Gaussian processes for machine learning (GPML) toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

Comparison of square and hexagonal fuel lattices for high conversion PWRs

This paper reports on an investigation into fuel design choices of a pressurized water reactor operating in a self-sustainable Th- 233U fuel cycle. In order to evaluate feasibility of this concept, two types of fuel assembly lattices were considered: square and hexagonal. The hexagonal lattice may offer some advantages over the square one. For example, the fertile blanket fuel can be packed more tightly reducing the blanket volume fraction in the core and potentially allowing to achieve higher core average power density. The calculations were carried out with Monte-Carlo based BGCore code system and the results were compared to those obtained with Serpent Monte-Carlo code and deterministic transport code BOXER. One of the major design challenges associated with the SB concept is high power peaking due to the high concentration of fissile material in the seed region. The second objective of this work is to estimate the maximum achievable core power density by evaluation of limiting thermal hydraulic parameters. The analysis showed that both fuel assembly designs have a potential of achieving net breeding. Although hexagonal lattice was found to be somewhat more favorable because it allows achieving higher power density, while having breeding performance comparable to the square lattice case. © Carl Hanser Verlag München.