Optimizing the Performance of Liquid-Based Medium-Temperature Volumetric Solar Thermal Receivers Using Genetic Algorithms

Liquid-based volumetric solar thermal receivers are a promising alternative to the traditional tubular receivers. By absorbing solar energy directly in a semi-transparent medium, volumetric receivers achieve higher capture efficiency and more uniform temperatures, overcoming the low capture efficiency issues faced by tubular receivers at high temperatures. However, accurately predicting the thermofluid behavior of volumetric receivers under various operating conditions is challenging due to the complex interactions between radiation, convection, conduction, and volumetric heating. A mechanistic model validated under a 6.5 kW solar simulator was developed, and demonstrated short-term (up to 7min) accuracy of predicting such behavior, yet the optimization of key parameters which can increase system efficiency and avoid the development of hot spots inside the receiver remains unexplored. This study addresses this parameter exploration and optimization using an evolutionary algorithm, specifically a genetic algorithm (GA), focusing on five critical parameters: attenuation coefficient, solar flux, receiver depth, top surface emissivity, and heating time. By using MATLAB’s multi-objective genetic algorithm solver, we identified multiple solutions representing trade-offs between efficiency and temperature uniformity inside the receiver. This evolutionary computation method constructs the Pareto front—a set of solutions that represent the best trade-offs between competing objectives—by iteratively selecting, recombining, and mutating candidate solutions while maintaining population diversity. The ability to produce multiple optimal solutions is highly valuable, as it provides a range of possible outcomes that can be adjusted to different operational conditions, ultimately improving the performance of volumetric receiver systems.