Session: VVUQ for Fluid Dynamics and Heat Transfer 2

Paper Number: 152485

152485 - Experimental Investigation of Near-Wall Bypass Flow in Randomly Packed Pebble Beds 

Abstract: 

Packed spherical pebble beds are increasingly utilized in Pebble Bed Reactors (PBR), presenting unique challenges due to their complex and randomized geometries. These inherent structures significantly influence fluid dynamics within reactor cores, particularly in near-wall regions where bypass flow or flow channeling occurs. This phenomenon, characterized by increased flow velocity near walls, can lead to uneven flow distribution, reduced heat transfer efficiency, and non-uniform temperature distribution, potentially compromising reactor safety and performance. This study focuses on the critical phenomenon of near-wall bypass flow, conducting a rigorous experimental investigation with an emphasis on verification, validation, and uncertainty quantification (VVUQ) principles. We examine a near-wall region of a randomly packed pebble bed facility with a bed-to-sphere diameter ratio of 4.7, employing advanced experimental techniques including Particle Image Velocimetry (PIV) and Laser Induced Fluorescence (LIF). These methods measure fluid velocity and temperature distributions at Reynolds numbers of 160, 370, 580, 770, and 1100 under both isothermal and non-isothermal conditions. This allowed us to discern differences in flow patterns under varying thermal influences. The Matched Index of Refraction (MIR) method enables non-intrusive observation of void spaces, providing critical insights into flow patterns under both isothermal and non-isothermal conditions. Our comprehensive analysis of PIV results encompasses first and second-order statistical measures of the flow, including velocity magnitude, mean velocity components, velocity fluctuations, Reynolds stresses, vorticity, and turbulence kinetic energy. We further employ advanced decomposition techniques such as Proper Orthogonal Decomposition (POD) and its spatial and multi-scale variants to elucidate complex flow structures. The LIF measurements provided detailed temperature distributions, revealing how the increased flow velocity near the wall affects thermal dynamics. Additionally, we conduct a three-dimensional reconstruction of the packed bed geometry, validating computational simulations and illustrating the intricate geometry of the system. Axial pressure drop measurements are compared against existing correlations, with a focus on quantifying prediction uncertainties. Our research contributes significantly to VVUQ in nuclear engineering by providing high-fidelity experimental datasets with well-characterized uncertainties for computational fluid dynamics (CFD) model validation. It demonstrates a systematic approach to uncertainty quantification in complex, multi-physics experiments and offers insights into the propagation of geometric uncertainties in randomly packed beds to flow and heat transfer predictions. This comprehensive VVUQ approach enhances the reliability of PBR design and safety analyses, addressing critical aspects of flow distribution and heat transfer. By rigorously quantifying uncertainties and validating models against detailed experimental data, our work contributes to the development of more accurate and reliable simulation tools for next-generation nuclear reactor designs. The methodologies and findings presented here have broad implications for improving the fidelity of computational models in complex engineering systems beyond nuclear applications.

Presenting Author: Seth Macias Texas A&M University

Presenting Author Biography: Seth Macias is a mechanical engineering graduate student at Texas A&M University currently researching at the Thermal Hydraulics Lab under the supervision of Dr. Hassan of the Nuclear Engineering Department. His research focuses on experimental validation of fluid and heat transfer behavior in pebble bed reactors for computational fluid dynamics models. He plans to graduate with a Masters of Science in mechanical engineering in the spring of 2025.

Authors:

Seth Macias Texas A&M University
Hansol Kim Texas A&M University
Joseph Seo Texas A&M University
Yassin Hassan Texas A&M University