Cavitation of Multiscale Vortices in Circular Cylinder Wake at Re = 9500

Cavitation characteristics in the wake of a circular cylinder, which contains multiscale vortices, are numerically investigated via Large Eddy Simulation (LES) in this paper. The Reynolds number is 9500 based on the inlet velocity, the cylinder diameter and the kinematic viscosity of the noncavitation liquid. The Schneer–Sauer (SS) model is applied to cavitation simulation because it is more sensitive to vapor–liquid two-phase volume fraction than the Zwart–Gerber–Belamri (ZGB) model, according to theoretical analyses. The wake is quasiperiodic, with an approximate frequency of 0.2. It is found that the cavitation of vortices could inhibit the vortex shedding. Besides, the mutual aggregation of small-scale vortices in the vortex system or the continuous stripping of small-scale vortices at the edge of large-scale vortices could induce the merging or splitting of cavities in the wake.

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical fluid flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, and (4) a cross-sectional field of turbulent channel flow, in terms of a number of latent modes, the choice of nonlinear activation functions, and the number of weights contained in the AE model. We find that the AE models are sensitive to the choice of the aforementioned parameters depending on the target flows. Finally, we foresee the extensional applications and perspectives of machine learning based order reduction for numerical and experimental studies in the fluid dynamics community.

Effect of magnetic field on the steady nanofluid flow past obstacle

The fluid flow and heat transfer of a nanofluid past a circular cylinder in a rectangular duct under a strong transverse magnetic field is studied numerically using a quasitwo-dimensional model. Transition from laminar flow with separation to creeping laminar flow is determined as a function of Hartmann number and the volume fraction of nanoparticle, as are critical Hartmann number, and the heat transfer from the heated wall to the fluid. Downstream cross-stream mixing induced by the cylinder wake was found to increase heat transfer. The successive changes in the flow pattern are studied as a function of the Hartmann number. Suppression of vortex shedding occurs as the Hartmann number increases.

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the thresholded least square algorithm and the adaptive least absolute shrinkage and selection operator which show reasonable ability with a wide range of sparsity constant in our preliminary tests, to a two-dimensional single cylinder wake at $Re_D=100$ , its transient process and a wake of two-parallel cylinders, as examples of high-dimensional fluid data. To handle these high-dimensional data with SINDy whose library matrix is suitable for low-dimensional variable combinations, a convolutional neural network-based autoencoder (CNN-AE) is utilized. The CNN-AE is employed to map a high-dimensional dynamics into a low-dimensional latent space. The SINDy then seeks a governing equation of the mapped low-dimensional latent vector. Temporal evolution of high-dimensional dynamics can be provided by combining the predicted latent vector by SINDy with the CNN decoder which can remap the low-dimensional latent vector to the original dimension. The SINDy can provide a stable solution as the governing equation of the latent dynamics and the CNN-SINDy-based modelling can reproduce high-dimensional flow fields successfully, although more terms are required to represent the transient flow and the two-parallel cylinder wake than the periodic shedding. A nine-equation turbulent shear flow model is finally considered to examine the applicability of SINDy to turbulence, although without using CNN-AE. The present results suggest that the proposed scheme with an appropriate parameter choice enables us to analyse high-dimensional nonlinear dynamics with interpretable low-dimensional manifolds.

Transition to Periodic Behaviour of Flow Past a Circular Cylinder under the Action of Fluidic Actuation in the Transitional Regime

This study focuses on the numerical investigation of the underlying mechanism of transition from chaotic to periodic dynamics of circular cylinder wake under the action of time-dependent fluidic actuation at the Reynolds number = 2000. The forcing is realized by blowing and suction from the slits located at ±90∘ on the top and bottom surfaces of the cylinder. The inverse period-doubling cascade is the underlying physical mechanism underpinning the wake transition from mild chaos to perfectly periodic dynamics in the spanwise-independent, time-dependent forcing at twice the natural vortex-shedding frequency.

Influence of span-wise coherence on the acoustic radiation in a cylinder wake

The aim of this study is to detect and visualise the influence of span-wise coherence on propagating sound waves emanating from a flow around circular cylinders with span-wise variations of the local radius. Synchronous particle image velocimetry (PIV) and microphone measurements are performed in a circular wind tunnel with a nozzle size of 0.4 m×0.4 m at a maximum flow speed of U∞ = 43m s−1 . The test section is surrounded by a full anechoic chamber of approximately 9 m×9 m×5 m.