arXiv subjects

Stefano Rolfo

Publications and source records attributed to Stefano Rolfo.

3 recordsLinked to original sources

arXiv2025

Development of a Cost-Effective Simulation Tool for Loss of Flow Accident Transients in High-Temperature Gas-cooled Reactors

The aim of this work is to further expand the capability of the coarse-grid Computational Fluid Dynamics (CFD) approach, SubChCFD, to effectively simulate transient and buoyancy-influenced flows, which are critical in accident analyses of High-Temperature Gas-cooled Reactors (HTGRs). It has been demonstrated in our previous work that SubChCFD is highly adaptable to HTGR fuel designs and performs exceptionally well in modelling steady-state processes. In this study, the approach is extended to simulate a Loss of Flow Accident (LOFA) transient, where coolant circulation is disrupted, causing the transition from forced convection to buoyancy-driven natural circulation within the reactor core. To enable SubChCFD to capture the complex physics involved, corrections were introduced to the empirical correlations to account for the effects of flow unsteadiness, property variation and buoyancy. A 1/12th sector of the reactor core, representing the smallest symmetric unit, was modelled using a coarse mesh of approximately 60 million cells. This mesh size is about 6% of that required for a Reynolds Averaged Navier Stokes (RANS) model, where mesh sizes can typically reach the order of 1 billion cells for such configurations. Simulation results show that SubChCFD effectively captures the thermal hydraulic behaviours of the reactor during a LOFA transient, producing predictions in good agreement with RANS simulations while significantly reducing computational cost.

cs.CE↗

arXiv2020

An Iterative Machine-Learning Framework for RANS Turbulence Modeling

Machine-learning (ML) techniques provide a new and encouraging perspective for constructing turbulence models for Reynolds-averaged Navier--Stokes (RANS) simulations. In this study, an iterative ML-RANS computational framework is proposed that combines an ML algorithm with transport equations of a conventional turbulence model. This framework maintains a consistent procedure for obtaining the input features of an ML model in both the training and predicting stages, ensuring a built-in reproducibility. The effective form of the closure term is discussed to determine suitable target variables for the ML algorithm, and the multi-valued problem of existing constitutive theory is studied to establish a proper regression system for ML algorithms. The developed ML model is trained under a cross-case strategy with data from turbulent channel flows at three Reynolds numbers and \textit{a posteriori} simulations of channel flows show that the framework is able to predict both the mean flow field and turbulent variables accurately. Interpolation tests for the channel flow show the proposed framework can reliably predict flow features that lie between the minimum and maximum Reynolds numbers associated with the training data. A further test related to the flow over periodic hills also demonstrates a better result than a traditional turbulence model, indicating a promising predictive capability of the developed ML model for separated flow even though the model is only trained with planar channel flow data.

physics.flu-dyn↗

arXiv2019

An Initial Attempt of Converged Machine-Learning Assisted Turbulence Modeling in RANS Simulations with Eddy-Viscosity Hypothesis

This work presents a converged framework of Machine-Learning Assisted Turbulence Modeling (MLATM). Our objective is to develop a turbulence model directly learning from high fidelity data (DNS/LES) with eddy-viscosity hypothesis induced. First, the target machine-learning quantity is discussed in order to avoid the ill-conditioning problem of RANS equations. Then, the novel framework to build the turbulence model using the prior estimation of traditional models is demonstrated. A close-loop computational chain is designed to ensure the convergence of result. Besides, reasonable non-dimensional variables are selected to predict the target learning variables and make the solver re-converge to DNS mean flow field. The MLATM is tested in incompressible turbulent channel flows, and it proved that the result converges well to DNS training data for both mean velocity and turbulence viscosity profiles.

physics.flu-dyn↗