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RISS 인기검색어
- 검색키워드
- 저자 : Zhaofei Tian (검색결과 3 건)
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Application of POD reduced-order algorithm on data-driven modeling of rod bundle
Huilun Kang,Zhaofei Tian,Guangliang Chen,Lei Li,Tianyu Wang 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.1
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.
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Investigation on the nonintrusive multi-fidelity reduced-order modeling for PWR rod bundles
Huilun Kang,Zhaofei Tian,Guangliang Chen,Lei Li,Tianhui Chu 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.5
Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transferstate of the coolant in the reactor core is expensive, especially in scenarios that require extensiveparameter search, such as uncertainty analysis and design optimization. This work investigated theperformance 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 areextracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogatemodel 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 areextracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the lowfidelity 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 ofthe MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-drivenalgorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MFROM 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 highfidelity CFD simulation result, while the former only requires to taken the computational burden oflow-fidelity simulation. The results also show that the performance of the ANN model is slightly betterthan the Kriging model when using a high number of POD basis vectors for regression. Moreover, theresult presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelityfixed value initialization to accelerate complex simulation
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Design and analysis of RIF scheme to improve the CFD efficiency of rod-type PWR core
Chen, Guangliang,Qian, Hao,Li, Lei,Yu, Yang,Zhang, Zhijian,Tian, Zhaofei,Li, Xiaochang Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.10
This research serves to advance the development of engineering computational fluid dynamics (CFD) computing efficiency for the analysis of pressurized water reactor (PWR) core using rod-type fuel assemblies with mixing vanes (one kind of typical PWR core). In this research, a CFD scheme based on the reconstruction of the initial fine flow field (RIF CFD scheme) is proposed and analyzed. The RIF scheme is based on the quantitative regulation of flow velocities in the rod-type PWR core and the principle that the CFD computing efficiency can be improved greatly by a perfect initialization. In this paper, it is discovered that the RIF scheme can significantly improve the computing efficiency of the CFD computation for the rod-type PWR core. Furthermore, the RIF scheme also can reduce the computing resources needed for effective data storage of the large fluid domain in a rod-type PWR core. Moreover, a flow-ranking RIF CFD scheme is also designed based on the ranking of the flow rate, which enhances the utilization of the flow field with a closed flow rate to reconstruct the fine flow field. The flow-ranking RIF CFD scheme also proved to be very effective in improving the CFD efficiency for the rod-type PWR core.
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