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This study investigates the potential of hyperbolic paraboloid (hypar) shapes for enhancing wave attenuation and structural efficiency in Free-Surface Breakwaters (FSBW). A decoupled approach combining Smoothed Particle Hydrodynamics (SPH) and Finite Element Method (FEM) is employed to analyze hypar-faced FSBW performance across varying hypar warping values and wave characteristics. SPH simulations, validated through experiments, determine wave attenuation performance and extract pressure values for subsequent FEM analysis. Results indicate that hypar-faced FSBW produces increased wave attenuation compared to traditional flat-faced designs, particularly for shorter wave periods and smaller drafts. Furthermore, hypar surfaces exhibit up to three times lower principal stresses under wave loading compared to the flat counterpart, potentially allowing for thinner surfaces. The study also shows that peak-load static stress values provide a reasonable approximation for preliminary design, with less than 6% average difference compared to dynamic analysis results. In summary, this research presents hypar-faced FSBW as a promising alternative in coastal defense strategies, offering effective wave attenuation and structural efficiency in the context of rising sea levels and increasing storm intensities. 

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.