Experimental study on flow control of the turbulent boundary layer with micro-bubbles

Zhao, Penglong; Chen, Yaohui; Dong, Gang; Liu, Yixin; Lyu, Xujian

doi:10.1007/s10409-018-0765-0

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…As a representative of the passive control methods, Wash and Choi et al [13][14][15] achieved considerable drag reduction on riblet surfaces by conducting experiments and direct numerical simulations. In comparison with the passive control method, the active control method has wider adaptability to complex flows, and it enhances the control effectiveness [16][17][18][19], which has been confirmed by several experimental and simulation investigations [20][21][22][23][24][25][26][27][28][29]. Bai et al [2] used a spanwisealigned array of piezoceramic actuators to generate a transverse traveling wave along the wall surface.…”

Section: Introductionmentioning

confidence: 92%

Active control of multiscale features in wall-bounded turbulence

et al. 2019

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This study experimentally investigates the impact of a single piezoelectric (PZT) actuator on a turbulent boundary layer from a statistical viewpoint. The working conditions of the actuator include a range of frequencies and amplitudes. The streamwise velocity signals in the turbulent boundary layer flow are measured downstream of the actuator using a hot-wire anemometer. The mean velocity profiles and other basic parameters are reported. Spectra results obtained by discrete wavelet decomposition indicate that the PZT vibration primarily influences the near-wall region. The turbulent intensities at different scales suggest that the actuator redistributes the near-wall turbulent energy. The skewness and flatness distributions show that the actuator effectively alters the sweep events and reduces intermittency at smaller scales. Moreover, under the impact of the PZT actuator, the symmetry of vibration scales' velocity signals is promoted and the structural composition appears in an orderly manner. Probability distribution function results indicate that perturbation causes the fluctuations in vibration scales and smaller scales with high intensity and low intermittency. Based on the flatness factor, the bursting process is also detected. The vibrations reduce the relative intensities of the burst events, indicating that the streamwise vortices in the buffer layer experience direct interference due to the PZT control.

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Section: Introductionmentioning

confidence: 92%

Active control of multiscale features in wall-bounded turbulence

et al. 2019

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“…Obviously，from (2)(3)(4)(5)(6)(7)(8) and (2)(3)(4)(5)(6)(7)(8)(9)(10)(11), it is easy to obtain the approximation function of the second partial derivative of the temperature field in the x direction and y direction on the Chevbyshev-Gauss-Lobatto configuration point.…”

Section: Iterative Algorithm Of Two-dimensional Central Difference Ga...mentioning

confidence: 99%

“…Currently, the research on two-dimensional parabolic partial differential equations is more and more significant, whose important mathematical structure model and strong physical background have attracted many scholars' attention. It has been widely used in practical engineering problems [1][2][3][4][5][6], especially in the numerical analysis of unsteady flow and heat transfer [7][8][9][10][11][12].The spectral method is one of the numerical solutions of partial differential equations. It has unique weighted residual method and higher calculation accuracy, which is incomparable to the conventional finite element method (FEM), finite volume method (FVM) and finite difference method (FDM).…”

Section: Introductionmentioning

confidence: 99%

“…to the iterative scheme(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) of the central difference Galerkin spectral method, the numerical solution of the classical collocation point spectral method is calculated when t=8s and the time step t  is taken as ， we make figures, which are shown in Fig.8-2to8-5 respectively.Secondly, the numerical solution of the first L=20 steps at time position node   spectral method iteration format(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16).We can construct sample matrix At this time, we can know that the eigenvector of matrixT shown in Fig.8-1, only the first five POD bases can meet the actual accuracy requirements. It is known that it is not necessary to reselect the POD base，at this time, we can calculate the numerical approximate solution of the reduced dimension spectrum method in the time steps of then, we can draw their figures, which are shown in Fig.…”

mentioning

confidence: 99%

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Numerical Analysis of Transient Non-linear Heat Transfer by Spectral Method Based on POD Reduced-order Extrapolation Algorithm

Wang

2023

Preprint

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In order to meet the requirements of high accuracy and fast algorithm for numerical heat transfer simulation, a POD reduced-order extrapolation algorithm based on the iterative format of the classical central difference Gakerkin spectral method is proposed for solving two-dimensional transient heat conduction problems. Using the calculation results of classical central difference Gakerkin spectral method as sample data constructs POD reduced-order spectral method model; A study on numerical algorithm characteristics of flow and heat transfer using partial differential equation as mathematical model, and gives the error estimate; Finally, taking different time steps as parameters to conduct experimental simulation in turn. The results show that: the spectral method based on POD reduced-order extrapolation method is at least 253 times faster than the central difference Gakerkin spectral method, and gradually decreases with the interval of variable parameters. The calculation speed of POD reduced-order model and the calculation speed of central difference are both reduced. However, the average relative error between the temperature field reconstructed by POD reduced dimension model and the central difference result is smaller and the calculation accuracy is higher, the maximum value is not more than 0.11%.The average computational efficiency is 458 times higher than that of the traditional central difference Gakerkin spectral method.

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“…The interaction between microbubbles and the liquid boundary layer is one of the methods to obtain the DR effect . The addition of microbubbles in the boundary layer can change the spatial–temporal spectral characteristics of the flow structure, affect the frictional drag during fluid transportation, and change the mass transfer and heat transfer efficiency. − This method is widely used in underwater weapons, submarines, torpedoes, and other fields. , In recent years, a CAF technology combined with the idea of microbubble DR has been proposed for heavy oil transportation . This new technology considers aqueous foam instead of a water film lubricating core oil.…”

Section: Drag Reduction By Boundary Injection Of Low-viscosity Fluidmentioning

confidence: 99%

Drag Reduction Related to Boundary Layer Control in Transportation of Heavy Crude Oil by Pipeline: A Review

Jing,

Guo,

Karimov

et al. 2023

Ind. Eng. Chem. Res.

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With the depletion of conventional light crude oil, heavy crude oil will occupy an increasing share of the energy structure in the 21st century. Heavy crude oil is characterized by an API gravity of 10 to 22.3 or a viscosity of 0.09 to 10 Pa·s. The flow of heavy crude oil in the pipeline is laminar in the higher viscosity range and turbulent in the lower viscosity range, and its flow resistance comes from the viscous force between the oil and the wall or the additional stress of turbulent flow. Hence, pipeline transportation of heavy crude oil is faced with a huge loss of frictional resistance, which means a reduction of the transportation efficiency. To decrease pump power consumption, improving the transportation efficiency of heavy crude oil pipelines is a key factor. In recent years, some biomimetic technologies that reduce the flow resistance of viscous oils have made new progress in improving their fluidity and have not yet been put into use in commercial pipelines. Therefore, it is important and appropriate to discuss the breakthrough achievements and progress made by researchers in the drag reduction (DR) of heavy crude oil and to summarize its advantages, disadvantages, and potential problems. This review discusses boundary layer control methods for heavy crude oil drag reduction. First, conventional DR technologies, such as polymers, surfactants, fiber suspensions, oil–water core annular flow (CAF) and oil–aqueous foam CAF, and potential DR technologies, including oleophobic surfaces, flexible walls, biomimetic microgrooves, and ferrofluid annular DR under magnetic confinement, are presented. Second, the mechanism of DR is investigated and summarized; the highlights and progress of the technology are reviewed, and new ideas to improve the existing DR technology are proposed. Finally, the challenges and prospects of DR are presented.

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Experimental study on flow control of the turbulent boundary layer with micro-bubbles

Cited by 8 publications

References 17 publications

Active control of multiscale features in wall-bounded turbulence

Active control of multiscale features in wall-bounded turbulence

Numerical Analysis of Transient Non-linear Heat Transfer by Spectral Method Based on POD Reduced-order Extrapolation Algorithm

Drag Reduction Related to Boundary Layer Control in Transportation of Heavy Crude Oil by Pipeline: A Review

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