Our colloquium series offers a diverse platform for research scholars, faculty, students, and industry experts to share and exchange ideas, fostering discussion and networking across mathematics, statistics, and data science.
Dr. David Guinovart from University of Minnesota will speak at this week's colloquium on "Controlling Surface Waves with Laminated Metabarriers: Theory and Dynamic Homogenization."
Abstract: We investigate the interaction between Love waves and laminated metabarriers embedded in an elastic half-space. Metabarriers, defined as periodic arrays of resonant structures, enable sub-wavelength control of surface wave propagation, yet remain less explored than metasurfaces. The configuration considered consists of periodically arranged multilayered laminates, whose geometric parameters, such as layer height, volume fraction, and unit-cell size, provide tunable control over wave behavior.
To analyze the system, we employ a non-classical dynamic asymptotic homogenization framework that replaces the discrete laminated structure with an effective dispersive multilayer. This approach allows the derivation of closed-form expressions for the dispersion relation, displacement fields, and time-averaged kinetic energy. The analytical model predicts the emergence of cut-off frequencies and hybrid dispersion regions within the first Brillouin zone. In single-layer metabarriers, one hybrid region is obtained, while bilayer configurations produce multiple cut-off frequencies and band gaps due to stronger mode coupling.
Finite element simulations of the discrete unit cell validate the homogenized model in the low-frequency regime and confirm wave propagation within pass bands and attenuation inside band gaps. The results provide insight into Love-wave hybridization mechanisms and demonstrate the potential of laminated metabarriers for tunable wave control and energy harvesting.
Speaker Bio: David Guinovart is an Assistant Professor at The Hormel Institute, University of Minnesota, where he leads the Mathematical Modeling Lab and serves as a Bioinformatics Advisor for research teams across the institute. A proud UCF alumnus, he earned his Ph.D. in Applied Mathematics at the University of Central Florida, where he developed a love for using math to tackle real scientific problems. His work sits at the intersection of applied mathematics, computation, and biology, with two main tracks: cancer research and material science. In cancer, he builds models and machine learning tools that help make sense of complex biomedical data and support efforts in prediction and biomarker discovery. In material science, he develops mathematical and numerical models to understand multiscale behavior and effective properties of complex structures. Through collaboration and advising, he enjoys helping researchers turn ideas and data into clear, actionable results.
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