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    Study of Flux Pinning in Thick Film REBCO Coated Conductors Over a Wide Range of Magnetic Fields and Temperatures

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    KOCHAT-DISSERTATION-2020.pdf (5.560Mb)
    Date
    2020-05
    Author
    Kochat, Mehdi
    0000-0002-2928-0718
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    Abstract
    RE-Ba-Cu-O (REBCO, RE = rare earth) coated conductors are approaching the large-scale electric power and magnetic applications over a wide range of temperatures and magnetic fields owing to their high critical current density (Jc), their high critical temperature (Tc) and their strong irreversibility field (Hirr). However, further enhancement of the engineering critical current (Je) is required to make REBCO more cost-effective. Improving Je can be achieved by combining two strategies: achieving a thickness independent Jc and enhancing the flux pinning landscape in REBCO through the incorporation of artificial pinning centers (APC). In this work, are studied REBCO tapes deposited using an advanced metal organic vapor deposition process (A-MOCVD) allowing the growth of up to 5 µm thick high performing REBCO films without a deterioration in Jc. BaMO3 (M: Zr, Hf, and Nb) self-assembled nanorods with different concentrations up to 15 mol% were incorporated into REBCO to enhance their Jc over a wide range of temperature (4.2—77 K) and applied magnetic field (0—31 T). The effect of the density, size, and continuity of the BMOs were systematically studied revealing a stronger contribution of the continuous and dense nanorods to the Jc at high magnetic fields and low temperatures. Additionally, BaZrO3 doped REBCO were subject to post-deposition tensile-creep-deformation at 580°C leading to an increase in the density of ab-plane stacking faults which correlated with up to 3 times higher critical current than a reference sample at 77 K and 1 T when the magnetic field is parallel to the ab-plane. Finally, Artificial Neural Networks (ANN) were trained to accurately predict lower temperature critical currents out of the 65 K critical currents. The ANN predictions showed an average error of 2.8% at 4.2 K and 13 T when applied on a validation dataset containing 100 samples.
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    https://hdl.handle.net/10657/6563
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