"An enhanced and optimized Monte Carlo method to calculate view factors in packed beds". (25th January 2023)

• Record Type: Journal Article
• Authors: Cortés, Eduardo; Gaviño, David; Calderón-Vásquez, Ignacio; García, Jesús; Estay, Danilo; Cardemil, José M.; Barraza, Rodrigo
• Is Part Of: Applied thermal engineering. Volume 219(2022)Part A
• Publication Date: 2023-01-25
• Subjects: Radiation Heat Transfer -- View factor -- Packed Bed -- MCRT -- Thermal Storage; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2022.119391
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Improved Monte Carlo method is proposed to calculate view factors in a packed bed. A novel view factor calculation methodology is described and evaluated. Characteristic values and correlations for view factors are found. A view factor calculation method under a layer concept is proposed. Abstract: Comprehensive radiation studies on packed beds require the estimation of view factors between the surfaces, which often computationally expensive. This paper proposes a novel method based on Monte Carlo Ray Tracing for high-precision and low time computation processing of view factors in random assemblies of cylindrical packed beds. The Monte Carlo Ray Tracing method is improved by coupling it with Kowsary's tangent sphere method under the parallel computation processing through Compute Unified Device Architecture, showing a reduction of the computational time in approximately 153 times less than a traditional Monte Carlo Ray Tracing method on the hardware used and obtaining a maximum relative error of 0.39% for configurations evaluated in the literature. Furthermore, a calculation methodology for view factors between particle–particle, particle–wall, and particle-lid under an original layer concept, is presented and applied on a set of randomly assembled monosized packed beds generated with the LIGGGHTS software, covering an average porosity range from 0.38 to 0.50 for the arrays. Finally, detailed analysis and discussion of the results are performed, allowing to find characteristic view factors for the particles according to their positions and correlations for view factors particle–wall and particle-lid which is defined as layer view factor method, considering error intervals for each interaction respectively, defining a view factor calculation methodology for packed beds within the studied porosity range.