Ultra‐Wideband, Polarization‐Independent, Wide‐Angle Multilayer Swastika‐Shaped Metamaterial Solar Energy Absorber with Absorption Prediction using Machine Learning

Parmar

2022

Sci Rep

Self Cite

Energy utilization is increasing day by day and there is a need for highly efficient renewable energy sources. Solar absorbers with high efficiency can be used to meet these growing energy demands by transforming solar energy into thermal energy. Solar absorber design with highly efficient and Ultra-broadband response covering visible, ultraviolet, and near-infrared spectrum is proposed in this paper. The absorption response is observed for three metamaterial designs (plus-shape slotted design, plus-shape design, and square-shape design) and one optimized design is used for solar absorber design based on its high efficiency. The design results are compared with AM 1.5 spectral irradiance response. The electric field response of the plus-shape slotted metamaterial design is also presented which matches well with the absorption results of different solar spectrum regions. The results proved that the attained absorption response showing wide angle of incidence. Machine learning is also used to examine the design data in order to forecast absorption for various substrate thickness, metasurface thickness, and incidence angles. Regression and forecasting simulations based on machine learning are used to try to anticipate absorber behaviour at forthcoming and intermediate wavelengths. Simulation results prove that Machine Learning based methods can lessen the obligatory simulation resources, time and can be used as an effective tool while designing the absorber. The proposed highly efficient, wide-angle, ultra-broadband solar absorber design with its behavior prediction capability using machine learning can be utilized for solar thermal energy harvesting applications.

Section: Design Modelling and Resultsmentioning

confidence: 99%

Ultra-broadband, wide-angle plus-shape slotted metamaterial solar absorber design with absorption forecasting using machine learning

Parmar

2022

Sci Rep

Self Cite

“…This particular design achieved average absorption of 95% from 0.1 to 3 µm and for 2516 nm, the absorption above 90% is achieved. [34] Li and co-authors also presented a titanium material-based solar energy absorber design that can achieve absorption above 90% for a range of 712 nm bandwidth. [35] Patel et al reviewed numerous solar energy absorbers based on 2D materials for numerous applications including sensing, solar energy harvesting, solar cells, photovoltaic systems, etc.…”

Section: Doi: 101002/adts202200139mentioning

confidence: 99%

Optimization of Metamaterial‐Based Solar Energy Absorber for Enhancing Solar Thermal Energy Conversion Using Artificial Intelligence

Surve

et al. 2022

Advcd Theory and Sims

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A highly efficient psi‐shaped solar energy absorber in the paper is developed. This structure is designed using titanium‐based psi shaped resonator, SiO2 based substrate, and Tungsten metal‐based base layer to achieve a near‐perfect ultrawideband absorption spectrum under solar radiation. The average absorption of 97.04% is achieved in the observed range including the ultraviolet to mid‐infrared regimes and the ultrawideband characteristics are also achieved. It is also noted that for the bandwidth of 3730 and 2500 nm, above 90% and 95% absorption rate are achieved, respectively. The effect of several design parameters on the spectrum of absorption is explored and accordingly, optimized design is identified. Furthermore, the absorption response of the developed solar energy absorber is polarization‐insensitive and wide‐angle from 0° to 50°. Experiments are meant to identify the optimal parameter values for solar absorber design by employing a random restart hill climbing optimization approach. According to the experimental data, this strategy can reduce the computing power and time requirements of the simulation process by 97.57%.

“…Machine learning (ML) can be applied to predict or forecast the behavior of different parameters of electronic and photonic systems. ML can also be used to observe, predict, and forecast the reflectance response of the photonic devices 31 – 34 . Misilmani reviewed ML applications for antenna designs utilizing regression models 35 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices

Surve

et al. 2022

Sci Rep

Self Cite

Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today’s high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR). The high gain requirement is met by adding metamaterial superstrates having thin wires (TW) and SRRs. The reconfigurability is achieved by adding three PIN diode switches. Multiple designs have been observed by adding superstrate layers ranging from one layer to four layers with interchanging TWs and SRRs. The TW metamaterial superstrate design with two layers is giving the best performance in gain, bandwidth, and the number of bands. The design is optimized by changing the path’s physical parameters. To shrink simulation time, Extra Tree Regression based machine learning model is used to learn the behavior of the antenna and predict the reflectance value for a wide range of frequencies. Experimental results prove that the use of the Extra Tree Regression based model for simulation of antenna design can cut the simulation time, resource requirements by 80%.