International perspectives on energy policy and the role of nuclear power

In this book, expert energy economists assess the energy policy of thirty-one countries and the role of nuclear power. For many years the shock of Chernobyl took nuclear power off the agenda in most countries. Intense public relations activities by the industry, increasing evidence of climate change and failures to effectively reduce greenhouse gas emissions, have brought nuclear power issues back to the forefront of policy discussion in the nuclear renaissance countries. But some countries are just not prepared to go in that direction and, indeed, are still divesting themselves of their nuclear legacy, the nuclear phase-out countries. And how are nuclear issues being approached in the industrializing countries? An in-depth country-by-country analysis is presented within this framework. Out of such an analysis emerge thematic discussions on, among others, strategy in energy policy; nuclear plant safety, the impacts of nuclear accidents; the adequacy of nuclear power expertise. [Source: publisher's product description].

Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.

A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.

The financial crisis and nuclear power

An assessment of the impact of the financial crisis on the prospects for new nuclear power plant orders worldwide.