RESUMO
This proposed design presents a novel bandpass filter employing a Marchand balun to attain ultra-wideband (UWB) performance extending from 3.1 to 10.7 GHz with 6.8 GHz central frequency and 110% FBW. The UWB bandpass filter's fractional bandwidth can be tailored owing to the diverse input/output impedances of the planar Marchand balun. This adaptability is accomplished by connecting two planar Marchand baluns consecutively, leveraging the concepts of transversal filter ideas and multilayer LCP technology resulting in 0.3 dB and 12 dB insertion and return losses respectively. In-depth guidelines for the formulation and synthesis of the UWB bandpass filter are incorporated.
RESUMO
This paper introduces a novel, cost-effective solution designed to achieve large absorption bandwidth within the THz spectrum employing a miniaturized, single-layer metamaterial structure. The designed structure features a single circular ring composed of an ohmic resistive sheet with notably higher sheet resistance than traditional metallic resonators. This distinctive design is implemented on a lossy dielectric polyimide substrate with a backing of metallic gold. Our developed absorbing structure demonstrates the capability to achieve a substantial absorption bandwidth ranging from 3.78 to 4.25 THz, maintaining a consistent absorption rate of over 90%. Moreover, we conducted an analysis to assess its absorption performance under various sheet resistance values within the top layer. Additionally, we characterized its angular stability and polarization insensitivity through oblique incident and polarization angle analysis. Finally, an RLC circuital and interference theory approach is adopted to justify its simulated results. The proposed absorber shows potential for a broad spectrum of applications, encompassing communication, imaging, and diverse integrated circuits operating within the THz band.
RESUMO
Thermal energy harvesting has seen a rise in popularity in recent years due to its potential to generate renewable energy from the sun. One of the key components of this process is the solar absorber, which is responsible for converting solar radiation into thermal energy. In this paper, a smart performance optimization of energy efficient solar absorber for thermal energy harvesting is proposed for modern industrial environments using solar deep learning model. In this model, data is collected from multiple sensors over time that measure various environmental factors such as temperature, humidity, wind speed, atmospheric pressure, and solar radiation. This data is then used to train a machine learning algorithm to make predictions on how much thermal energy can be harvested from a particular panel or system. In a computational range, the proposed solar deep learning model (SDLM) reached 83.22 % of testing and 91.72 % of training results of false positive absorption rate, 69.88 % of testing and 81.48 % of training results of false absorption discovery rate, 81.40 % of testing and 72.08 % of training results of false absorption omission rate, 75.04 % of testing and 73.19 % of training results of absorbance prevalence threshold, and 90.81 % of testing and 78.09 % of training results of critical success index. The model also incorporates components such as insulation and orientation to further improve its accuracy in predicting the amount of thermal energy that can be harvested. Solar absorbers are used in industrial environments to absorb the sun's radiation and turn it into thermal energy. This thermal energy can then be used to power things such as heating and cooling systems, air compressors, and even some types of manufacturing operations. By using a solar deep learning model, businesses can accurately predict how much thermal energy can be harvested from a particular solar absorber before making an investment in a system.