Modeling of In-Line Tube Banks Inside Advanced Gas-Cooled Reactor Boilers. Issue 19 (12th November 2020)

• Record Type: Journal Article
• Authors: Blackall, James L.; Iacovides, Hector; Uribe, Juan C.
• Is Part Of: Heat transfer engineering. Volume 41:Issue 19/20(2020)
• Page Start: 1731
• Page End: 1749
• Publication Date: 2020-11-12
• Subjects: Heat -- Transmission -- Periodicals; 621.4022
• DOI: 10.1080/01457632.2019.1640486
• Languages: English
• ISSNs: 0145-7632

Abstract

Abstract: The objective of this research is the prediction of forced convection across confined in-line tube banks found in Advanced Gas-Cooled Reactor (AGR) boiler systems. The focus of this work is periodic flow about a 2 × 2 array. A number of Reynolds-Averaged Navier–Stokes (RANS) turbulence modeling closures have been applied to the challenging case of 1.6 pitch-to-diameter spacing at which flow undergoes transition from straight to diagonal. In contrast to Large Eddy Simulation (LES) predictions, those of RANS models fail to predict transition to diagonal flow. As LES computations are not anticipated to become routine enough for the purpose of flow diagnostics in industrial contexts for many years, further refinement to the RANS methodology in the form of an analytical wall function is one of the main objectives. Its eventual inclusion is not only expected to improve the representation of turbulent boundary layer behavior, but also has scope to account for augmented surface roughness effects experienced by AGR technologies nearing their end-of-life. This work is ultimately in aid of EDF Energy's policy of AGR lifetime extensions, to allow the UK Government sufficient time to formulate a credible Nuclear New Build scheme to secure our energy requirements in the coming decades. It was found that all but dynamic LES adequately reproduces flow behavior across all criteria considered, and that standard wall functions left much to be desired in the case of flow and thermal predictions, while introducing the analytical wall function generates improvements for flow and heat transfer predictions that are not universally shared between high-Re models utilized.