Optimization of subsurface imaging antenna capacitance through geometry modeling using Archimedes, Lichtenberg and Henry gas solubility metaheuristics

Authors

Adrian Genevie Janairo, De La Salle University, Manila
Jonah Jahara G. Baun, De La Salle University, Manila
Ronnie S. Concepcion II, De La Salle University, Manila
R-jay Relano, De La Salle University, Manila
Kate Francisco, De La Salle University, Manila
Mike Louie Enriquez, De La Salle University, Manila
Argel A. Bandala, De La Salle University, Manila
Ryan Rhay P. Vicerra, De La Salle University, Manila
Melchizedek I. Alipio, De La Salle University, Manila
Elmer P. Dadios, De La Salle University, Manila

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

Publication Date

2022

Abstract

Capacitive resistivity subsurface imaging of roads operating at very low frequency is susceptible to antenna characteristic capacitance dynamics that may cause unwanted signal reflection, coupling, and unfavorable effect on reception sensitivity. Antennas are conventionally modeled using a complex and repetitive default mathematical method that is prone to human error and discrete results. To address this emerging challenge, this study has developed a new technique for plate-wire antenna capacitance optimization through equatorial dipole-dipole antenna geometry modeling using genetic programming (GP) integrated with metaheuristic methods, namely Archimedes optimization algorithm (AOA), Lichtenberg algorithm (LA), and Henry gas solubility optimization (HGSO). GP was used to construct the antenna capacitance fitness function based on 241 combinations of wire antenna radius and elevation, and dipole plate elevation, length, width, and thickness measurements. Minimization of antenna capacitance (approaching 1 nF) to achieve quasi-static condition was performed using GP-AOA, GP-LA, and GP-HGSO. The 3 metaheuristic-based antennas were 3D-modeled using Altair Feko and compared from the default antenna’s electrical features. It was found that even with the smallest dipole geometry, hybrid GP-LA antenna model exhibited the most practical outputs at 5 kHz with correct directional propagation based on its radiation pattern, a realistic receiver voltage of -8.86 dBV which is close to the default model, and a high-power efficiency of 99.925%. While hybrid GP-AOA and GP-HGSO resulted in indirect coupled transceiver systems with unsuitable antenna characteristic capacitance inducing anomalous receiver voltages. The experimental results prove the validity of the developed technique for more accurate determination of optimal antenna geometry.

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Digitial Object Identifier (DOI)

10.1109/IEMTRONICS55184.2022.9795789

Recommended Citation

Janairo, A., Baun, J. G., Concepcion, R. S., Relano, R., Francisco, K., Enriquez, M., Bandala, A. A., Vicerra, R. P., Alipio, M. I., & Dadios, E. P. (2022). Optimization of subsurface imaging antenna capacitance through geometry modeling using Archimedes, Lichtenberg and Henry gas solubility metaheuristics. IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) https://doi.org/10.1109/IEMTRONICS55184.2022.9795789

Disciplines

Electrical and Computer Engineering

Keywords

Antennas (Electronics); Metaheuristics

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