Computational electromagnetic modeling and optimization techniques to enhance the accuracy and efficiency of a 2.45 GHz pyramidal horn antenna by Newton-Raphson method for IEMI testing
This paper presents computational electromagnetic techniques that can be effectively used to improve the accuracy and efficiency of designing a 2.45GHz pyramidal horn antenna for high-power microwave applications. The design parameters were determined using analytical methods, and optimization techn...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
World Scientific
2026
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/123776/ https://www.worldscientific.com/doi/10.1142/S2424862225500125 |
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| Summary: | This paper presents computational electromagnetic techniques that can be effectively used to improve the accuracy and efficiency of designing a 2.45GHz pyramidal horn antenna for high-power microwave applications. The design parameters were determined using analytical methods, and optimization techniques were conducted with CST Microwave Studio. The highest antenna gain achieved was 19.89dBi, which is comparable to the proposed numerical value of 20dBi. The return loss of the horn antenna was calculated numerically and found to be-26.2 dB, with a resonance frequency of 2.45GHz. The Voltage Standing Wave Ratio (VSWR) was measured to be less than 1.5, while the time required for the analysis was 120min, and the storage needed was 17 GB. Using CST Microwave Studio and optimization techniques, the return losses for the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Interior Point Quadratic Newton (IPQN), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) were-29.61dB,-30.45dB,-26.2dB, and-30.24dB, respectively. The corresponding VSWR values for GA, PSO, IPQN, and CMA-ES were 1.491, 1.42, 1.141, and 1.13. The gains for the same methods were found to be 20.3dBi, 20.2dBi, 19.8dBi, and 20dBi, respectively. This demonstrates that CST Microwave Studio effectively optimizes return loss, VSWR, and gain. In comparison to the GA optimizer, which required 3876min and 29s with a storage capacity of 23.4GB to perform 145 evaluations, the PSO optimizer took 7055min and 5s to complete 241 evaluations, needing 36GB of storage. Additionally, compared to CMA-ES, which utilized 26GB of storage to finish 186 evaluations in 6131min and 42 s, IPQN required only 155min and 44s with 24.6GB of storage to complete 3729 evaluations. |
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