Training artificial neural network for optimization of nanostructured VO<sub>2</sub>-based smart window performance

Balin, Igal; Garmider, V.; Long, Yi; Abdulhalim, Ibrahim

doi:10.1364/oe.27.0a1030

Cited by 35 publications

(20 citation statements)

References 26 publications

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“…For convenience in this study, the average luminous transmittance (T lum,ave ) which describes the average value of luminous transmittance in the cold (T lum,cold ) and hot state (T lum,hot ) is defined as, where AM 1.5 (λ) is the solar irradiance spectrum for an air mass of 1.5. The AM1.5 weighting spectrum is chosen for T sol calculations as it represents an overall annual average for mid-latitudes including diffuse light from the ground and sky on a south facing surface tilted 37° from horizontal 41 . The wavelength range for calculation is from 300 to 2,500 nm which accounts for higher than 99% of terrestrial solar energy.…”

Section: Methodsmentioning

confidence: 99%

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows

Liu

Tso

Lee

et al. 2020

Sci Rep

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Vanadium dioxide (VO 2) is a promising material for thermochromic glazing. However, VO 2 thermochromic smart windows suffer from several problems that prevent commercialization: low luminous transmittance (T lum) and low solar modulation ability (ΔT sol). The solution to these problems can be sought from nature where the evolution of various species has enabled them to survive. Investigations into the morphology of moths eyes has shown that their unique nanostructures provide an excellent antireflection optical layer that helps moths sharply capture the light in each wavelength from a wide angle. Inspired by this mechanism, a VO 2 thermochromic smart window coated with a TiO 2 antireflection layer with a novel nano-cone structure, is presented in this study to achieve high T lum and ΔT sol. Optimization for the key structure parameters is summarized based on the FDTD numerical simulations. The optimized structure exhibits a T lum of 55.4% with ΔT sol of 11.3%, an improvement of about 39% and 72% respectively compared to the VO 2 window without an antireflection layer. Furthermore, wide-angle antireflection and polarization independence are also demonstrated by this nano-cone coating. This work provides an alternative method to enhance the optical performance of VO 2 smart windows. Energy and environmental problems have become critical issues in modern society. It has been proven that buildings consume 20-40% of the total energy used. Most importantly, 60% of that energy is consumed to maintain thermal comfort by heating, ventilation and air conditioning (HVAC) systems 1. The huge consumption of energy by HVAC systems is mainly due to heat loss through the building envelope such as the roof, walls and windows. Among these building envelopes, approximately 50% of the energy is lost through windows, so more attention has been focused on energy efficient windows 2. Thermochromic smart windows are widely investigated because of their low cost and passive controllability of solar irradiance 3. Vanadium dioxide (VO 2) is one of the thermochromic materials to modulate transmittance of solar radiation because of its internal reversible phase change from metal (hot state) to insulator state (cold state) at a critical transition temperature of 68 °C 4. The phase change gives rise to an abrupt change of the near-infrared (NIR) transmittance, which renders VO 2 a promising candidate for thermochromic smart windows. To fulfill the demand of human vision and energy efficiency, improving luminous transmittance (T lum) and solar modulation ability (i.e. the difference of solar transmittance (T sol) between cold and hot states of materials or ΔT sol) simultaneously are key for the practical applications of VO 2 smart windows in buildings 5. Many efforts have been made to improve T lum and ΔT sol of thermochromic smart windows, such as chemical doping 6,7 , synthesizing VO 2 nanoparticles 8-11 and morphology modification 12-15. Among the different methods, depositing a planar antireflection layer is a commonly used solution to achi...

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Section: Methodsmentioning

confidence: 99%

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows

Liu

Tso

Lee

et al. 2020

Sci Rep

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“…[ 123–125 ] In photonic crystals, DNNs have been used to optimize the Q‐factor in nanocavities, [ 99 ] waveguide properties in fibers, [ 126 ] compute the band structure in 1D [ 127 ] and 2D [ 128–130 ] PCs, and predict edge states in topological insulators. [ 107 ] Last, several groups have utilized DNNs in the optimization of nanophotonic devices including plasmonic [ 131,132 ] and dielectric [ 102,133–137 ] waveguides, nanoantennas, [ 101,110 ] thermophotovoltaics, [ 138 ] power splitters, [ 133 ] biosensors, [ 139 ] smart windows, [ 140 ] and grating couplers. [ 141–143 ]…”

Section: Forward Modeling Of Aemsmentioning

confidence: 99%

“…Instead of using a pure DL model, surrogate DNNs can also be combined with the above mentioned optimization methods to solve AEM inverse design problems, which we categorically label as a hybrid approach. There are many such reports of hybrid optimization models in the DL AEM literature, [ 14,115,116,119,120,132,135,136,140,141,143,175,176,189,197,208–212 ] which we summarize very generally here while noting that the individual models may vary substantially due to the number of optimization techniques available.…”

Section: Inverse Designmentioning

confidence: 99%

“…[ 116,120,142,175 ] Another class of hybrid models involves combining generative networks with a secondary optimization procedure to iteratively refine the solution space, based on either gradient‐free ES [ 189 ] or adjoint‐based topology optimization. [ 190,194,197 ] Other conventional strategies that were also shown to facilitate inverse AEM design with DNNs include Bayesian, [ 140,212,213 ] interior point method, [ 119 ] truncated Newtonian optimization, [ 143 ] as well as search‐based lookup tables. [ 135,136,141 ]…”

Section: Inverse Designmentioning

confidence: 99%

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Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Khatib

Ren

Malof

et al. 2021

Adv Funct Materials

118

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Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented.

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“…Global warming-related concerns and environmental protection trends and policies also continue to favor the development of renewable energy generation and storage facilities [3][4][5][6]. At present, the BIPV technologies and products are only beginning to experience their expected widespread adoption, and a range of different novel technologies are being introduced into the well-established market of construction materials [7][8][9][10][11][12]. The benefits of distributed energy generation (an approach based on employing a combination of small-scale technologies to produce electricity close to the end users of power) include the avoidance of significant transmission-line losses and the provision of blackout resistance.…”

Section: Introductionmentioning

confidence: 99%

Initial Field Testing Results from Building-Integrated Solar Energy Harvesting Windows Installation in Perth, Australia

2019

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We report on the field testing datasets and performance evaluation results obtained from a commercial property-based visually-clear solar window installation site in Perth-Australia. This installation was fitted into a refurbished shopping center entrance porch and showcases the potential of glass curtain wall-based solar energy harvesting in built environments. In particular, we focus on photovoltaic (PV) performance characteristics such as the electric power output, specific yield, day-to-day consistency of peak output power, and the amounts of energy generated and stored daily. The dependencies of the generated electric power and stored energy on multiple environmental and geometric parameters are also studied. An overview of the current and future application potential of high-transparency, visually-clear solar window-based curtain wall installations suitable for practical building integration is provided.

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Training artificial neural network for optimization of nanostructured VO₂-based smart window performance

Cited by 35 publications

References 26 publications

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Initial Field Testing Results from Building-Integrated Solar Energy Harvesting Windows Installation in Perth, Australia

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Training artificial neural network for optimization of nanostructured VO2-based smart window performance

Cited by 35 publications

References 26 publications

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Initial Field Testing Results from Building-Integrated Solar Energy Harvesting Windows Installation in Perth, Australia

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Training artificial neural network for optimization of nanostructured VO₂-based smart window performance