URANS–FSI Case Files for Axial Flow-Induced Vibration of a Cantilever Rod for Nuclear Application

This repository contains the input files, setup scripts, and documentation for the URANS–FSI simulation framework developed during the author’s PhD at the University of Manchester. This version includes two simulation cases of axial flow-induced vibration (FIV) of a blunt-ended cantilevered rod in turbulent axial flow: A case using the Reynolds Stress Model (RSM–LRR), and A case using the Eddy Viscosity Model (EVM–k–ω SST). For each turbulence model, the repository includes: Starting files required to initialise and run the URANS–FSI simulation, and Selected transient results that capture the coupled fluid–structure response over the simulated period. Both configurations employ an unsteady Reynolds-averaged Navier–Stokes (URANS) turbulence approach coupled with a structural solver through two-way fluid–structure interaction (FSI), implemented in foam-extend 4.0 using the solids4Foam library. The work forms part of the following publications and supplementary materials: 1. Muhamad Pauzi, A., Iacovides, H., Cioncolini, A., Li, H., & Nabawy, M. R. A. (2024). Application of URANS Simulation and Experimental Validation of Axial Flow-Induced Vibrations on a Blunt-End Cantilever Rod for Nuclear Applications. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-024-09505-5 2. Muhamad Pauzi, A. (2025). Axial Flow-Induced Vibrations over a Blunt-Ended Cantilevered Rod. PhD thesis, University of Manchester. https://research.manchester.ac.uk/en/studentTheses/axial-flow-induced-vibrations-over-a-blunt-ended-cantilevered-rod 3. Muhamad Pauzi, A. (2024a). Video of axial flow-induced vibration (FIV) simulations at different axial positions using RSM LRR and EVM k–ω SST models. https://doi.org/10.48420/27925344.v1 4. Muhamad Pauzi, A. (2024b). Video of axial flow-induced vibration (FIV) simulations at varying annulus Reynolds numbers using RSM LRR and EVM k–ω SST models. https://doi.org/10.48420/27936300.v1 These datasets are intended to support reproducibility, validation, and benchmarking of FSI research in nuclear and energy applications.