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within fusion reactors, especially plasma-facing materials (PFMs) exposed to intense heat fluxes and energetic particles. Understanding and predicting how these materials degrade under such conditions is
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Magnesium Alumina and Silicates -CMAS-) can infiltrate these coatings and accelerate their degradation. Leveraging a fundamental understanding of material science, coatings technologies and advanced thermal
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and pressures; however, external contaminants (e.g. Calcium Magnesium Alumina and Silicates -CMAS-) can infiltrate these coatings and accelerate their degradation. Leveraging a fundamental understanding
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completion of this project will yield validated designs, novel manufacturing routes, and a deeper understanding of metamaterial-based radiation detectors, accelerating the development of advanced systems
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the development of specialized hardware architectures capable of efficient, real-time processing. Embedded AI hardware architectures, including neuromorphic processors and low-power AI accelerators
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-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate
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Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine Fault Simulator