The use of Th in HTR: State of the art and implementation in Th/Pu fuel cycles

Nowadays nuclear is the only greenhouse-free source that can appreciably respond to the increasing worldwide energy demand. The use of Thorium in the nuclear energy production may offer some advantages to accomplish this task. Extensive R&D on the thorium fuel cycle has been conducted in many countries around the world. Starting from the current nuclear waste policy, the EU-PUMA project focuses on the potential benefits of using the HTR core as a Pu/MA transmuter. In this paper the following aspects have been analysed: (1) the state-of-the-art of the studies on the use of Th in different reactors, (2) the use of Th in HTRs, with a particular emphasis on Th-Pu fuel cycles, (3) an original assessment of Th-Pu fuel cycles in HTR. Some aspects related to Thorium exploitation were outlined, particularly its suitability for working in pebble-bed HTR in a Th-Pu fuel cycle. The influence of the Th/Pu weight fraction at BOC in a typical HTR pebble was analysed as far as the reactivity trend versus burn-up, the energy produced per Pu mass, and the Pu isotopic composition at EOC are concerned. Although deeper investigations need to be performed in order to draw final conclusions, it is possible to state that some optimized Th percentage in the initial Pu/Th fuel could be suggested on the basis of the aim we are trying to reach. Copyright © 2009 Guido Mazzini et al.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.

Modular risk modeling and management of water infrastructure disaster incidents

Water supply and wastewater control are critical elements of society's infrastructure. The objective of this study will be to provide a generic risk assessment tool to provide municipalities and the nation as a whole with a quantifiable assessment of their vulnerability to water infrastructure threats. The approach will prioritize countermeasures and identify where research and development is required to further minimize risk. This paper outlines the current context, primary concerns and state-of-the art in critical infrastructure risk management for the water sector and proposes a novel approach to resolve existing questions in the field. The proposed approach is based on a modular framework that derives a quantitative risk index for varied domains of interest. The approach methodology is scaleable and based on formal definitions of event probability and severity. The framework is equally applicable to natural and human-induced hazard types and can be used for analysis of compound risk events.

Numerical analysis of shallow foundations under combined horizontal and torsional loading

Foundations of subsea infrastructure in deep water subjected to asymmetric environmental loads have underscored the importance of combined torsional and horizontal loading effects on the bearing capacity of rectangular shallow foundations. The purpose of this study is to investigate the undrained sliding and torsional bearing capacity of rectangular and square shallow foundations together with the interaction response under combined loading using three-dimensional finite element (3D-FE) analysis. Upper bound plastic limit analysis is employed to establish a reference value for horizontal and torsional bearing capacity, and an interaction relationship for the combined loading condition. Satisfactory agreement of plastic limit analysis (PLA) and 3D-FE results for ultimate capacity and interaction curves ensures that simple PLA solution could be used to evaluate the bearing capacity problem of foundation under combined sliding and torsion.