The light collection and concentration subsystem (LCCS) of any concentrating solar thermal (CST) system is composed of the surfaces that collect and concentrate the sunlight and of the input surfaces of the receivers, or receivers’ envelopes, where the light is concentrated. For all commercial CST technologies the LCCS is, together with the power block, the subsystem that has more influence in the overall performance and cost. Thus, its optimization is critical to increase the cost-competitiveness of these systems. This optimization requires, in many cases, the optimization of the position, geometry and size of a very large number of solar collecting and concentrating surfaces as well as the optimization of the shape and size of the input surfaces of the receivers where the sunlight is concentrated. Because a full optimization requires the exploration of a configuration space with a very large number of dimensions, the traditional approach consist in making many initial assumptions to drastically reduce the number of dimensions of the configuration space to a handful, so that the optimization can be carried out using conventional high-end workstations in a matter of hours.
However, to achieve relevant breakthroughs and to substantially increase the cost-competitiveness of CST systems a bolder approach is needed, where sophisticated design and analysis tools, engineered from the start to be used in High Performance Computers (HPC), will be combined with sophisticated optimization strategies targeted to explore and find optimal solutions in very high dimensional configuration spaces.
This paper presents the first of a series of such design and analysis tools. The tool, call Flux Tracer, partitions the three-dimensional space in which the LCC subsystem under analysis is immersed into volumetric pixels (voxels) and computes the radiant energy flux that traverses each voxel as a function of time. It integrates the energy density in every voxel overtime, providing detailed information regarding how the radiant energy flows in space in a given LCC subsystem and in a given period of time. This information is the cornerstone of the highly sophisticated computational LCC subsystem optimization framework The Cyprus Institute (CYI) is developing, in collaboration with the Australian National University (ANU), targeted to be used in HPC’s.