• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.7
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• pp.689-695
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• 2012

The friction factor in a rod bundle and the loss coefficient at a spacer grid were examined. As a test section, 25 smooth rods, 9.5 mm in diameter and 2000 mm in length, were prepared and installed in a $5{\times}5$ square array in a square channel. In this case, the P/D (Pitch-to-Diameter ratio) was 1.35. In this work, plain (i.e., no mixing vanes), split-vane, and hybrid-vane spacer grids were tested. In a bare rod bundle (i.e., no spacer grid), the measured friction factors were in good agreement with the previous correlations. Among the spacer grids tested, the hybrid-vane spacer grid presented the largest friction factor in the rod bundle and loss coefficient. This may be because of the flow pattern change induced by large relative plugging of the flow cross section and mixing vane geometry. At Re=$5{\times}10^5$, the predicted loss coefficients of plain, splitvane, and hybrid-vane spacer grids were approximately 0.79, 0.80, and 0.88, respectively.