A frequency selective surface that acts as an RF filter and helps reduce the radar cross section of antennas consists of a pattern of geometrical objects with thousands of possibilities, which makes the physical testing of each one time consuming.
One of the largest leaps in defense technology developed in recent years focuses on stealth airplanes and ships that avoid radar detection by combining several technologies that include the shape of the target’s surfaces to reflect energy away from the source, and the use of radar-absorbent materials. For an antenna to operate properly on a ship or aircraft it cannot be completely covered up, which makes it one of the remaining components with a large radar cross section (RCS) that can destroy the overall system’s invisibility to radar.
The RCS depends on the polarization and frequency of the incident wave. When an electromagnetic wave is incident on a target, electric currents are induced in the target and a secondary radiation from that target produces a scattered wave.
The scattered field is partially reflected straight back to the source of the incident wave, and this is the principle upon which radar is based. The peak reflected wave is related to the standard antenna gain and its peak effective surface area. In this case, there is an ironic twist: antenna designers normally look to maximize antenna gain, but to lower the RCS they must reduce the gain.
One way around this problem is to employ a frequency selective surface (FSS), which consists of a pattern of shaped holes or surfaces on a substrate and creates a bandpass filter. In the intended frequency range, where radio operators are transmitting or receiving, the antenna acts normal. At other frequencies, the FSS absorbs rather than scatters incident radiation. Antennas are housed in a protective enclosure called a radome. If that enclosure is made from an FSS, its RCS is reduced at all but the operating frequencies.
Geometric Patterns as a Filter
An FSS is constructed from periodically arranged metallic patterns of an arbitrary geometry, with openings similar to patches within a metallic screen (Figure 1). The performance of an FSS is linked to its shape, thickness, choice of substrate, and the phasing between individual elements. COMSOL Multiphysics has been an invaluable tool for studying the physical configurations and the resonant frequencies for certain bandwidths.
An FSS consists of a series of geometrical objects, and can be electrically large in terms of wavelength with many instances of the object. This makes simulating the entire surface extremely cumbersome and expensive in terms of compute power and time. COMSOL Multiphysics has a convenient answer to this problem in the Periodic Boundary Condition (PBC) feature, which allows the simulation of a single cell unit and thus a less time consuming process (Figure 2). This feature provides continuity of the electric and magnetic fields, producing equivalent results of a simulated array of objects.
Figure 3 shows an example FSS made of simple metallic strips surrounded by air. The simulation mesh was created for one of the metallic strips, as shown from the frequency plot (bottom right), were it has a passband in the region of 40 GHz.
To validate the simulation, the results from a case that was already dealt with in the literature were in COMSOL Multiphysics with the aim of tuning the simulation procedure. In a second step, this same validated simulation was modeled with other types of FSS, while changing geometries and materials and evaluating the impact of these changes on FSS performance.
The software was used to investigate the frequency responses of a variety of simple shapes and sizes, and how they were distributed on a surface. It is possible to make the design more complicated by using two structures with complementary behavior. In this way a design can be created with multiple resonant frequencies. The ability to try any number of shapes highlights the capacity of the software to help find a good solution. The alternative would be fabricating various shapes for the FSS and physically testing them, which would involve more time and expense. It only takes a few minutes with modeling to determine if a pattern is worth pursuing in detail.
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