Thermo-chemical Characteristics of Powder Fabricated by Oxidation of Spent PWR Fuel
Thermochemcial characteristics of powder fabricated using oxidation treatment of spent PWR fuel and SIMFUEL were evaluated for recycling of spent fuel such as DUPIC process. Especially, the influence of spent fuel burn-ups on the powder fabrication characteristics was experimentally evaluated, ranging from 27,300 to 65,000 MWd/tU. Densities of powder manufactured from an oxidation, OREOX and the milling processes at the same process conditions were compared as a function of the fuel burn-ups respectively. Also, based on chemical analysis results, homogeneity of fissile elements in oxidized powder was confirmed. Keywords—Spent PWR fuel, DUPIC, Oxidation, OREOX, Powder, Chemical analysis
Phase characterisation of simulated high burn-up UO2 fuel
Abstract To determine the lattice constant variation with burn-up and the oxidation behaviour at high temperatures, simulated UO2-spent fuels with burn-ups up to 200 GWd tM−1 were prepared by cold pressing and sintering and characterised by X-ray diffraction analysis and thermogravimetry. The fcc-lattice parameter of the sintered samples determined at room temperature, showed a continuous decrease with the simulated burn-up. During oxidation the conversion of the simulated fuels to the (U,FP)4O9 and (U,FP)3O8-type phases was strongly delayed when the simulated burn-up exceeded 80 GWd tM−1. Also, at high burn-ups (>100 GWd tM−1), large fractions of the (U,FP)4O9-type phase remained untransformed to (U,FP)3O8 after prolonged exposure to air at 950–1000°C. This suggests a marked widening of the (U,FP)4O9-phase field when the amount of substituted uranium atoms exceeds values of about 8%.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.