Development of Data Driven Models to Predict Pressure Drop in Wire-Wrapped Rod Bundles

Abstract

The Generation IV reactors (Gen IV) are a set of new designs of nuclear reactors under development by a consortium of counties to meet the energy necessities of the future, including sustainability, safety, and economic feasibility. The liquid metal fast reactor design (LMFRs) is one of the most prominent Gen VI options. Liquid metal has a large thermal conductivity, allowing for a significant power density. Moreover, in opposition to the traditional water reactors, LMFRs operate at near atmospheric pressures as metals have a high boiling temperature. One of the most common fuel designs for LMFRs is the tightly packed wire-wrapped rod bundle. The wires around the fuel pins keep the distance between the rods, contribute to the flow mixing, homogenize the temperature field, and increase the flow friction. Thus, the knowledge of the pressure drop through the bundle and the flow split across the flow area is fundamental in the design of LMFR reactors. The first part of this work presents a recalibration of the UCTD correlation for friction factor in wire-wrapped rod bundles using Multi-Objective Genetic Algorithm in the turbulent regime, improving its prediction of the flow split. When applying this methodology to the laminar regime, this study identified that the laminar data available in the literature is insufficient to extend this method to this regime. The second part of this work presents a dataset of 93 CFD simulations of laminar flows in wire-wrapped rod bundles. The data includes the friction factor and the flow split between the interior, edge, and corner regions for bundles with 7 to 91 pins. This publication also presents an ANN-based model to predict these flow parameters based on the CFD dataset. The accuracy of the predictions is verified with a dataset of experimental data collected from the open literature composed of 42 bundles.