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Fully Coupled Fluid And Electrodynamic Modeling Of Plasmas: A Two-Fluid Isomorphism And A Strong Conservative Flux-Coupled Finite Volume Framework, Richard Joel Thompson
Fully Coupled Fluid And Electrodynamic Modeling Of Plasmas: A Two-Fluid Isomorphism And A Strong Conservative Flux-Coupled Finite Volume Framework, Richard Joel Thompson
Doctoral Dissertations
Ideal and resistive magnetohydrodynamics (MHD) have long served as the incumbent framework for modeling plasmas of engineering interest. However, new applications, such as hypersonic flight and propulsion, plasma propulsion, plasma instability in engineering devices, charge separation effects and electromagnetic wave interaction effects may demand a higher-fidelity physical model. For these cases, the two-fluid plasma model or its limiting case of a single bulk fluid, which results in a single-fluid coupled system of the Navier-Stokes and Maxwell equations, is necessary and permits a deeper physical study than the MHD framework. At present, major challenges are imposed on solving these physical models …
The Engineering Design Process As A Model For Stem Curriculum Design, Krystal Sno Corbett
The Engineering Design Process As A Model For Stem Curriculum Design, Krystal Sno Corbett
Doctoral Dissertations
Engaging pedagogics have been proven to be effective in the promotion of deep learning for science, technology, engineering, and mathematics (STEM) students. In many cases, academic institutions have shown a desire to improve education by implementing more engaging techniques in the classroom. The research framework established in this dissertation has been governed by the axiom that students should obtain a deep understanding of fundamental topics while being motivated to learn through engaging techniques. This research lays a foundation for future analysis and modeling of the curriculum design process where specific educational research questions can be considered using standard techniques. Further, …
Pattern Recognition For Electric Power System Protection, Yong Sheng
Pattern Recognition For Electric Power System Protection, Yong Sheng
Doctoral Dissertations
The objective of this research is to demonstrate pattern recognition tools such as decision trees (DTs) and neural networks that will improve and automate the design of relay protection functions in electric power systems. Protection functions that will benefit from the research include relay algorithms for high voltage transformer protection (TP) and for high impedance fault (HIF) detection. A methodology, which uses DTs and wavelet analysis to distinguish transformer internal faults from other conditions that are easily mistaken for internal faults, has been developed. Also, a DT based solution is proposed to discriminate HIFs from normal operations that may confuse …