Photonic crystals (PhC) are artificial structures fabricated with a periodicity in the dielectric function. This periodic
electromagnetic potential results in creation of energy bandgaps where photon propagation is prohibited. PhC structures
have promising use in thermal applications if optimized to operate at specific thermal emission spectrum. Here, novel
utilization of optimized PhC's in thermal applications is presented. We demonstrate through numerical simulation the
modification of the thermal emission spectrum by a metallic photonic crystal (PhC) to create high-efficiency
multispectral thermal emitters. These emitters funnel radiation from a broad emission spectrum associated with a Plancklike
distribution into a prescribed narrow emission band. A detailed quantitative evaluation of the spectral and power
efficiencies of a PhC thermal emitter and its portability across infrared (IR) spectral bands are provided. We show an
optimized tungsten PhC with a predominant narrow-band emission profile with an emitter efficiency that is more than
double that of an ideal blackbody and ~65-75% more power-efficiency across the IR spectrum. We also report on using
optimal three-dimensional Lincoln log photonic crystal (LL-PhC) emitters for thermophotovoltaic (TPV) generation as
opposed to using a passive filtering approach to truncate the broadband thermal source emission to match the bandgap of
a photovoltaic (PV) cell. The emitter performance is optimized for the 1-2μm PV band using different PhC materials,
specifically copper, silver and gold. The use of the proposed PhC in TPV devices can produce significant energy savings
not reported before. The optimal design of the PhC geometry is obtained by implementing a variety of optimization
methods integrated with artificial intelligence (AI) algorithms.
Photonic crystals (PC) are artificially fabricated crystals with a periodicity in the dielectric function. These crystals have the novel ability to mold and control light in three dimensions by opening a frequency region (bandgap) in which light is forbidden to propagate. We demonstrate using a simulation model that a photonic crystal sensor attached to a composite substrate will experience a significant change in its bandgap profile when damage is induced in the composite substrate. The frequency response of the photonic crystal sensor is modeled using the finite difference time domain (FDTD) method. A damage metric using principles of fuzzy pattern recognition is developed to evaluate and quantify the change in the frequency response in relation to the induced damage. Results for different damage scenarios are examined and reported with significantly high success rate. Successful developments of photonic crystal sensors will allow damage identification at scales not attainable using current sensing technologies.
Photonic bandgap materials (PBM) are synthetic materials that artificially manufactured at the nano-scale to control light propagation. These crystals have the ability to control light propagation in three dimensions by opening a frequency gap in which light is forbidden to propagate. When light is reflected by a nano photonic (NP) crystal a spectral signature that is directly related to its crystalline structure periodicity can be observed. It is suggested here that microscale damage in a substrate attached to the NP sensor might result in a significant change in the spectral signature of the NP sensor, hence allowing for micro-scale damage detection and quantification.
To demonstrate the use of sensors for microdamage detection in structural materials an integrated numerical modelling approach was used. The approach augments two numerical methods to simulate the effect of microdamage in the material substrate on the spectrum signature of NPC sensors. First, the finite element method (FEM) was used to simulate structural response of the NP sensor under strain induced in the substrate with and without substrate damage. Second, the results of the finite element analysis were used as inputs to simulate the optical response of the NP sensors using the finite difference time domain method (FDTD). The integrated numerical approach was applied to a wood pile NP sensor attached to a silicon substrate. The numerical analysis showed promising results. Changes in the NP spectral signatures due microdamage in the silicon substrate were successfully identified.
Photonic crystals (PC) are artificially fabricated crystals with a periodicity in the dielectric function. The spectral signature of such crystals is intricately tied to their underlying crystal lattice structural parameters. A result of this is that significant spectral changes can occur if damage is induced in the photonic crystal. In this work we present preliminary experimental results that demonstrate the possible use of photonic crystals as sensors for the detection, quantification and diagnosis of sub-micron damage. The experimentally observed variation in the reflection spectra of the photonic crystals is related to the damage induced in the material. A novel damage metric, based on principles of fuzzy pattern recognition, is introduced and is used to identify and quantify micro-damage in the photonic crystal. The corresponding damage metric is also presented and discussed. The detailed fabrication steps, as well as the advantages and limitations of this new approach are also addressed. It is concluded that photonic crystals can be successfully used for micro-damage quantification.