The effect of Y2O3 nanoparticle addition on the superconducting properties of Bi1.6Pb0.4Sr 2CaCu 2Oy have been investigated. The samples were prepared using high purity oxide powders via solid state reaction method. Y2O3 nanoparticle with 0.0-1.0 wt. % was systematically added to the well balanced Bi1.6Pb0.4 Sr2CaCu2Oy before sinter in order to trace the existense of nanoparticle addition in the system. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and critical current density, Jc. The current density measurement was done via four-point probe method under zero magnetic fields. The critical current density, Jc and superconductivity transition temperature, Tc for sample with addition of Y2O3 nanoparticle were found to be higher than the pure sample. The optimal addition of Y2O3 nanoparticle to the sample Bi-2212 system was found at 0.7 wt. %. The crystallographic structure of all samples was evidenced to be orthorhombic where a ≠ b ≠ c. Changes in superconducting properties of Y2O3 nanoparticle added Bi-2212 system were discussed.
The samples with nominal composition of Bi1.6Pb0.4Sr2Ca2-xDyxCu3Oywhere x = 0.000, 0.025, 0.050, 0.100 and 0.200 were prepared by the co-precipitation (COP) method. The samples were characterized by x-ray diffraction, electrical resistivity measurement and critical current density. The critical current density (JC) and superconductivity transition temperature (TC) of Dy substituted were found to be lower than the Dy-free sample. The TCvalues vary between 100 K and 75 K toward Dy concentration due to a small change of carrier concentration. The highest TC in Dy-doped sample was found at 96 K in x = 0.025. The JCdecreased towards Dy substitution, and it was measured to be 5751.2 mA/cm2 in Dy-free and 3769.8 mA/cm2in x = 0.025 at 77 K. XRD analysis showed the substitutions of Dy reduced the volume fraction of the 2223 phase and increased the volume fraction of the 2212 phase. The proportion of Bi-2223/Bi-2212 (%) were estimated from 76.74/23.26 in Dy free to 18.90/81.10 in x = 0.200.
Abstract-The conventional stress wave signal interpretation in heat exchanger tube inspection is human dependent. The difficulties associated with accurate defect interpretations are skills and experiences of the inspector. Hence, in present study, alternative pattern recognition approach was proposed to interpret the presence of defect in carbon steel heat exchanger tubes SA179. Several high frequency stress wave signals propagated in the tubes due to impact are captured using Acoustic Emission method. In particular, one reference tube and two defective tubes were adopted. The signals were then clustered using the feature extraction algorithms. This paper tested two feature extraction algorithms namely Principal Component Analysis (PCA) and Auto-Regressive (AR). The pattern recognition results showed that the AR algorithm is more effective in defect identification. Good comparisons with the commonly global statistical analysis demonstrate the effective application of the present approach for defect detection.
IndexTerms-Auto-regressive, pattern recognition, principal component analysis, stress wave.
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