TRISO-wrapped fuel particle failure is a function of local temperature. The spherical symmetry and hence central temperature peak in PBM-type reactor fuel pebbles can lead to considerably higher failure rates at the center of the pebbles. The so-called “wallpaper fuel” concept has been shown to be a viable solution to this issue. In this paper, the neutronic aspects of this modification were studied using MCNP. According to the obtained results, the inclusion of a central fuel-free zone could lead to a more reactive system with lower actinide production while damping the temperature response of the reactor.
The rapid advancements in the semantic web technologies has enabled personalised learning based on learnerâ??s characteristics in the learning process. We have implemented a Personalised Adaptive e-Learning system (onto-PAdeL) which uses an ontological approach in design-ing learnersâ?? models. Thus, this paper focuses on describing our approach for modelling learners based on their charac-teristics such as abilities, learning style(s), prior knowledge and preferences. The system uses Item Response Theory (IRT) for calculating learnerâ??s abilities. The learning style can be represented according to different theories, each of which supports personalisation in different ways. We show that using ontologies for learner modelling, in addition to many different benefits, enables reasoning for adaptive learning.
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