• Nuclear Engineering and Technology
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• 제54권1호
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• pp.36-48
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• 2022

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.

• Journal of Korean Society for Atmospheric Environment
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• 제16권E호
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• pp.1-17
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• 2000

In this study, the concentrations of particulate organic constituents of environmental tobacco smoke(ETS) were determined using an environmental smog chamber, where ETS is the sole source of target compounds. ETS was generated in a 30 ㎥ environmental chamber by a number of different cigarettes, including the Kentucky 1R4F reference cigarette and eight commercial brands. A total of 12 experimental runs was conducted, and target analytes included a group of ETS markers both in vapor and particulate phase and a class of polycylic aromatic hydrocarbos(PAHs) associated with ETS particles. The mass concentrations of PAH in ETS particles were also determined. The average contents of benzo(a) pyrene and benzo(a) anthracene in ETS particles for the commercial brands were 12.8 and 21.5$\mu\textrm{g}$/g, respectively, There values are all somewhat higher than those determined previously by other studies. Results form the chamber study are further used to estimate the average and variability of cigarette yields for target compounds associated with ETS. Finally, ratios of RSP to the surrogate standards of UVPM, FPM and solanesol were calculated for each sample. The average conversion factors factors for the eight commercial brands were 7.3, 38, and 41 for UVPM, EPM, and solanesol, respectively. The UVPM and FPM factors are in good agreement with the recently published values. Whereas there might be a substantial difference in the solanesol content among cigarettes produced in different countries, the variability is somewhat greater than those of UVPM and FPM, Unfortunately, comparison of the PAH yield data from this study with literature values was complicated by a lack of consistency in cigarette smoke generating methodology. Validation of the PAH yields was also difficult due to a lack of information on the ETS related PAH in the literature. From and engineering viewpoint , however, these data on the cigarette yields of ETS components may still provide useful information to studies on the mathematical modeling of indoor air quality management regarding tobacco smoke as a source of interest, or to studies on the assessment of human exposure to ETS.