Periodic inspections are carried out on the cores of the British fleet of Advanced Gas-Cooled Reactors. Each graphite reactor core is comprised of about 300 fuel channels, but it is possible to inspect only a small fraction of these during a single outage. Decisions on which channels to inspect and take samples from require a balance to be struck between many competing requirements for data, as well as operational considerations.
An important part of making good decisions is ready access to existing data. There is a large amount of information available on the state of the core; data from previous inspections, and output from computer simulations. This information informs safety cases and plans for the lifetimes of the reactors, and it is therefore imperative that it is analysed in an effective and coherent manner.
Data visualisation is one way of disseminating this information. Not only is a graphical representation of data easy to understand, but the human eye is remarkably good at picking up patterns and trends. CHANSELA and PANDA both employ core maps, which are top‑down views of the reactor core, to visualise reactor data. Both pieces of software have been developed for EDF Energy.
The primary function of the CHANSELA software is to aid the choice of channels to inspect during reactor outages, but an important secondary function is the ability for the user to visualise the data that informs such decisions. PANDA allows the user to visualise core data such as fuel element burnup and channel power, which has been produced by computational simulations of the reactor. PANDA can also display histograms of the data across the whole core (or specific rings of channels), or plots of properties against time (or core burnup) for specified channels. The data can be exported in a range of formats for use in other software (including CHANSELA, where it directly supports the selection of channels for inspection).
These pieces of software provide support to those making complex and difficult decisions about reactor inspections and safety cases. The use of tools such as these, along with the development of new ways of visualising core data, makes the decision making process easier – which is beneficial for both safety and economic considerations