Theory for Invisible Thermal Sensors: Optimization Scheme

Abstract

AbstractMetamaterial-based devices have been extensively explored for their intriguing functions, such as cloaking, concentrating, rotating, and sensing. However, they are usually achieved by employing metamaterials with extreme parameters, critically restricting engineering preparation. In this chapter, we propose an optimization model with particle swarm algorithms to simplify parametric designs to realize bilayer thermal sensors composed of bulk isotropic materials (circular structure). For this purpose, the fitness function is defined to evaluate the difference between the actual and expected temperatures. By choosing suitable materials for different regions and treating the sensor, inner shell, and outer shell radii as design variables, we finally minimize the fitness function via particle swarm optimization. The designed scheme is easy to implement in applications and shows excellent performances in detective accuracy and thermal invisibility, which are confirmed by finite-element simulations and laboratory experiments. The optimization model can also be flexibly extended to a square case. This method can calculate numerical solutions for difficult analytical theories (circular structure) and optimal solutions for problems without analytical theories (such as square structure), providing new inspiration for simplifying the design of metamaterials in various communities.