Ball lightning is an unexplained phenomenon reported by thousands of eyewitnesses as a fireball moving unpredictably and independently of the wind, observed sometimes during lightning storms. Here a potential theory for the creation of the phenomenon is explored. At the tip of a lightning stroke reaching the ground, a relativistic electron bunch of ~10^(14) electrons can be produced, which in turn excites intense microwave radiation as the product of coherent transition radiation. This intense radiation (~310 MV/m) ionizes the surrounding air, and the radiation pressure evacuates the resulting plasma, forming a spherical plasma bubble that traps the electromagnetic energy as a standing microwave with a wavelength of 30 cm and amplitude of 170 MV/m. This mechanism is explored using particle-in-cell plasma simulations to replicate previous results of a plasma-trapped microwave bubble lifespan of ~15 ns. Next, the goal is to extend this model by incorporating additional physical effects, such as gravity, the upward force due to air convection, and plasma shell surface tension. The ultimate goal is to develop a comprehensive model that explains ball lightning formation, its stability during its lifespan, and the two most common forms of termination. This theory suggests how ball lightning can be created in a laboratory or triggered during thunderstorms. The results of this research will advance lightning protection and aviation safety.

An inertial electrostatic confinement (IEC) fusion device traps plasma particles with a spherically symmetric electrostatic field that accelerates ions toward the device's center, where they can collide and potentially fuse. The IEC fusion device is simpler and less expensive to construct when compared to magnetic confinement devices, like the tokamak: its employment will benefit fusion research. Here, the plasma motion in an IEC device is numerically simulated by solving the Vlasov-Poisson system of partial differential equations in spherical coordinates with one dimension of space and one dimension of momentum. The stability and accuracy of solutions obtained by various numerical methods, including the finite element method and the finite volume method, are compared.

References

• Poster presented at Florida Undergraduate Research Conference (FURC 2023), St. Thomas University, Miami FL, US (February 17-18, 2023) [PDF file, 447Kb]

Advancing Scan-specific Parameter-free Artifact Reduction in K-space (SPARK) with Gradient-based Optimization

Junfu Cheng, undergraduate (BS) student in Electrical Engineering, Florida Tech

Partially Parallel Acquisition (PPA) uses spatial information contained in the component coils of an array to replace spatial encoding, typically performed using gradients, thereby reducing imaging time. Parallel imaging reconstruction for accelerated acquisitions of magnetic resonance imaging (MRI) is generally posed as an optimization problem. SPARK is a convolutional neural network (CNN) that works with physical-based reconstruction methods to reduce artifacts in accelerated MRI. The CNN predicts a K-space correction term for each reconstructed coil. We apply different gradient-based optimization schemes in the neural network single channel training to minimize the mean square error (MSE) as a loss function value: e.g., steepest descent, conjugate gradient, and adaptive moment (ADAM) optimization algorithms supplied with various methods for the optimal step size search (e.g., diminishing, bisection, and golden section methods). We aim at the optimal combination of strategies to minimize lapsed computational time to train all 31 channels of neural network connections between inputs and outputs.

References

• Poster presented at Florida Undergraduate Research Conference (FURC 2023), St. Thomas University, Miami FL, US (February 17-18, 2023) [PDF file, 1.43Mb]

Undergraduate Research Minisymposium

Dept. of Mathematical Sciences, Florida Institute of Technology

Full presentation (Graduate Seminar, February 10, 2023) is available here [PDF file, 41.8Mb & MP4 file, 256.2Mb].