Data Driven Modelling of Microstrip Frequency Selective Surface for X Band Applications

Year 2023, Volume: 3 Issue: 1, 22 - 26, 30.06.2023

Aysu Belen

Abstract

Efficient and accurate modelling approaches have become necessary in computational science and engineering due to the rising complexity and high dimensionality of physical and engineered systems. The utilisation of data-driven surrogate modelling has surfaced as a potent methodology to overcome the disparity between computationally expensive simulations and prompt, dependable predictions. The current study offers a thorough examination of data-driven surrogate modelling methods as they pertain to the optimisation and design of microstrip frequency selective surfaces (FSSs) within microwave systems. In this discourse, we delve into the rudiments of surrogate modelling, diverse categories of surrogate models, and their respective merits and demerits in the realm of FSS modelling. The utilisation of widely used Artificial Intelligence algorithms is implemented for the purpose of data-driven surrogate modelling, and their efficacy is evaluated through the Relative Mean Error metric. The research findings indicate that the M2LP surrogate model exhibits optimal performance in the specific scenario under investigation. Furthermore, the Honey Bee Mating Optimisation algorithm is utilised to optimise the design of FSS. The results of our study demonstrate that data-driven surrogate modelling is an efficient and effective method for designing and optimising microstrip frequency selective surfaces (FSSs). Specifically, our approach yielded a gain improvement of nearly 3 dB within the chosen frequency band. The forthcoming research endeavours to investigate the optimisation of more intricate FSS designs for analogous applications that encompass broader operation bands.

Keywords

Data driven, Surrogate model, Antenna, Gain, optimization

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