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.

Fire field modelling using the SMARTFIRE automated dynamic solution control environment

SMARTFIRE is a fire field model based on an open architecture integrated CFD code and knowledge-based system. It makes use of the expert system to assist the user in setting up the problem specification and new computational techniques such as Group Solvers to reduce the computational effort involved in solving the equations. This paper concentrates on recent research into the use of artificial intelligence techniques to assist in dynamic solution control of fire scenarios being simulated using fire field modelling techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxations using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate enhanced solution reliability due to obtaining acceptable convergence within each time step unlike some of the comparison simulations.

[Book review] Psicologia Della Prova [Psychology of Proof]

Review of: Psicologia Della Prova [Psychology of Proof] edited by C. Cabras, Giuffré, Milano. 1996.

Zinc polycarboxylate dental cement for the controlled release of an active organic substance: proof of concept

The potential of employing zinc polycarboxylate dental cement as a controlled release material has been studied. Benzalkonium chloride was used as the active ingredient, and incorporated at concentrations of 1, 2 and 3% by mass within the cement. At these levels, there was no observable effect on the speed of setting. Release was followed using an ion-selective electrode to determine changes in chloride ion concentration with time. This technique showed that the additive was released when the cured cement was placed in water, with release occurring by a diffusion mechanism for the first 3 h, but continuing beyond that for up to 1 week. Diffusion coefficients were in the range 5.62 × 10(−6) cm(2) s(−1) (for 1% concentration) to 10.90 × 10(−6) cm(2) s(−1) (for 3% concentration). Up to 3% of the total loading of benzalkonium chloride was released from the zinc polycarboxylate after a week, which is similar to that found in previous studies with glass-ionomer cement. It is concluded that zinc polycarboxylate cement is capable of acting as a useful material for the controlled release of active organic compounds.