Research and development on digital twins of nuclear power systems has focused on high-precision real-time simulation and the prediction of local complex three-dimensional fluid dynamics. Traditional computational fluid dynamics (CFD) methods cannot take into consideration the efficiency and accuracy of fluid dynamics. In this study, a fast-flow field-prediction framework based on proper orthogonal decomposition (POD) and deep learning is proposed. Compressed data containing the original flow field information are obtained using POD and deep neural network (DNN) is used to construct the POD-DNN flow field reduction model to achieve fast flow field prediction. The calculation accuracy and speed of the reduced-order model are analyzed in detail, considering the flow field of the nuclear compressor and key flow equipment of the nuclear power system as objects. The results show that the average relative deviation of the POD-DNN is <10% and calculation time is <1% when compared to those of CFD. This research shows that the high-fidelity model constructed using model reduction and deep learning is a feasible method for the realization of digital twins of the nuclear power system in engineering.
A passive flow control method is applied to a vehicle external door mirror by introducing flow from a slot drilling from the front surface to four slots around the rear surface. The wake flow fields behind the door mirror and slotted door mirror are experimentally investigated via particle image velocimetry measurements at a Reynolds number of 12,736. The time-averaged flow fields suggest that the reverse-flow region of the slotted door mirror is reduced and pushed downstream; correspondingly, the turbulent kinetic energy of the slotted door mirror is reduced, implying the suppression of unsteadiness and a reduction in the drag force acting on the door mirror. As a result of the jet flow, the wake flow of the slotted door mirror exhibits a weaker spatial correlation and a smaller length scale, indicating the suppression of large-scale vortex shedding and the enhancement of small-scale flow motion by the slots. The jet flows from the left and right slots of the slotted door mirror prevent the upper and lower shear layers from interacting with each other, which promotes symmetric vortex shedding in the near-wake region. The power spectral density and modal energy distributions suggest that the strength of the primary vortex shedding is reduced by the slotted door mirror. The spatial patterns of the first two proper orthogonal decomposition modes suggest that the jet flow distorts the dominant large-scale structure, reducing the strength of the flow oscillation behind the slotted door mirror. The spatial distributions of modes 3 and 4 suggest that the symmetric vortex shedding is enhanced by the jet flow from the slotted door mirror.
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