Microstrip Patch Strain Sensor Miniaturization Using Sierpinski Curve Fractal Geometry

Area of Science:

• Electrical Engineering
• Materials Science
• Sensor Technology

Background:

• Microstrip patch strain sensors (MPSS) are crucial for structural health monitoring.
• Traditional MPSS designs often face limitations in miniaturization.
• Fractal geometries offer potential for novel sensor designs with enhanced properties.

Purpose of the Study:

• To propose and analyze the miniaturization of MPSS using fractal geometry, specifically the Sierpinski curve.
• To compare the performance of fractal-based sensors with traditional rectangular resonator-based sensors.
• To investigate the influence of patch shape and size on resonant frequency shift and sensor performance.

Main Methods:

• Designing and simulating MPSS with Sierpinski curve and rectangular resonator geometries.
• Ensuring both sensor designs operate at the same resonant frequency (2.725 GHz) for fair comparison.
• Conducting experimental analysis to validate simulation results and assess sensor performance under strain.

Main Results:

• Achieved good convergence between simulated and experimental results for all sensor types.
• Demonstrated significant reduction in microstrip strain sensor dimensions using Sierpinski curve fractal geometry.
• Analyzed the impact of microstrip line deformation on the proposed fractal-based sensors.

Conclusions:

• Fractal geometry, particularly the Sierpinski curve, enables effective miniaturization of microstrip patch strain sensors.
• The study validates the use of fractal shapes for developing smaller, high-performance strain sensors.
• Further research can explore other fractal geometries for advanced sensor applications.