Models of the Temperature Dependence and Scatter in J-R and J0.1 for Ferritic Reactor Pressure Vessel Steels

At the 2014 ASME Pressure Vessel and Piping Conference, these authors and others presented a paper that drew together a number of models describing the fracture toughness of ferritic reactor pressure vessel (RPV) steels. That paper summarized models of both the temperature dependence and scatter in a number of fracture toughness metrics (i.e., KJc, KIa, JIc, and J0.1). That paper also provided equations that quantify the interrelationships between these toughness metrics, and how these interrelationships are affected by hardening. Significantly, all of these models and interrelationships are linked via a single parameter: the Master Curve index temperature, To, which can be measured as described in ASTM Standard Test Method E1921. Work is currently underway within the ASME Section XI Working Group on Flaw Evaluation (WGFE) to develop a revision to Code Case N-830 that incorporates all of these models, and provides information on how to apply them in a flaw evaluation. As part of that work, an effort was initiated to augment these models by the addition of a model that can be used to predict the temperature variation of, and the scatter in, J-R curve behavior. A J-R curve model is also expected to support on-going WGFE efforts to in development of acceptance criteria for flaws in ferritic components operating in the upper shelf temperature range. The work presented in this paper provides a model of the J-R behavior of ferritic RPV steels. When combined with other fracture toughness models to be published in Code Case N-830-1, this model allows prediction of the mean J-R curve, confidence bounds on the mean, and the temperature dependence of J-R all based only on input of To. The J-R model described herein has equivalent or better accuracy to other models described in the literature, and generally has fewer fitting parameters than those other models. Because the full J-R curve is predicted, this model is also useful for prediction of J0.1.

A Forward Moving Method for Continuous Nearest Neighbor Queries

A continuous nearest neighbor (CNN) query retrieves the nearest neighbor of every point on a line segment and indicates its valid segments. Zheng et al. have proposed a Hilbert-curve index for the CNN query. This method contains two phases, searching candidates in the approximate search range, and filtering the candidates to get the final answer. However, it may determine a wide search range in the first phase based on this method, resulting in the decrease of the accuracy and the increase of the processing time. Therefore, in this paper, to avoid this disadvantage, we propose a forward moving method to efficiently support the CNN queries. The proposed method locally expands the search range along the query line segment to find the neighbors. Experimental results show that our method outperforms Zheng et al.’s method in terms of the accuracy and the processing time.

Atmospheric Quality Assessment Model Based on Immune Clonal Selection Algorithm

The immune clonal selection algorithm is used to optimize the parameters in the formula of S growth curve index in the paper, thus we can obtain an assessment model for atmospheric comprehensive pollution that is suitable to the cases of multi-pollutants. Moreover the proposed assessment model is applied in the field of atmosphere assessment. Experimental results show that the assessment method proposed for atmosphere quality has many advantages such as pellucid principle, physical explication and correct assessment results etc. It is a new effective approach for intelligence theory and technology applied in the field of environment. Therefore it has great potential in the field of assessment the atmospheric quality.