Improved Chimpanzee Optimization Algorithm Based on Multi-Strategy Fusion and Its Application in Multiphysics Parameter Optimization

The review focuses on architected acoustic metamaterials to manipulate airborne sound waves, with only limited discussions on elastic metamaterials related to solid media. We review the design of acoustic metamaterials and the physical mechanisms underpinning their performance and related manufacturing methodologies, while also examining potential issues and challenges affecting the use of metamaterials in acoustics. The complexities of several metamaterial architectures are discussed. A new classification system is proposed to distinguish metamaterial configurations based on the typology of the channels inside the acoustic meta-atom. Several types of acoustic metamaterials architectures, such as perforated and micro-perforated panels, acoustic foams, resonators, various geometrical paths, and piezoelectric patches, are also discussed. The fundamental acoustic mechanisms of these classes of metamaterials are identified and commented on. The paper also describes the main measurement techniques used for acoustic metamaterials and the physical quantities evaluated, providing a guide to characterize and assess their performance. The fundamental challenges of the current metamaterials designs are discussed, with a focus on the complex synergy between architectural patterns of acoustic metamaterials and their thickness. We clarify the distinction between acoustic and elastic metamaterials, emphasizing the design and applications of materials that manipulate sound waves in fluid media. The paper also offers further comments about the need for practical design tools to allow the use of acoustic metamaterials in real-world applications.

Design of low-frequency and broadband acoustic metamaterials with I-shaped antichiral units

This thesis deals with electromagnetic inspired acoustic metamaterials, enabling sound-matter interactions in different wave scenarios that include propagation, guided-waves, radiation, refraction, reflection and transmission. To this end, a particular emphasis is placed on introducing novel applications for acoustic metamaterials operating on each one of the aforementioned wave scenarios. A few years ago, metamaterials have been introduced as a new class of composite artificial materials, engineered to produce unusual effective material properties not readily available in nature. Electromagnetic metamaterials are probably the oldest class of metamaterials, being nowadays in the process of reaching maturity and being proposed for interesting commercial applications. On the other hand, as in most young but not yet mature emerging fields of science, acoustic metamaterials are still providing lots of fertile and unexplored ground for research and study. Despite many inherent physical differences, the propagation of electromagnetic and acoustic waves are both governed by a similar mathematical model, the so-called Helmholtz wave equation. The main purpose of this work is to leverage this amazing similarity by translating recent advances in electromagnetic metamaterials into their corresponding, previously unseen, applications in acoustics. The first contribution of this thesis is to adapt the classic electromagnetic transmission-line theory to allow the design of acoustic metamaterials. The proposed circuit-based theory finds a direct application in the design of composite right/left hand transmission-line metamaterials, which yield novel guided-wave applications for acoustic metamaterials. Then, the developed theory is leveraged to model and achieve optimal design of different acoustic metamaterial-based devices, such as leaky-wave antennas and reflector-type metasurfaces. The second part of the thesis spins around acoustic leaky-wave antennas and their different functionalities, showing that they are able to act as acoustic dispersive prisms in the refraction mode and as acoustic single sensor direction finder in the radiation mode. Studying reflection phenomena in sound-metasurface interactions constitutes the next part of this thesis, where a membrane-capped cavity is introduced performing as an ultra-thin unit-cell for reflective acoustic metasurfaces. This leads to exciting applications of the concept, like acoustic reflectarray antennas and acoustic metasurface skin-cloaks Finally, the last part of this thesis deals with transmission phenomena in acoustic metasurfaces and, especially, in orbital angular momentum metasurfaces. This concept results in the design of an innovative labyrinthine-type helicoidal unit-cell that is used for phase coding a surface and transforming impinging acoustic wavefronts into transmitted helical wavefronts.

This comprehensive review explores the design and applications of machine learning (ML) techniques to acoustic metamaterials (AMs) and phononic crystals (PnCs), with a particular focus on deep learning (DL). AMs and PnCs, characterized by artificially designed microstructures and geometries, offer unique acoustic properties for precise control and manipulation of sound waves. ML, including DL, in combination with traditional artificial design have promoted the design process, enabling data-driven approaches for feature identification, design optimization, and intelligent parameter search. ML algorithms process extensive AM data to discover novel structures and properties, enhancing overall acoustic performance. This review presents an in-depth exploration of applications associated with ML techniques in AMs and PnCs, highlighting specific advantages, challenges and potential solutions of applying of using ML algorithms associated with ML techniques. By bridging acoustic engineering and ML, this review paves the way for future breakthroughs in acoustic research and engineering.

Acoustic metamaterials (AMs) have attracted a substantial amount of attention in recent decades where the parameter design plays an important role. However, conventional design methods generally rely on analytical physical models and require a very large number of evaluations of acoustic performance. Here, we propose and experimentally demonstrate an inverse-design method for AMs based on machine learning using a Gauss–Bayesian model. As a result of the cycle of training and prediction and the use of adaptive acquisition functions, this method allows the parameters of AMs to be efficiently designed for specific functionalities without the need for physical models. Considering the significance of low-frequency ventilated sound absorption, we present a design for a typical acoustic metamaterial absorber with multiple structural parameters that facilitate high sound absorption at low frequencies. In the design process, the parameters were adaptively adjusted to improve the sound absorption performance at low frequencies using only 37 evaluations, and this high absorption performance was verified by the agreement of numerical and experimental results. Because of its low cost, high flexibility, and independence from physical models, this method paves the way for tremendous opportunities in the design of various AMs for particular desired functionalities.

In rods and beams, piezoelectric patches with external circuits have been extensively studied to dampen structural vibrations at the sound source. This work uses a large flat-layer type of piezoelectric acoustic metamaterial (AM) model for noise attenuation in the sound transmission stage rather than at the sound source. This could be directly implemented as a large space sound transmission barrier at the interface of diverse media including gas, liquid, and solid materials. The general analytical model to derive acoustic properties of the piezoelectric acoustic metamaterial is established by the equivalent transfer matrix approach for normal incidence waves. This has been validated numerically using the finite element method and experimentally in the solid medium propagation for the first time. Moreover, a parametric study of sound transmission properties in air, water, and steel is conducted by adjusting external circuit parameters, flat-layer structures, and piezoelectric materials. The findings demonstrate the ability to control sound resonance frequency and bandwidth through the propagation process over a wide range using external circuit parameters alone. This endows the piezoelectric acoustic metamaterial with great versatility relative to conventional, fixed structure, metamaterials used as sound barriers at the interface of diverse media. In addition, the size of the piezoelectric AM can reach a deeply sub-wavelength level, less than 10−3 of the resonance wavelength in both water and solid media, which goes far beyond the wavelength-to-thickness ratio of classical acoustic metamaterials. The layer-type piezoelectric AM thus shows excellent performance in tunability and compactness for space-sensitive applications as well as great potential in combining metamaterials with electronic control.

Microfluidics has achieved a paradigm-shifting advancement in life sciences, automation, thermal management, and various other engineering streams. In recent years, a considerable amount of research has been conducted on the use of microfluidics in designing novel systems and fabricating next-generation smart materials that are capable of outperforming historical barriers and achieving unprecedented qualities. One such innovative development is the integration of fluidics into building artificially structured smart materials called acoustic metamaterials to achieve active tunability for a real-time controllable manipulation of acoustic waves. Leveraging the capability of microfluidics to automate the manipulation of liquid droplets, fluid streams, or bubbles in a required arrangement has revolutionised the development of actively tunable fluidics-integrated acoustic metamaterials for widescale applications. This review first discusses the prominent microfluidic actuation mechanisms used in the literature to develop fluidics-integrated smart acoustic metamaterials, and then it details integrated metamaterial design and extraordinary applications such as active acoustic wave manipulation or building tunable acoustic holograms etc. The following review concludes by providing the importance and future perspective of integrating microfluidic techniques with novel metamaterial designs, paving the way for innovative futuristic applications.

Widening, transition and coalescence of local resonance band gaps in multi-resonator acoustic metamaterials: From unit cells to finite chains

Objective: The objective of this work is to investigate the mapping relationship between transcranial ultrasound image quality and transcranial acoustic metamaterial parameters using inverse design methods. Impact Statement: Our study provides insights into inverse design methods and opens the route to guide the preparation of transcranial acoustic metamaterials. Introduction: The development of acoustic metamaterials has enabled the exploration of cranial ultrasound, and it has been found that the influence of the skull distortion layer on acoustic waves can be effectively eliminated by adjusting the parameters of the acoustic metamaterial. However, the interaction mechanism between transcranial ultrasound images and transcranial acoustic metamaterial parameters is unknown. Methods: In this study, 1,456 transcranial ultrasound image datasets were used to explore the mapping relationship between the quality of transcranial ultrasound images and the parameters of transcranial acoustic metamaterials. Results: The multioutput parameter prediction model of transcranial metamaterials based on deep back-propagation neural network was built, and metamaterial parameters under transcranial image evaluation indices are predicted using the prediction model. Conclusion: This inverse big data design approach paves the way for guiding the preparation of transcranial metamaterials.

This paper presents a type of acoustic metamaterial that combines a labyrinth channel with a Helmholtz cavity and a thin film. The labyrinth-opening design and thin-film combination contribute to the metamaterial's exceptional sound-insulation performance. After comprehensive research, it is observed that in the frequency range of 20-1200 Hz, this acoustic metamaterial exhibits multiple sound-insulation peaks, showing a high overall sound-insulation quality. Specifically, the first sound-insulation peak is 26.3 Hz, with a bandwidth of 13 Hz and giving a transmission loss of 56.5 dB, showing excellent low-frequency sound-insulation performance. To further understand the low-frequency sound-insulation mechanism, this paper uses the equivalent model method to conduct an acoustic-electrical analogy, construct an equivalent model of the acoustic metamaterial, and delve into the sound-insulation mechanism at the first sound-insulation peak. To confirm the validity of the theoretical calculations, physical experiments are carried out by 3D printing experimental samples. The analysis of the experimental data has yielded results that are consistent with the simulation data, providing empirical evidence for the accuracy of the theoretical model. The material has significant practical application value. Finally, various factors are studied in depth based on the established equivalent model, which can provide valuable insights for the design and practical engineering application of acoustic metamaterials.

Symmetric metamaterials based on flower-shaped structure

This talk presents our findings and methodologies in applying reinforcement learning to the design of acoustic metamaterials [1]. Our reinforcement learning agents are capable of discovering configurations of cylindrical scatterers in water which minimize the scattering of an acoustic plane wave. These agents are successful in the optimization of two different parametric designs, i.e., radii and positions of each scatterer. The resultant designs produced by reinforcement learning algorithms such as double deep Q-learning network and deep deterministic policy gradient algorithms are comparable and in some cases superior to those produced by the gradient-based optimization solver such as fmincon. However, significant computational resources are required to train reinforcement learning models to completion. This poses a significant challenge when attempting to increase the complexity of a design; as training times increase to unfeasible levels. Methods to counteract this challenge are discussed in this talk. These methods include utilization of the Julia programming language as well as multiprocessing and multithreaded programming. Together they create a synergy which reduces training times by orders of magnitude. [1] T. Shah, L. Zhuo, P. Lai, A. Rosa-Moreno, F. Amirkulova, and P. Gerstoft, “Reinforcement learning applied to metamaterial design,” J. Acoust. Soc. Am. 150(1), 321–338 (2021).

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.

On-demand tunable metamaterials design for noise attenuation with machine learning

Impedance theory of wave propagation on infinite periodic structures