Mon, 10 Oct 2016
12:00
L3

A space of states in Berkovits string theory: a mathematical approach

Michael Movshev
(SUNY at Stony Brook)
Abstract

Pure spinor space, a cone over orthogonal Grassmannian OGr(5,10), is a central concept in the Berkovits formulation of string theory. The space of states of the beta-gamma system on pure spinors is tensor factor in the Hilbert space of string theory . This is why it would be nice to have a good definition of this space of states. This is not a straightforward task because of the conical singularity of the target. In the talk I will explain a strategy for attacking  conical targets. In the case of pure spinors the method gives a formula for partition function of pure spinors.

Mon, 31 May 2010

17:00 - 18:00
Gibson 1st Floor SR

Mathematical, Numerical and Physical Principles for Turbulent Mixing

James Glimm
(SUNY at Stony Brook)
Abstract
Numerical approximation of fluid equations are reviewed. We identify numerical mass diffusion as a characteristic problem in most simulation codes. This fact is illustrated by an analysis of fluid mixing flows. In these flows, numerical mass diffusion has the effect of over regularizing the solution. Simple mathematical theories explain this difficulty. A number of startling conclusions have recently been observed, related to numerical mass diffusion. For a flow accelerated by multiple shock waves, we observe an interface between the two fluids proportional to Delta x-1, that is occupying a constant fraction of the available mesh degrees of freedom. This result suggests
  • (a) nonconvergence for the unregularized mathematical problem or
  • (b) nonuniqueness of the limit if it exists, or
  • (c) limiting solutions only in the very weak form of a space time dependent probability distribution.
The cure for the pathology (a), (b) is a regularized solution, in other words inclusion of all physical regularizing effects, such as viscosity and physical mass diffusion. We do not regard (c) as a pathology, but an inherent feature of the equations.
In other words, the amount and type of regularization of an unstable flow is of central importance. Too much regularization, with a numerical origin, is bad, and too little, with respect to the physics, is also bad. For systems of equations, the balance of regularization between the distinct equations in the system is of central importance.
At the level of numerical modeling, the implication from this insight is to compute solutions of the Navier-Stokes, not the Euler equations. Resolution requirements for realistic problems make this solution impractical in most cases. Thus subgrid transport processes must be modeled, and for this we use dynamic models of the turbulence modeling community. In the process we combine and extend ideas of the capturing community (sharp interfaces or numerically steep gradients) with conventional turbulence models, usually applied to problems relatively smooth at a grid level.
The numerical strategy is verified with a careful study of a 2D Richtmyer-Meshkov unstable turbulent mixing problem. We obtain converged solutions for such molecular level mixing quantities as a chemical reaction rate. The strategy is validated (comparison to laboratory experiments) through the study of 3D Rayleigh-Taylor unstable flows.
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