(Course description last updated for academic year 2016-17).
Synopsis

Draft 15-08-16

 

Simulations of light scattering experiments. The experimental techniques of static and dynamic light scattering will be introduced and discussed. We will test intensity correlation functions, the Siegert relation and other parameters that are not directly accessible in experiments. This will be done using Brownian Dynamics simulations.

Computing Flow in a Slit. Using stochastic rotation-dynamics or Dissipative Particle Dynamics (DPD): SRD and DPD are  mesoscopic solvent models that are very powerful for the simulation of systems with a separation of length scales. First we will simulate a fluid in a slit with stationary hard boundaries. By then applying a homogenous force to all fluid particles in the slit we will see how a classical Poisseuille flow develops. From that we can compute the fluids’ viscosity. Different boundary conditions and the concept of slip length along a wall will be discussed. Finally, we will measure the viscosity as function of the fluid density.

Simulating Micro-Rheology. Measuring the diffusion coefficient of a colloid in a viscous and viscoelastic medium. In particular we will compute the mean-square displacements of a colloid in a fluid using stochastic rotation dynamics to obtain the viscosity of the solvent.

Coupling of Colloid Motion to the Surrounding Fluid. Simulating a fluid performing Couette-flow, where one surface is stationary while the other is moving. What happens to the fluid flow when colloids that are much larger than the fluid particles are added?

Simulating Depletion Forces. In a mixture of small and large colloids one typically observes an attractive force between the large spheres, which is purely entropic in origin. This so-called depletion interaction will be simulated as function of the colloidal size ratios concentrations. (This is a static problem, which we will address with Monte Carlo Simulations.)

Simulating Polymers. Using off-lattice Monte Carlo simulations we will describe a polymer in terms of a random walk (fixed distances with random rotation).

We will briefly discuss why C, Fortran or other languages more suitable for large calculations. Interpreted languages such as Python or Matlab are perhaps more intuitive and easy to handle but are much slower.

Literature

  1. Frenkel & B. Smit , Understanding Molecular Simulation , Book, 2nd edition Academic Press, ISBN 0-12-267351-4
  2. P. Allen & D. J. Tildesly, Computer Simulation of Liquids, Oxford Science Publications, ISBN 0-19-855645-4

 

Handbooks for programming in Fortran, C, and Python

http://www.mrao.cam.ac.uk/~rachael/compphys/SelfStudyF95.pdf

http://www-pnp.physics.ox.ac.uk/~tseng/teaching/lab/handbook_C.pdf

http://www.physics.nyu.edu/pine/Teaching.html

 

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