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We propose a novel reduced-order model and examine its applicability to the complex three-dimensional turbulent wake of a generic square-backed bluff body called the Ahmed body at the Reynolds number ReH = U∞H/ν = 9.2 × 104 (where U∞ is free-stream velocity, H the height of the body, and ν viscosity). Training datasets are obtained by large eddy simulation. The model reduction method consists of two components—a Visual Geometry Group (VGG)-based hierarchical autoencoder (H-VGG-AE) and a temporal convolutional neural network (TCN). The first step is to map the high-dimensional flow attributes into low-dimensional features, namely latent modes, which are employed as the input for the second step. The TCN is then trained to predict the low-dimensional features in a time series. We compare this method with a TCN based on proper orthogonal decomposition (POD), which utilizes time coefficients as the input in the second part. It turns out that the H-VGG-AE has a lower reconstruction error than POD when the number of latent modes is relatively small in the first part. As the number of latent modes increases, POD exceeds in the performance of model reduction. However, the H-VGG-AE-based TCN is still more effective in terms of spatiotemporal predictions because it has a lower prediction error and costs much less time. [ABSTRACT FROM AUTHOR]

The flow around a seal-vibrissa-shaped cylinder (SVSC) is numerically investigated using the large eddy simulation framework at a Reynolds number of 20 000. Compared with a circular cylinder (CC), the wake of the SVSC presents more stable three-dimensional separation, a longer vortex formation length, and a weaker vortex strength. The mean drag and fluctuation of the lift coefficient are 59.5% and 87.7% lower than those of the CC, respectively. Three-dimensional spectral proper orthogonal decomposition (SPOD) is used to investigate the turbulent flow around these two types of cylinders in terms of the spatial modes, mode energy, mode coefficients, and reconstructed flow by a reduced-order modeling. Four typical vortex shedding patterns are first extracted by SPOD for the SVSC, producing crescent-, twist-, branch-, and knot-shaped vortices. A concept model is proposed for the wake dynamics of the SVSC, allowing the formation and transformation of these modes to be elucidated. Detailed analysis of the impact of the flow pattern on the associated forces indicates that the dominant out-phase vortex shedding at the upper and lower saddle planes makes a significant contribution to the reduction in lift fluctuations. [ABSTRACT FROM AUTHOR]

In this paper, the laminar forced convection heat transfer of nanofluid through a bent channel was numerically investigated. The lattice Boltzmann method was used for solving the governing equations in the domain. The effect of different parameters such as Reynolds number (50 ≤ Re ≤ 150), vertical passage ratio (2.0 ≤ M ≤ 4.0), and nanoparticle solid volume fractions (Φ = 0, 0.01, 0.03, 0.05) are analyzed in terms of streamlines, isotherms, and local Nusselt numbers. It was concluded from this study that the local and average Nusselt number increased with increasing nanoparticle volume fraction regardless of Re and M. Moreover, the effect of the nanofluid concentration on the increment of heat transfer was more remarkable at higher values of the Reynolds number. Simulations show that by increasing the Reynolds number or decreasing the vertical passage ratio, the local and average Nusselt number increases. [ABSTRACT FROM AUTHOR]