Solar receiver cavities, which are designed to absorb large amounts of concentrated solar irradiation,
form the central component of a solar collection plant. Since this receiver’s efficiency is directly
proportional to the plant’s overall performance, the optimum design of these receivers is an important
research field, as it is key to the maximisation of electricity output, while maintaining reasonable costs
as an alternative to the high costs of fossil fuel energy generation technologies.
Due to the high temperatures that are reached inside a solar receiver, the prediction of heat flux
distribution and the subsequent effects on conjugate heat transfer have been key areas of research in the
solar field. Initially dominated by experimental studies, research has trended towards numerical
prediction using finite volume methods (FVM), due to the low turnaround time and cost-effective nature
of this type of analysis.
Owing to the need to accurately predict these heat flux distributions, a methodology to numerically
simulate concentrated heat flux on complex surfaces of a solar receiver is developed. A combination of
Monte Carlo ray tracing (MCRT) methods and computational fluid dynamics (CFD) is implemented to
estimate system performance, while minimising computational time and expense, with limited sacrifice
After successful validation of this method with experimental data, iterative performance simulations on
a candidate geometry, implemented in a realistic solar-concentrating field, are performed to showcase
the ability of the methodology to accurately predict system performance. The sample geometry is based
on a number of implementations from various case studies and receivers that are used nowadays, with
each iteration allowing for parameter adjustment to maximise optical and thermal efficiency.
Key result outputs include the prediction of heat flux distributions and subsequent thermal stress raisers,
such as hot spots, convective and re-radiation heat losses, and operating temperatures. Determining
which of these thermal stress raisers from the implementation of this model can further improve and
Dissertation (MEng)--University of Pretoria, 2018.