scroll to top
0
Press enter or spacebar to select a desired language.
Press enter or spacebar to select a desired language.
Your source for trusted research content

EBSCO Auth Banner

Let's find your institution. Click here.

Particle swarm optimization algorithm for optical-geometric optimization of linear fresnel solar concentrators.

  • Academic Journal
  • Renewable Energy: An International Journal. Jan2019, Vol. 130, p992-1001. 10p.
  • Abstract In this work we propose the application of the particle swarm optimization (PSO) method to Optical-geometric optimization of linear Fresnel reflector solar concentrators (LFR). The optical-radiative behavior of the system is modeled based on the Ray tracing-Monte Carlo algorithm that calculates the optical performances and the radiative energy collected by the absorber tube. For this system, the application of particle swarm optimization method for optical optimization has not been studied yet. For this reason, testing this method and developing its strategy of implementation in the case of Fresnel concentrator system is one of other objective of this work. In that sense, we present mainly the coupling strategy between the Monte Carlo-ray tracing algorithm and the particle swarm optimization method. In first, the PSO optimization algorithm established is validated by comparison with a deterministic method results. Then, we demonstrated the ability of the method to resolve optimization problem with high number of decision parameters and complex objective function. Subsequently, the various guidelines allowing the rational use of this method in the case of linear Fresnel systems optimization are proposed and discussed. Subsequently, the optimization algorithm is applied to the case of linear Fresnel concentrator module designed in the framework of SIROCCO project. Highlights • Modeling the optical behavior of the solar linear Fresnel reflectors system. • Optimize solar linear Fresnel reflectors geometry using the PSO method. • Effect of the PSO method parameters on the quality of the optimum. • Find the minimum swarm size to avoid the problem of local optimums. [ABSTRACT FROM AUTHOR]
Additional Information
sponsored