Session: 06-02: CSP Receivers and Reactors I

Paper Number: 166469

166469 - Numerical and Design Analysis of Free and Obstructed Falling Particle Receivers in Solar Tower Systems 

Abstract: 

Falling particle receivers and curtains are essential components of next-generation solar tower technologies, enabling direct particle heating to achieve high operational temperatures while mitigating flux limitations inherent to conventional receivers. This study develops a robust, reduced-order computational model to characterize the thermal and flow behavior of falling-particle curtains under various operating conditions. The model evaluates different particle flow scenarios, including free-falling and various obstructed-flow curtain configurations, using a hybrid approach that combines the Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD). DEM determines the velocity profiles of impeded particles, while CFD solves the energy transport equations. To enhance computational efficiency, a repeating representative domain is employed in DEM simulations, reducing cost while maintaining accuracy. The model utilizes 3D discretization to track curtain thickness evolution with a generic curtain mesh, ensuring that only physically relevant regions are populated with particles. Additionally, Monte Carlo ray tracing (MCRT) is incorporated to accurately model radiative exchange within the receiver, capturing the complex interactions between particles and walls. The formulation integrates continuity, momentum, and energy conservation equations to account for solar radiative heating, conduction, and convective losses while selectively resolving particle-populated regions and excluding void spaces. This computational framework provides a foundation for optimizing particle-based solar receivers, assessing key performance parameters, and enhancing the performance of falling-particle receivers in solar thermal applications. Furthermore, the efficiency and optimization of these computational models can be significantly enhanced using machine learning (ML) and multi-objective optimization (MOO). Trained surrogate models serve as intermediaries between high-fidelity simulations and optimization routines, capturing complex nonlinear relationships within the system while reducing computational demands. This data-driven approach strengthens the ability to identify high-performance configurations and provides a more flexible and efficient methodology for optimizing particle-based solar receivers in thermal energy applications.

Presenting Author: Abdullah Alfarhan University of Dayton

Presenting Author Biography: Abdullah Alfarhan is a post-doctoral research assistant in the field of falling particle receiver modeling at the University of Dayton. His area of research is Thermos-Fluid/Renewable energy applications that directly help to serve, develop, and support communities’ essential needs. His PhD research included modeling and designing of porous metallic structures for passive pumping in solar-thermal desalination systems. He has experience in gas/oil production as he worked in a gas plant operation, sulfur recovery unit dealing with various kinds of valves, pumps, and vessels. He also has experience in well drilling/cementing as he worked in the field, testing cementing mixture and then pumping it into wells. Abdullah has earned a B.S degree in mechanical engineering from University of Texas at San Antonio, and M.S. and Ph.D. degree from University of Dayton.