Fluid data are of great significance for analyzing the fluid structure and understanding the law of fluid movement. Apart from the experimental test, the computational fluid dynamics (CFD) method has been widely applied in the field of fluid dynamics over the past few decades. However, due to the high computational costs of CFD method and the limitation of computational resources, it is still challenging to accurately calculate and obtain the high-resolution (HR) flow fields. To this end, a novel framework based on the super-resolution (SR) algorithm, namely, new enhanced down-sampled skip-connection and multi-scale (E-DSC/MS), is reported to achieve the HR global flow reconstruction from low-resolution data. Through the new SR flow reconstruction method, the HR flow fields of two benchmark 2D cases (i.e., cylinder and hydrofoil) are precisely and efficiently predicted using a universal SR model. The effectiveness of the new E-DSC/MS algorithm is tested by comparing it with the traditional super-resolution convolution neural network and U-net in terms of the velocity field prediction of the self-region (training region) and other-region (untrained region). The result shows that the universal SR flow reconstruction framework is able to increase the spatial resolution of velocity field by 16 times, and flow fields reconstructed by E-DSC/MS are in good agreement with the ground-truth data. In addition, the E-DSC/MS model could reconstruct the global flow field with a correlation coefficient of more than 99% regardless of the selection of the arbitrary region/window for SR training. The present method overcomes the limitation of the existing techniques in efficiently reconstructing HR flow field, which helps to reduce the requirement for expensive experimental equipment and to accelerate the CFD simulation process.
The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].
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