to  generate  a  finer  mesh  when accuracy is not
sufficient.   Finite  element  and  finite volume procedures
utilizing  piecewise  polynomial approximations in space and
either  explicit  or  implicit  finite difference methods in
time will be discussed.

Temporal  mesh  refinement strategies can either be local or
global.   Global  temporal  refinement  strategies  lead  to
adaptive  methods  of lines.  Such schemes can fully utilize
the  power  of  existing  software for solving initial value
problems   for   ordinary   differential  equations.   Local
temporal refinement strategies, on the other hand, offer the
promise  of greater efficiency by using different time steps
on different portions of the problem domain.

Spatial   refinement   strategies  are  grouped  into  three
categories:  h-refinement  where  a mesh is refined, but the
order  of  the  finite  volume  or  finite element method is
unchanged;  p-refinement  where  the  same  mesh is used for
methods  of  increasing  order of accuracy; and r-refinement
where  the  structure  of  a  mesh is changed without either
increasing  the  number of cells or the order of the method.
Mesh  refinement can further be classified as being cellular
or noncellular.  With cellular refinement, the computational
cells  of  finer  meshes  are  properly  nested  within  the
boundaries of cells of coarser meshes; thus, simplifying the
transfer   of  solutions  between  meshes  having  different
spacings.   With  noncellular refinement, the cells of finer
meshes  can  overlap those of coarser ones.  Mesh structures
can  be kept uniform and this may offer improved performance
on  computers  having  vector processors.  In either case, a
tree  structure,  with  finer grids regarded as offspring of
coarser ones, is used to manage the data associated with the

refinement   process.    Several   specific   cellular   and
noncellular  refinement  techniques  will be presented.  The
use of these procedures in combination with r-refinement and
p-refinement  techniques and with mesh generation codes will
also be discussed.

Error   indicators   based   on   estimates   of  the  local
discretization  error  are  used  to  control  the  adaptive
refinement  process.   Several  error  estimates  for finite
element  methods  that  utilize  a  hierarchic  p-refinement
approach   will  be  presented.   There  is  an  interesting
dichotomy  between  the errors in odd- and even-order finite
elements.   In  particular norms, errors in odd-order finite
element  solutions  are  concentrated  near  element  edges,
whereas  the errors in even-order methods are principally in
element  interiors.   These  results are used to improve the
computational efficiency of the error estimates.