Optimization-based synthesis of a metamaterial electric cloak using nonhomogeneous composite materials

Steckiewicz, Adam; Choroszucho, Agnieszka

doi:10.1080/09205071.2019.1654928

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…Application field: Electromagnetics 159 Li, Chen, Zeng, et al [368] 2017 Adaptive GA optimization framework 160 Zhang and Cuii [369] 2017 BPSO optimization framework 161 Ahmed, Chandra, and Al-Behadili [370] 2017 GA Inverse design 162 Han, Cao, Gao, et al [371] 2017 GA optimization framework 163 Pfeiffer and Tomasic [372] 2017 GA optimization framework 164 Pelluri and Appasani [373] 2017 GA optimization framework 165 Feng, Chen, and Huang [374] 2017 GA optimization framework 166 Allen, Dykes, Reid, et al [375] 2017 GA optimization framework 167 Ding, Zhang, Zhang, et al [376] 2017 GA optimization framework 168 Mahdi and Taha [377] 2017 GA Topology optimization 169 Su, Lu, and Li [378] 2017 PSO optimization framework 170 Orlandi [379] 2018 Differential evolution optimization framework (DE) algorithm 171 Bagmancı, Karaaslan, Altıntaş, et al [380] 2018 GA optimization framework 172 Lim, Song, Kim, et al [381] 2018 GA optimization framework 173 Corrêa, Resende, Bicalho, et al [382] 2018 GA optimization framework 174 Kumar, Behera, and Suraj [383] 2018 GA optimization framework 175 Clemens, Iskander, Yun, et al [189] 2018 Hybrid genetic programming optimization framework 176 Soltani, Soltani, and Aguili [384] 2019 GA Inverse design 177 Ibili, Karaosmanoglu, and Ergul [385] 2019 GA optimization framework 178 Seshadri and Gupta [386] 2019 GA optimization framework 179 Nanda, De, Sahu, et al [387] 2019 GA optimization framework 180 Assal, Benzerga, Sharaiha, et al [388] 2019 GA optimization framework 181 Karatzidis, Kantartzis, Pyrialakos, et al [389] 2019 GA optimization framework 182 Tian and Li [390] 2019 GA optimization framework 183 Yuan, Ma, Sui, et al [391] 2019 GA Topology optimization 184 Yanzhang and Jinghao [392] 2019 GA Topology optimization 185 Sui, Ma, Chang, et al [393] 2019 IAGA optimization framework 186 Steckiewicz and Choroszucho [394] 2019 PSO optimization framework 187 Hao, Du, and Zhang …”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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“…In WPT technology, metamaterials have also been used to better control the magnetic field and the possibility of shaping it [28,29]. Thanks to the advantages of metamaterials, it is possible to shape coils in such a way that it is possible to achieve the assumed parameters of the system.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Influence of the Coil Resistance on the Power and Efficiency of the WPT System

Stankiewicz

2023

Energies

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This paper presents the results of an analysis of a low-power Wireless Power Transfer (WPT) system. The system consists of periodically distributed planar spiral coils that form the transmitting and receiving planes. An analytical and numerical analysis of the WPT system, over the frequency range from 100 to 1000 kHz, was carried out. A simpler and faster solution is the proposed use of an equivalent circuit represented by a single WPT cell. The influence of coil resistance changes on the power and efficiency of the WPT system was studied. This was obtained by changing the diameter of the wire from which the coils were wound. In addition, the size of the coil, the number of turns, and the distance between the two planes have changed. After a detailed analysis, the results showed that the highest efficiency values were obtained for a wire diameter of 200 μm, which means the lowest coil resistance. However, the lowest efficiency values were obtained for the smallest wire diameter, i.e., 100 µm, which means the highest coil resistance. In this case, the efficiency decreased by more than 40%. Based on the calculation results, it was also shown that it was better to accept the skin effect (efficiency decreased below 7%) than to reduce the wire diameter to eliminate it.

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“…The first MM structure, which comprises split-ring resonators (SRR) and metallic wires, was experimentally verified by Smith et al in 2000 [6]. These unique characteristics of MMs enable many practical applications, including super-lense [7], [8] and cloaking technology [9], [10]. Furthermore, they are widely utilized to realize different electromagnetic devices such as filters, absorbers, and sensors [11]− [13].…”

Section: Introductionmentioning

confidence: 99%

Overview of Metamaterials-Integrated Antennas for Beam Manipulation Applications: The Two Decades of Progress

Esmail

Koziel

Golunski³

et al. 2022

IEEE Access

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Metamaterials (MMs) are synthetic composite structures with superior properties not found in naturally occurring materials. MMs have gained massive attention over the last two decades because of their extraordinary properties, such as negative permittivity and permeability. These materials enable many applications in communication subsystems, especially in the field of antenna design, to enhance gain, bandwidth, and efficiency, reduce the size, and deflect the radiation pattern. The demand for beam-deflection antennas is significant in modern wireless communication research studies due to their importance in enhancing service quality, system security, avoiding interference, and economizing power. The MM structures are usually included in the vicinity of the radiating element or incorporated in the antenna substrate for controlling the radiation pattern. This review study provides an introduction to MMs, focusing on their electromagnetic properties, classification, and design approaches. Furthermore, a detailed study of using the MMs to manipulate the radiation is carried out, where different properties such as the positive/negative refractive index, epsilon-near-zero (ENZ), and mu near-zero (MNZ) are employed to achieve a beamdeflection antenna. Reconfigurable MMs are also loaded to the antenna to achieve multi-directional beam deflection with negligible effect on the antenna's physical size. Moreover, the gradient-index (GRIN) based on MMs is used to obtain high deflection angles with minor effects on other antenna properties.INDEX TERMS, Beam deflection, gradient-index (GRIN), metamaterials (MMs), reconfigurable MMs.

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Optimization-based synthesis of a metamaterial electric cloak using nonhomogeneous composite materials

Cited by 6 publications

References 11 publications

Machine Intelligence in Metamaterials Design

Machine Intelligence in Metamaterials Design

Estimation of the Influence of the Coil Resistance on the Power and Efficiency of the WPT System

Overview of Metamaterials-Integrated Antennas for Beam Manipulation Applications: The Two Decades of Progress

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