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: reduced-order models (ROMs) and input-output models derived from high-fidelity Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter
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the final morphology of the self-assembled interfacial structure. However, precise and quantitative characterization of such self-assembly dynamics has thus far proven to be a challenge due to the large
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for determining the final morphology of the self-assembled interfacial structure. However, precise and quantitative characterization of such self-assembly dynamics has thus far proven to be a challenge due to the
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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
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transitions, flow dynamics, stratification) inside thermal storages containing PCMs? How can we describe via validated multi-physics simulation models; heat transfer, flow behavior, and phase changes? Your task
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Vacancies PhD position on the design and fabrication of MEMS drag force-based flow and fluid composition sensors Key takeaways In this project, we will combine well-known thermal flow sensing
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and Innovatie (TKI) project in a public-private partnership (PPP) scheme in collaboration with DENS solutions company. Femto-Cryo will use 3D printed fluid force microscopy (FluidFM) cantilevers from TU