Computational and Multi-Scale Methods for Nonlinear Electromagnetic Models in Plasmas and Nanocomposites

Oregon State University, Corvallis OR

Investigators

Abstract

This project is an inter-disciplinary collaboration involving mathematical modeling, computational simulation and experimental data for accelerating the design of advanced electromagnetic nanocomposite materials as well as alternative power generators. Nanocomposites, made of ferromagnetic nanoparticles in a dielectric, non-magnetic matrix, offer unparalleled opportunities for innovation in electromagnetic materials. The ability to predict electromagnetic material properties as a function of size, shape and concentration of inclusions in the host matrix, from computational simulations of physics-based models, will crucially aid in the digital fabrication of nanocomposites. These advances in design will enable applications including microwave frequency antennas and gradient refractive index lenses, printed electronic circuits and systems, to name a few. This objective is related to the Materials Genome Initiative's mission to accelerate materials innovation via computation. A second objective involves Magnetohydrodynamic (MHD) power generation, which is potentially a significant component of a secure U.S. energy portfolio. The lack of moving parts in an MHD power generator increases the overall efficiency of the power plant and potentially decreases carbon emissions significantly. Computational simulations of physics-based models will aid in the optimal design of these thermally efficient energy systems. The models we consider are also essential to correctly modeling solar flares which can trigger geomagnetic storms disrupting power and communications costing millions of dollars in losses. Thus our techniques will advance applications in astrophysics, space weather prediction and clean energy systems, among others. The major goal of this project is to develop novel numerical discretizations and computational multiscale electromagnetic models incorporating uncertainties in nonlinear material properties. The resulting methods will enable optimal design strategies in the applications discussed above. We will validate the effectiveness of our methods using experimental data provided by our collaborators, which will also be used to calibrate statistical descriptions of uncertainties. One area of research involves a model-driven robust design methodology for a magnetic nanocomposite material with the desired properties critically needed for advanced devices. A second area, involves nonlinear models for magnetic fields in plasma. The PIs each have demonstrated records of integration of research into education and are dedicated educators committed to recruiting minorities and creating welcoming environments. The PIs will train three doctoral students on theoretical, computational and experimental aspects of this interdisciplinary project. The partnerships with government labs and industry will provide internship opportunities for the graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.