Combining Data-Driven and Physics-Based Methods for EM Propagation and Imaging Through Inhomogeneous Turbulent Media and During Extreme Events
Abstract
This project created a data-driven paradigm integrating physics-based computational studies of 3D EM wave dynamics and propagation beyond conventional paraxial models. Major Outcomes: (1) Developed novel methods for propagation through inhomogeneous random media by taking into account gradients and curvature of the refractive index, thus capturing non-paraxial physics effects of lensing, mirages, and reduction of focal length by stochastic fluctuations. (2) Analyzed electromagnetic (EM) propagation using the 3D Maxwell vector wave equation; recent interest in vectorial sensors requires application of vectorial propagation methods, rather than scalar wave equation approaches. (3) Discovered and analyzed permittivity gradient induced depolarization effects. (4) Characterized EM waves and scintillations associated with Rayleigh-Taylor and Richtmyer-Meshkov instabilities and turbulence in ionosphere. (5) Improved data accuracy for ionospheric electron layers by incorporating new data assimilation algorithms into computational scales of the physical models, accounting for their dynamics during geomagnetic storms.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 14, 2022
- Accession Number
- AD1230298
Entities
People
- Alex Mahalov
Organizations
- Arizona State University