Unsteady RANSCalculations of Perturbed Natural Circulating Flow Within an Experimental Test Loop
By Deacon Marshall, Sophie L Brown, Ryan Tunstall, Alex Skillen
Published in Nuclear Future 21.3
DOI: https://doi.org/10.63198/CSQY3991
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SUMMARY
- The implications of transient events can have significant influence on the cooling capabilities of a natural circulation loop, where perturbations such as cold fluid injection can hinder natural circulation flow.
- Comparisons have been made between unsteady Reynolds averaged Navier-Stokes (RANS) computations to high fidelity Large Eddy Simulation (LES) results for a simple natural circulation loop.
- Two transient types were considered; low point cold fluid injection for 2000 seconds, and zero power scenarios where the heat input was removed once a steady state was established.
- The results from RANS and LES were shown to be comparable in their predictions of bulk mass flow rate and temperature trends, with both transient types demonstrating a reduction in prevailing mass flow rate and heat transfer capability.
AUTHORS
- Deacon Marshall holds the position of Thermofluid Engineer at Rolls‑Royce plc, his focus being in developing computationally feasible analysis methods to substantiate fluid system safety. He has a BEng (Hons) in Aerospace Engineering from Sheffield Hallam University with a thesis on CFD analysis
- Dr Sophie Brown completed her PhD in Applied Aerodynamics at Loughborough University in 2019. Following this she worked as an Aerodynamics Engineer in the Automotive Industry before joining Rolls-Royce as a Thermofluid Engineer in 2020.
- Dr Ryan Tunstall has a PhD in CFD and turbulence modelling for nuclear power and propulsion systems from the University of Manchester and is a Chartered Engineer with the IMechE.,He has spent over eight years at Rolls‑Royce plc, where he is a Technical Specialist in Nuclear Thermal Hydraulics.
- Dr Alex Skillen is a Lecturer in Engineering Simulation and Data Science within the Department of Mechanical and Aerospace Engineering at the University of Manchester. Alex’s research interests lie at the intersection of Computational Fluid Dynamics (CFD) and machine learning.