Date of Graduation
8-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Engineering (PhD)
Degree Level
Graduate
Department
Electrical Engineering
Advisor/Mentor
El-Ghazaly, Samir M.
Committee Member
Naseem, Hameed A.
Second Committee Member
Du, Yuchun
Third Committee Member
McCann, Roy A.
Fourth Committee Member
Decrossas, Emmanuel
Keywords
Analytical model; beamwidth enhancement; choke horn antenna; empirical model; gain enhancement; neural network
Abstract
This dissertation presents a comprehensive study focusing on the modeling, optimization, and characterization of choke horn antennas (CHAs). An analytical model designed to capture the parameters of CHA and derive the total radiated fields from the choke and waveguide elements is primarily focused on in this work. Compared to the use of simulation software, such as ANSYS HFSS (High Frequency Structure Simulator) or CST Studio, which employs numerical methods to simulate and calculate antenna performance, numerous advantages are offered by the analytical model. Analytical models provide deeper insight into electromagnetic interactions and the principles governing antenna behavior, leading to a better understanding of antenna operations and allowing for the prediction of antenna performance without the need for extensive optimization sweeps commonly used in numerical methods. Moreover, in terms of computational resources, analytical solutions can be more efficient. Substantial computational power and time, especially for complex models or fine resolution, are required by numerical methods and simulations, whereas results are often produced more quickly and with less demand on computing resources by analytical models. The first modeling approach that was explored incorporated the application of the Geometrical Theory of Diffraction (GTD), which extends Geometrical Optics principles to include diffraction alongside direct, reflected, and refracted waves. The curved edges of the choke were simplified into wedges, which facilitated the application of GTD. Additionally, the calibration of the GTD analytical model against simulation results, through the adjustment of constants derived from waveguide far-field components, established an accurate comparison with ANSYS HFSS simulations, validating the GTD approach and revealing an excellent agreement between the model and simulation data. Another modeling technique is presented for a single and double choke, which leverages the electrical current distribution of the parasitic elements to obtain the total radiated fields. The electrical current distribution of the choke will be simulated using software (the empirical part) and then imported into a derived mathematical formulation (the analytical part), resulting in a hybrid model. The electric and magnetic fields, which are excited directly from the distribution of the source currents, will be calculated through vector potentials, and the analysis will be simplified by discretizing the current distribution and employing Riemann sums for field approximation. The results of the model will be validated against ANSYS HFSS simulations which demonstrated significant computational speed improvements over conventional methods, enabling rapid design iterations and optimizations, thereby confirming its potential to enhance antenna design processes. Finally, a novel rectangular choke horn antenna was designed and analyzed using the hybrid method. The geometry of the antenna’s feed removes the need for rectangular-to-circular waveguide transitions, successfully tackling the issues of mode conversion and the possible compromise of signal integrity caused by imperfections in transitions. Gradient boosting and neural network algorithms were used to predict the current distributions and antenna performance values. The antenna was fabricated, and its radiation patterns were measured to validate the model and simulation results, which showed excellent agreement.
Citation
Alquaydheb, I. (2024). Modeling, Optimization, and Characterization for Choke Horn Antennas. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5407