Onset of vortex shedding around a short cylinder

This paper presents results of three-dimensional direct numerical simulations (DNS) and global linear stability analyses of a viscous incompressible flow past a finite-length cylinder with two free flat ends. The cylindrical axis is normal to the streamwise direction. The work focuses on the effects of aspect ratios (in the range of $0.5\leq {\small \text{AR}} \leq 2$ , cylinder length over diameter) and Reynolds numbers ( $Re\leq 1000$ based on cylinder diameter and uniform incoming velocity) on the onset of vortex shedding in this flow. All important flow patterns have been identified and studied, especially as ${\small \text{AR}}$ changes. The appearance of a steady wake pattern when ${\small \text{AR}} \leq 1.75$ has not been discussed earlier in the literature for this flow. Linear stability analyses based on the time-mean flow has been applied to understand the Hopf bifurcation past which vortex shedding happens. The nonlinear DNS results indicate that there are two vortex shedding patterns at different $Re$ , one is transient and the other is nonlinearly saturated. The vortex-shedding frequencies of these two flow patterns correspond to the eigenfrequencies of the two global modes in the stability analysis of the time-mean flow. Wherever possible, we compare the results of our analyses to those of the flows past other short- ${\small \text{AR}}$ bluff bodies in order that our discussions bear more general meanings.

CFD Modelling of Wake-Induced Vibration At Low Reynolds Number

Flow-induced vibration is an enthralling phenomenon in the field of engineering. Numerous studies have been conducted on converting flow kinetic energy to electrical energy using the fundamental. Wake-induced vibration is one of the configurations used to optimise the generation of electricity. The results of the study on the effect of the gap between the multiple bluff bodies will provide insight into optimising the energy harvesting process. This study focuses on fluid behaviour and response behind two circular cylinders arranged in tandem when interacting with a fluid flow at low Reynolds numbers ranging from 200 to 1000. The study has been done on several gap lengths between the two cylinders, between 2D and 5D. The study was carried out numerically by using OpenFOAM. At Re = 1000, it is found that the gap length of 2.5D is optimal in terms of producing the highest lift force coefficient on the downstream circular cylinder.

Interaction and acoustics of separated flows from a D-shaped bluff body

Purpose For flow around elongated bluff bodies, flow separations would occur over both leading and trailing edges. Interactions between these two separations can be established through acoustic perturbation. In this paper, the flow and the acoustic fields of a D-shaped bluff body (length-to-height ratio L/H = 3.64) are investigated at height-based Reynolds number Re = 23,000 by experimental and numerical methods. The purpose of this paper is to study the acoustic feedback in the interaction of these two separated flows. Design/methodology/approach The flow field is measured by particle image velocimetry, hotwire velocimetry and surface oil flow visualization. The acoustic field is modeled in two dimensions by direct aeroacoustic simulation, which solves the compressible Navier–Stokes equations. The simulation is validated against the experimental results. Findings Separations occur at both the leading and the trailing edges. The leading-edge separation point and the reattaching flow oscillate in accordance with the trailing-edge vortex shedding. Significant pressure waves are generated at the trailing edge by the vortex shedding rather than the leading-edge vortices. Pressure-based cross-correlation analysis is conducted to clarify the effect of the pressure waves on the leading-edge flow structures. Practical implications The understanding of interactions of separated flows over elongated bluff bodies helps to predict aerodynamic drag, structural vibration and noise in engineering applications, such as the aerodynamics of buildings, bridges and road vehicles. Originality/value This paper clarifies the influence of acoustic perturbations in the interaction of separated flows over a D-shaped bluff body. The contribution of the leading- and the trailing-edge vortex in generating acoustic perturbations is investigated as well.

The characters exploration of a piezoelectric pump with fishtail-shaped bluffbody

This paper focuses on a new structure in the valveless piezoelectric pump, which has a combination structure of the conical flow channel and two fishtail-shaped bluffbodies in the chamber of the pump. The fishtail-shaped bluffbody is inspired by the shape of the swimming fish to diminish the backflow and optimize the performance of the pump. The performance is studied by changing the shape and size of the inlet and outlet, the bluff bodies’ height and the space between two bluff bodies. The results show that the 3 mm × 3 mm square inlet, 3 mm diameter round outlet, 3 mm height of bluffbodies, 6.8 mm pitch of bluffbodies has a best performance in all 10 prototypes, which implements a maximum flow rate of 87.5 ml/min at 170 V 40 Hz with a noise of 42.6 dB. This study makes a preliminary investigation and theoretical explanation for the subsequent optimization of this structure, improved the performance of the valveless piezoelectric pump, broaden the thinking of the design for the bluffbody for better performance of the valveless piezoelectric pump.

Deep Learning Techniques for Effective Prediction of Aerodynamic Properties of Elliptical Bluff Bodies

Predicting aerodynamic forces on bluff bodies remains to be a challenging task due to the unpredictable flow behavior, specifically at higher Reynolds numbers. Experimental approaches to determine aerodynamic coefficients could be costly and time consuming. In the meantime, use of numerical techniques could also require a considerable computational cost and time depending on complexity of the flow behavior. The research focusses on developing an effective deep learning technique to predict aerodynamic force coefficients acting on elliptical bluff bodies for a given aspect ratio and given flow condition. Collecting data for drag and lift coefficients of several aspect ratios for flow conditions starting from onset of vortex shredding to verge of subcritical region is conducted by an accurate full order model. The specified region will provide a transient flow behavior and thus lift coefficient will be represented in terms of root mean square value and drag coefficient in terms of a mean value. With variations in flow behavior and vortex shredding frequencies, it requires to select an appropriate turbulence model, optimum discretization of fluid domain and time step to obtain an accurate result. Flow simulations are conducted primarily using Unsteady Reynolds Averaged Navier-Stokes Equations (URANS) model and Detached Eddy Simulations (DES) model. Effectiveness in using different turbulence models for specified flow regimes are also explored in comparison to available experimental results. At lower Reynolds numbers, aerodynamic force coefficients for a specified body will only depend on Reynolds number. But after a certain specific Reynolds number, aerodynamic forces are dependent on the Mach number in addition to Reynolds number. Therefore, for higher Reynolds numbers, aerodynamic force coefficients are recorded for multiple Mach numbers with same Reynolds number and will be fed to the neural network. With the development of the machine learning and neural network modelling, many of the fields have nourished and created effective and efficient technologies to ease complex functions and activities. Our goal is to ease the complexity in the computational fluid dynamic field with a deep neural network tool created to predict drag and lift coefficient of elliptical bluff bodies for a given aspect ratio with an acceptable accuracy level. Researchers have developed deep neural network tools to predict various flow conditions and have succeeded with sufficient accuracy and a satisfying reduction of computational cost. In our proposed deep learning neural network, we have chosen to model the network with inputs as the geometry setup and the flow conditions with validated drag and lift coefficients. The model will extract the necessary flow features into filters with the convolution operation performed on the inputs. Our main directive is to create a deep learned neural network tool to predict the target values within an acceptable range of accuracy while minimizing the computation cost.