Convolutional autoencoder for on-demand parametric inverse design of local resonator geometry in wind turbine metastructure targeting vibration control
Vibration control in structures is essential to mitigate undesired dynamic responses, thereby enhancing stability, safety, and performance under varying loading conditions. Mechanical metamaterials have emerged as effective solutions, enabling tailored dynamic properties for vibration attenuation. T...
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| Published in: | AI EDAM Vol. 39 |
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| Abstract | Vibration control in structures is essential to mitigate undesired dynamic responses, thereby enhancing stability, safety, and performance under varying loading conditions. Mechanical metamaterials have emerged as effective solutions, enabling tailored dynamic properties for vibration attenuation. This study introduces a convolutional autoencoder framework for the inverse design of local resonators embedded in mechanical metamaterials. The model learns from the dynamic behaviour of primary structures coupled with ideal absorbers to predict the geometric parameters of resonators that achieve desired vibration control performance. Unlike conventional approaches requiring full numerical models, the proposed method operates as a data-driven tool, where the target frequency to be mitigated is provided as input, and the model directly outputs the resonator geometry. A large dataset, generated through physics-informed simulations of ideal absorber dynamics, supports training while incorporating both spectral and geometric variability. Within the architecture, the encoder maps input receptance spectra to resonator geometries, while the decoder reconstructs the target receptance response, ensuring dynamic consistency. Once trained, the framework predicts resonator configurations that satisfy predefined frequency targets with high accuracy, enabling efficient design of passive controllers of the syntonized mass type. This study specifically demonstrates the application of the methodology to resonators embedded in wind turbine metastructures, a critical context for mitigating structural vibrations and improving operational efficiency. Results confirm strong agreement between predicted and target responses, underscoring the potential of deep learning techniques to support on-demand inverse design of mechanical metamaterials for smart vibration control in wind energy and related engineering applications. |
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| AbstractList | Vibration control in structures is essential to mitigate undesired dynamic responses, thereby enhancing stability, safety, and performance under varying loading conditions. Mechanical metamaterials have emerged as effective solutions, enabling tailored dynamic properties for vibration attenuation. This study introduces a convolutional autoencoder framework for the inverse design of local resonators embedded in mechanical metamaterials. The model learns from the dynamic behaviour of primary structures coupled with ideal absorbers to predict the geometric parameters of resonators that achieve desired vibration control performance. Unlike conventional approaches requiring full numerical models, the proposed method operates as a data-driven tool, where the target frequency to be mitigated is provided as input, and the model directly outputs the resonator geometry. A large dataset, generated through physics-informed simulations of ideal absorber dynamics, supports training while incorporating both spectral and geometric variability. Within the architecture, the encoder maps input receptance spectra to resonator geometries, while the decoder reconstructs the target receptance response, ensuring dynamic consistency. Once trained, the framework predicts resonator configurations that satisfy predefined frequency targets with high accuracy, enabling efficient design of passive controllers of the syntonized mass type. This study specifically demonstrates the application of the methodology to resonators embedded in wind turbine metastructures, a critical context for mitigating structural vibrations and improving operational efficiency. Results confirm strong agreement between predicted and target responses, underscoring the potential of deep learning techniques to support on-demand inverse design of mechanical metamaterials for smart vibration control in wind energy and related engineering applications. |
| Author | Dutkiewicz, Maciej Machado, Marcela Naeini, Mohammadreza Sahaf |
| Author_xml | – sequence: 1 givenname: Mohammadreza Sahaf surname: Naeini fullname: Naeini, Mohammadreza Sahaf organization: Faculty of Civil, Environmental Engineering and Architecture, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland – sequence: 2 givenname: Marcela orcidid: 0000-0002-7488-7201 surname: Machado fullname: Machado, Marcela email: marcelam@unb.br organization: Faculty of Civil, Environmental Engineering and Architecture, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland – sequence: 3 givenname: Maciej surname: Dutkiewicz fullname: Dutkiewicz, Maciej organization: Faculty of Civil, Environmental Engineering and Architecture, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland |
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| Keywords | deep learning parameter-based inverse design supervised autoencoder latent space exploration sensitivity optimization method |
| Language | English |
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| References_xml | – volume: 129 year: 2021 article-title: Inverse design of a helmholtz resonator based low-frequency acoustic absorber using deep neural network publication-title: Journal of Applied Physics – year: 2018 article-title: Tunable fluid-solid metamaterials for manipulation of elastic wave propagation in broad frequency range publication-title: Applied Physics Letters – volume: 13 start-page: 1780 year: 2025 end-page: 1801 article-title: Enhancing vibration attenuation in offshore wind turbine with multiphysics mechanical metamaterial publication-title: Energy Reports – year: 2022 article-title: Acoustic metamaterial design framework using deep learning and generative modeling publication-title: The Journal of the Acoustical Society of America – volume: 22 issue: 05 year: 2022 article-title: Spectral model of offshore wind turbines and vibration control by pendulum tuned mass dampers publication-title: International Journal of Strucutral Stability and Dynamics – volume: 30 start-page: 807 year: 2024 end-page: 821 article-title: Vibration transmission characteristic prediction and structure inverse design of acoustic metamaterial beams based on deep learning publication-title: JVC/Journal of Vibration and Control – volume: 66 start-page: 365 issue: 6 year: 2022 end-page: 393 article-title: Artificial intelligence-enabled smart mechanical metamaterials: Advent and future trends publication-title: International Materials Reviews – volume: 120 start-page: 4963648 issue: 13 year: 2016 article-title: On the mechanism of bandgap formation in locally resonant finite elastic metamaterials publication-title: Journal of Applied Physics – year: 2009 article-title: Definition of a 5-MW reference wind turbine for offshore system development [electronic resource] publication-title: National Renewable Energy Laboratory – volume: 125 start-page: 650 year: 2024 end-page: 671 article-title: Manipulating flexural waves to enhance the broadband vibration mitigation through inducing programmed disorder on smart rainbow metamaterials publication-title: Applied Mathematical Modelling – volume: 180 start-page: 109383 year: 2022 article-title: On the formation of a super stop-band in finite mono-coupled periodic structures using an array of vibration absorbers: Controlling parameters and physical insight publication-title: Mechanical Systems and Signal Processing – volume: 11 start-page: 439 year: 2022 end-page: 460 article-title: Intelligent on-demand design of phononic metamaterials publication-title: Nano – volume: 510 start-page: 116280 year: 2021 article-title: Harnessing bulging or sloshing modes to design locally resonant liquid-solid metamaterials publication-title: Journal of Sound and Vibration – volume: 155 start-page: 100745 year: 2023 article-title: Programmable multi-physical mechanics of mechanical metamaterials, materials science and engineering: R publication-title: Report – volume: 46 start-page: 1806 issue: 190 year: 2024 end-page: 3691 article-title: Enhancing broadband vibration suppression of a cable conductor using graded metamaterials publication-title: Journal of the Brazilian Society of Mechanical Sciences and Engineering – volume: 25 start-page: 2300251 issue: 17 year: 2023 article-title: Tensile properties of 3d-projected 4-polytopes: A new class of mechanical metamaterial publication-title: Advanced Engineering Materials – volume: 191 start-page: 108680 year: 2022 article-title: Deep auto-encoder network in predictive design of helmholtz resonator: On-demand prediction of sound absorption peak publication-title: Applied Acoustics – volume: 55 start-page: 101827 year: 2022 article-title: A physics-guided machine learning for multifunctional wave control in active metabeams publication-title: Extreme Mechanics Letters – volume: 30 issue: 8 year: 2021 article-title: Ultrathin acoustic absorbing metasurface based on deep learning approach publication-title: Smart Materials and Structures – volume: 11 start-page: 9749 issue: 19 year: 2019 end-page: 9755 article-title: Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network publication-title: Nanoscale – volume: 488 start-page: 115647 year: 2020 article-title: Multiple bandgap formation in a locally resonant linear metamaterial beam: Theory and experiments publication-title: Journal of Sound and Vibration – volume: 57 start-page: 1780 year: 2016 end-page: 1801 article-title: Homogenization of locally resonant acoustic metamaterials towards an emergent enriched continuum publication-title: Computational Mechanics – volume: 79 start-page: 1 year: 2012 end-page: 17 article-title: Wave motion in periodic flexural beams and characterization of the transition between bragg scattering and local resonance publication-title: Journal of Applied Mechanics, Transactions ASME – volume: 126 start-page: 6326 year: 2018 end-page: 6334 article-title: Deep-learning-enabled on-demand design of chiral metamaterials publication-title: ACS Nano – volume: 11 start-page: 2709 issue: 6 year: 2023 end-page: 2731 article-title: Multiclass supervised machine learning algorithms applied to damage and assessment using beam dynamic response publication-title: Journal of Vibration Engineering and Technologies – volume: 88 year: 2024 article-title: Knowledge graph based opc ua information model automatic construction method for heterogeneous devices integration publication-title: Robotics and Computer-Integrated Manufacturing – volume: 72 start-page: 106563 year: 2023 article-title: A fast-response-generation method for single-layer reticulated shells based on implicit parameter model of generative adversarial networks publication-title: Journal of Building Engineering – volume: 22 start-page: 1341 issue: 9 year: 2011 end-page: 1356 article-title: Comprehensive review of neural network-based prediction intervals and new advances publication-title: IEEE Transactions on Neural Networks – volume: 20 issue: 6 year: 2023 article-title: Research of the numerical simulation and machine learning backpropagation networks analysis of the sound absorption properties of cellular soundproofing materials publication-title: Results in Engineering – volume: 9 start-page: 1041 year: 2020 end-page: 1057 article-title: Deep learning enabled inverse design in nanophotonics publication-title: Nano – volume: 15 year: 2020 article-title: Prediction network of metamaterial with split ring resonator based on deep learning publication-title: Nanoscale Research Letters – volume: 223 start-page: 120056 year: 2024 article-title: Metamaterial-based vibration control for offshore wind turbines operating under multiple hazard excitation forces publication-title: Renewable Energy – volume: 53 issue: 27 year: 2020 article-title: Metasurface inverse design using machine learning approaches publication-title: Journal of Physics D: Applied Physics – volume: 101 year: 2020 article-title: Inverse design of broadband highly reflective metasurfaces using neural networks publication-title: Physical Review B – volume: 126 start-page: 106998 year: 2023 article-title: Deep reinforcement learning for the rapid on-demand design of mechanical metamaterials with targeted nonlinear deformation responses publication-title: Engineering Applications of Artificial Intelligence – volume: 97 start-page: 70 year: 2019 end-page: 78 article-title: Spectral formulated modelling of an electrodynamic shaker publication-title: Mechanics Research Communications – volume: 88 start-page: 85 year: 2019 end-page: 105 article-title: Inverse metamaterial design for controlling band gaps in scalar wave problems publication-title: Wave Motion – volume: 133 start-page: 108595 year: 2024 article-title: Beyond the limits of parametric design: Latent space exploration strategy enabling ultra-broadband acoustic metamaterials publication-title: Engineering Applications of Artificial Intelligence – volume: 112 issue: 24 year: 2018 article-title: Neural network based design of metagratings publication-title: Applied Physics Letters – volume: 11 year: 2021 article-title: Metamaterial reverse multiple prediction method based on deep learning publication-title: Nanomaterials – volume: 8 start-page: 211849 year: 2020 end-page: 211859 article-title: Customized inverse design of metamaterial absorber based on target-driven deep learning method publication-title: IEEE Access – volume: 95 start-page: 106 issue: 666 year: 1918 end-page: 115 article-title: The periods of lateral vibration of loaded shafts.—The rational derivation of dunkerley’s empirical rule for determining whirling speeds publication-title: Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character – volume: 262 year: 2025 article-title: Industrial robot energy consumption model identification: A coupling model-driven and data-driven paradigm publication-title: Expert Systems with Applications – volume: 11 start-page: 18629 year: 2021 article-title: Leveraging long short-term memory (LSTM)-based neural networks for modeling structure–property relationships of metamaterials from electromagnetic responses publication-title: Scientific Reports – volume: 4 year: 2024 article-title: Machine intelligence in metamaterials design: A review publication-title: Oxford Open Materials Science – volume: 71 start-page: 291 issue: 3 year: 2019b end-page: 303 article-title: Spectral element method in the analysis of vibrations of overhead transmission line in damping environment publication-title: Structural Engineering and Mechanics – volume: 338 start-page: 118087 year: 2024 article-title: Deep learning uncertainty quantification for ultrasonic damage identification in composite structures publication-title: Composite Structures – volume: 40 start-page: 279 year: 2024 end-page: 300 article-title: A deep autoencoder-based approach for the inverse design of an acoustic-absorber publication-title: Engineering with Computers – volume: 81 start-page: 23 issue: 1 year: 2003 end-page: 69 article-title: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems publication-title: Reliability Engineering & System Safety – volume: 361 year: 2020 article-title: Designing phononic crystal with anticipated band gap through a deep learning based data-driven method publication-title: Computer Methods in Applied Mechanics and Engineering – volume: 4 issue: 6 year: 2018 article-title: Nanophotonic particle simulation and inverse design using artificial neural networks publication-title: Science. The Advocate – volume: 10 start-page: 602 year: 2023 end-page: 614 article-title: Yu, deep learning for the design of phononic crystals and elastic metamaterials publication-title: Journal of Computational Design and Engineering – volume: 23 year: 2024 article-title: Insights into modern machine learning approaches for bearing fault classification: A systematic literature review publication-title: Results in Engineering – volume: 70 start-page: 137 year: 2023 end-page: 148 article-title: Energy consumption prediction and optimization of industrial robots based on LSTM publication-title: Journal of Manufacturing Systems – volume: 196 start-page: 105073 year: 2024 article-title: Homogenization and continuum limit of mechanical metamaterials publication-title: Mechanics of Materials – volume: 250 start-page: 111702 year: 2022 article-title: Inverse metamaterial design combining genetic algorithms with asymptotic homogenization schemes publication-title: International Journal of Solids and Structures – volume: 123 start-page: 106413 year: 2023 article-title: A hybrid deep learning approach for the design of 2d low porosity auxetic metamaterials publication-title: Engineering Applications of Artificial Intelligence – volume: 38 start-page: e16 year: 2024 article-title: A semi-supervised anomaly detection approach for detecting mechanical failures, artificial intelligence for engineering design publication-title: Analysis and Manufacturing – volume: 29 start-page: 3237 issue: 22 year: 2022 end-page: 3262 article-title: A review on application of mechanical metamaterials for vibration control publication-title: Mechanics of Advanced Materials and Structures – year: 2022 article-title: Maximized frequency doubling through the inverse design of nonlinear metamaterials publication-title: ACS Nano – volume: 38 year: 2024 article-title: A novel intelligent fault diagnosis method of bearing based on multi-head self-attention convolutional neural network publication-title: Artificial Intelligence for Engineering Design, Analysis and Manufacturing – volume: 66 start-page: 1 issue: 4 year: 2014 end-page: 38 article-title: Dynamics of phononic materials and structures: Historical origins, recent progress, and future outlook publication-title: Applied Mechanics Reviews – volume: 8 start-page: 1213 year: 2020 end-page: 1225 article-title: Multitask deep-learning-based design of chiral plasmonic metamaterials publication-title: Photonics Research – volume: 63 start-page: 2399 year: 2021 end-page: 2423 article-title: Design of one-dimensional acoustic metamaterials using machine learning and cell concatenation publication-title: Structural and Multidisciplinary Optimization – volume: 3 year: 2023 article-title: Machine learning assisted intelligent design of meta structures: A review publication-title: Microstructures – volume: 48 start-page: 101372 year: 2021 article-title: Machine-learning based design of digital materials for elastic wave control publication-title: Extreme Mechanics Letters – volume: 62 issue: B year: 2024 article-title: A blockchain-empowered secure federated domain generalization framework for machinery fault diagnosis publication-title: Advanced Engineering Informatics – volume: 36 start-page: 306 year: 2023 end-page: 316 article-title: Developing mechanical metamaterials under an adaptable topology optimization design framework publication-title: Acta Mechanica Solida Sinica – volume: 236 start-page: 2171 issue: 11 year: 2022 end-page: 2210 article-title: Metamaterials and their applications: An overview, proceedings of the institution of mechanical engineers publication-title: Part L: Journal of Materials: Design and Applications – volume: 13 start-page: 531 issue: 4–5 year: 2006 end-page: 543 article-title: Some recent developments in adaptive tuned vibration absorbers/neutralisers publication-title: Shock and Vibration – volume: 56 year: 2022 article-title: Inverse design of a topological phononic beam with interface modes publication-title: Journal of Physics D: Applied Physics – volume: 11 start-page: 3756 year: 2024 end-page: 3791 article-title: Wind turbine vibration management: An integrated analysis of existing solutions, products, and open-source developments publication-title: Energy Reports – volume: 215 start-page: 110499 year: 2022 article-title: Deep-subwavelength lightweight metastructures for low-frequency vibration isolation publication-title: Materials & Design |
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