Application of POD reduced-order algorithm on data-driven modeling of rod bundle

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper proposes a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) to efficiently simulate the flow field in fuel rod bundles. A validated CFD model is established to output the flow field dataset, and the modes and coefficients of the flow field are extracted using the POD method. A deep feed-forward neural network is then selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. The flow field is reconstructed by combining the product of the POD basis and coefficients. Evaluation results show that the proposed POD-ROM accurately describes the flow status with high resolution in a few milliseconds.