Determining the Effect of Antipodal Vivaldi Antenna Size on Bandwidth with Accelerated Surrogate Model Based Digital Twin
MOBILE NETWORKS & APPLICATIONS, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Publication Date: 2026
- Doi Number: 10.1007/s11036-026-02511-x
- Journal Name: MOBILE NETWORKS & APPLICATIONS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Pharma Collection (ProQuest), Technology Collection (ProQuest)
- Istanbul University Affiliated: No
Abstract
Vivaldi antennas are in high demand and interest because they offer suitable solutions to overcome the difficulties such as having sufficient bandwidth, maintaining directionality and increasing efficiency. Here, an accelerated surrogate model based digital twin will be used to determine the effect of the size of an Antipodal Vivaldi antenna with a center frequency of 5 GHz that can be used in Wireless local area network/ Wireless fidelity (WLAN/WIFI) wireless communication systems on the bandwidth. Since the large number of input parameters and their interdependence make the design problem quite difficult, surrogate model-based optimization was performed. Since the optimization processes can often reach tedious times, the accelerated parallel matching method was applied. This problem was overcome by using the Incomprehensible but Intelligible-in-time (IbI) Logic algorithm, which has achieved successful results against current, existing traditional algorithms and has never been used in the antenna optimization field. The design results are demonstrated by simulating S11 (dB) and other parameters of the antenna with the help of MATLAB antenna toolbox. In addition, these parameters are verified with another 3D electromagnetic (EM) simulation tool. In the obtained results, it was observed that the optimization time was shortened and thus successful results were obtained with faster analyses. In the obtained results, it was observed that the optimization time was shortened by 30% and thus successful results were obtained with faster analyses. In addition, the study includes multiple innovations such as the goal-oriented objective function as well as the accelerated method and the surrogate model based digital twin. The proposed acceleration method can undoubtedly be adapted to any surrogate model-based optimization problem.