Year: 2025
Type: conference-paper
Abstract: An artificial intelligence (AI) aided generative design workflow for the optimization of cavity receivers for concentrated solar thermal (CST) energy systems is presented. The workflow integrates the Non-dominated Sorting Genetic Algorithm III (NSGA-III) with generative design methodologies and optical evaluation through Monte-Carlo ray-tracing in an interoperable way, to optically optimize the geometry of cavity receivers according to a set of objective functions for a given heliostat field. As a demonstrator test case, the workflow is used to provide an optimal geometrical design of a cavity receiver given the Cyprus Institute’s PROTEAS heliostat field. It is shown that the workflow is able to generate unconventional, non-intuitive and efficient receiver designs in an automated manner, which are often not conceived by traditional design approaches.
Institution: Cyprus Institute
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416666895
FWCI:
Cites:
Cited by:
Related to: