Search

A suite of methods has been established to quantitatively estimate uncertainties existed in source term analysis during a nuclear reactor severe accident. The accident sequence occurred at Unit 2 of the Fukushima Daiichi nuclear power plant (NPP) is taken as an example in which it is numerically modeled via the integrated severe accident code MELCOR 1.8.5. This standardized approach mainly consists of four steps: screening analysis, random sampling, numerical computation and verification of uncertainty distributions. First, by using an individually randomized one-factor-at-a-time screening method, a group of variables are preliminarily determined as important uncertain variables. Second, appropriate probability distributions are assigned to all selected variables. Multiple sets of random samples are generated using Latin Hypercube sampling combined with the consideration of rank correlation among input variables. Third, random samples of all selected variables are inputted into MELCOR 1.8.5. Numerical simulation with multiple code runs is implemented. Finally, uncertainty distributions for representative source terms (barium cesium, cesium iodide and tellurium) are obtained and verified. The technique of Bayesian nonparametric density estimation is applied to obtain probability density functions of interested source terms. In order to obtain a reasonable uncertainty distribution, several rounds of Latin Hypercube sampling and computation are conducted. As an alternative method to Wilks sampling criteria, the difference of probability density functions is evaluated through the comparison based on the Kullback-Leibler (KL) divergence. With the subjective judgment of small enough KL divergence, after a certain number of numerical computations, the uncertainty distributions of representative source terms are considered as stable enough as reliable results.

Ghost cell odontogenic carcinoma (GCOC) is a rare malignant odontogenic tumor with aggressive growth characteristics. We report a case of GCOC of the maxilla in a 65-year-old Japanese man who was referred to our hospital with painful swelling of the left maxilla. Computed tomography showed a bone defect in the left upper jaw and the border with the surrounding tissue was indistinct in one area. Tumor resection was performed under general anesthesia, and the histopathological diagnosis was dentinogenic ghost cell tumor (DGCT). Ten months postoperatively, the tumor recurred in the same area, and partial resection of the maxilla was performed. Twelve months after reoperation, the tumor recurred a second time. The histopathological diagnosis this time was GCOC, as a secondary malignant manifestation of the benign DGCT. [ABSTRACT FROM AUTHOR]

Stress corrosion cracking (SCC) in Type 316 L low-carbon austenitic stainless steel recirculation pipes have been observed near butt-welding joints. These SCC in Type 316 L stainless steel grow near the welding zone mainly because of the high tensile residual stress caused by welding. Therefore, for SCC growth analysis, it is important to assess the residual stress caused by welding of stainless steel piping. In the present study, which is focused on the scatters of welding parameters such as heat input and welding speed, these values were measured experimentally by fabricating a series of butt-welded specimens of Type 316 pipes. The distribution and scatter of residual stress were also measured by stress relief and X-ray diffraction methods. The effects of welding parameters on residual stress distribution have been evaluated through welding simulations based on finite-element analysis using three-dimensional and axisymmetric models. Parametric analyses were also performed, while taking into account the variation of some welding parameters based on the experiments. SCC growth behavior was calculated using simulated residual stress distributions and applying a procedure in the fitness-for-service code. It was clearly shown that the uncertainties on welding heat input and speed have strong influences on SCC growth behavior because residual stress is also affected by the scatter of these welding parameters. [ABSTRACT FROM AUTHOR]