Computational Non-Ideal Plasma Physics
Abstract
We propose to develop and utilize a variety of theoretical and computational techniques for modeling ultracold-neutral and dense-gas plasmas. Our main thrust is toward generalized hydrodynamic descriptions that incorporate the microscopic physics of strong Coulomb coupling, yet describe heterogeneous macroscopic plasmas that occur in the laboratory and in nature.We will further develop hydrodynamic models for strongly coupled plasmas based on our previous dynamical density functional theory efforts. We will extend that work to include magnetic fields, numerical methods and applications to ultracold neutral plasmas and densegas plasmas. Validation of the models will be carried out using our molecular dynamics tools (see below).A grand challenge in computational modeling is the treatment of non-equilibrium quantum systems. We will extend our previous work on empirical momentum-dependent potentials as an avenue of attack on this particularly challenging problem. Our main thrust will be an analysis of the exact time-dependent Schrodinger equation that can inform the functional forms for nonseparable interactions. We will also expand the training set for the empirical parameters and use modern statistical learning techniques. Finally, these new potentials will be implemented into our codes, validated versus experimental data and use to tackle the main application areas of this work.We plan to develop a pure Python open source molecular dynamics code, called Sarkas. This code will have numerous advantages over other codes, including being written in a user-friendly language with a vast scientific ecosystem, specific tools and models for the plasma physics community, best-in-class website and documentation, and efficient and fast algorithms. The goal of the subproject is to change the way experiments are performed by placing a tool in the hands of experimental graduate students who can run MD simulations as part of the experimental process.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501710394
Entities
People
- Michael S. Murillo
Organizations
- Air Force Office of Scientific Research
- Michigan State University
- United States Air Force