27,733 research outputs found
An adaptive grid refinement strategy for the simulation of negative streamers
The evolution of negative streamers during electric breakdown of a
non-attaching gas can be described by a two-fluid model for electrons and
positive ions. It consists of continuity equations for the charged particles
including drift, diffusion and reaction in the local electric field, coupled to
the Poisson equation for the electric potential. The model generates field
enhancement and steep propagating ionization fronts at the tip of growing
ionized filaments. An adaptive grid refinement method for the simulation of
these structures is presented. It uses finite volume spatial discretizations
and explicit time stepping, which allows the decoupling of the grids for the
continuity equations from those for the Poisson equation. Standard refinement
methods in which the refinement criterion is based on local error monitors fail
due to the pulled character of the streamer front that propagates into a
linearly unstable state. We present a refinement method which deals with all
these features. Tests on one-dimensional streamer fronts as well as on
three-dimensional streamers with cylindrical symmetry (hence effectively 2D for
numerical purposes) are carried out successfully. Results on fine grids are
presented, they show that such an adaptive grid method is needed to capture the
streamer characteristics well. This refinement strategy enables us to
adequately compute negative streamers in pure gases in the parameter regime
where a physical instability appears: branching streamers.Comment: 46 pages, 19 figures, to appear in J. Comp. Phy
Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight
into pathological and physiological alterations of living tissue, with the help
of which researchers hope to predict (local) therapeutic efficacy early and
determine optimal treatment schedule. However, the analysis of qMRI has been
limited to ad-hoc heuristic methods. Our research provides a powerful
statistical framework for image analysis and sheds light on future localized
adaptive treatment regimes tailored to the individual's response. We assume in
an imperfect world we only observe a blurred and noisy version of the
underlying pathological/physiological changes via qMRI, due to measurement
errors or unpredictable influences. We use a hidden Markov random field to
model the spatial dependence in the data and develop a maximum likelihood
approach via the Expectation--Maximization algorithm with stochastic variation.
An important improvement over previous work is the assessment of variability in
parameter estimation, which is the valid basis for statistical inference. More
importantly, we focus on the expected changes rather than image segmentation.
Our research has shown that the approach is powerful in both simulation studies
and on a real dataset, while quite robust in the presence of some model
assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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