​​​​​FAS faculty members are invited to submit proposals for funding in support of interdepartmental research endeavors. Projects should involve a minimum of two FAS departments.
Eligibility: FAS faculty members
Budget: Up to $7,500
Application Materials: Apply online. Please upload in a single PDF file: 1. Proposal including abstract (no more than 200 words), description of the proposed activity and expected outcomes (no more than 1000 words), information on collaborator(s), budget and budget narrative (indicating the anticipated use of the requested funds and all funds sought or secured from other sources), and timeline.​
Application deadline: October 1; March 15​
Contact: ³ó²â25°ª²¹³Ü²ú.±ð»å³Ü.±ô²ú​â¶Ä‹
2024-25
The Different Regimes of the Hasegawa-Wakatani Equations and Comparison to the Polaris E​xperiment​
Ghassan Antar, Department of Physics
Sophie Moufawad, Department of​ Mathematics
​Nabil Nassif, Department of Mathematics​
Interest in quasi-two-dimensional (Q2D) turbulence is driven by its relevance to the physics of transport in fusion plasmas, as well as its applications in atmospheric and ocean sciences. These fields are closely linked to critical global challenges such as energy production and climate change, which are significantly influenced by turbulent transport. We propose to continue the fruitful collaboration between the Mathematics and Physics departments. Continuing our last year’s grant, we propose to study mathematically, and computationally a more advanced model, namely Hasegawa-Wakatani (HW) [1, 2]. Numerical simulation methods and statistical analyses will be developed to understand the properties of turbulence and the effects of the dynamics parallel to the magnetic field that were neglected in our previous studies. We also propose to perform measurements of the velocity and density fluctuations on the Polaris linear plasma device and then compare the experiment to the theory and computational results for a more profound understanding of turbulence. Furthermore, since recent advancements in simulating solutions of partial differential equations have sparked interest in leveraging Artificial Intelligence (AI) techniques, we will be specifically using Physics-Informed Neural Networks (PINNs) and Neural Ordinary Differential Equations (Neural ODEs) for solving the (HW) system. Interestingly, such approaches are independent of space mesh size or time step and come with their own peculiarities and limitations.​