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 optimal sample allocation problem for mapped outputs

Abstract not available

Using computer models to identify optimal conditions for flip-chip assembly and reliability

Flip-chip assembly, developed in the early 1960s, is now being positioned as a key joining technology to achieve high-density mounting of electronic components on to printed circuit boards for high-volume, low-cost products. Computer models are now being used early within the product design stage to ensure that optimal process conditions are used. These models capture the governing physics taking place during the assembly process and they can also predict relevant defects that may occur. Describes the application of computational modelling techniques that have the ability to predict a range of interacting physical phenomena associated with the manufacturing process. For example, in the flip-chip assembly process we have solder paste deposition, solder joint shape formation, heat transfer, solidification and thermal stress. Illustrates the application of modelling technology being used as part of a larger UK study aiming to establish a process route for high-volume, low-cost, sub-100-micron pitch flip-chip assembly.

Predicting optimal process conditions for flip-chip aassembly using copper column bumped dies

Recently, research has been carried out to test a novel bumping method which omits the under bump metallurgy (UBM) forming process by bonding copper columns directly onto the Al pads of the silicon dies. This bumping method could be adopted to simplify the flip chip assembly process, increase the productivity and achieve a higher I/O count. Computer modelling methods are used to predict the shape of solder joints and response of the flip chip to thermal cyclic loading. The accumulated plastic strain energy at the comer solder joints is used as the damage indicator. Models with a range of design parameters have been compared for their reliability. The ranking of the relative importance of these parameters is given. Results from these analyses are being used by our industrial and academic partners to identify optimal design conditions.