Numerical simulation of drag reduction by microbubbles in a vertical channel

Velasco, Lívio José; Venturi, Diego Nei; Fontes, Douglas; Souza, Francisco José de

doi:10.1016/j.euromechflu.2021.12.007

Cited by 9 publications

(5 citation statements)

References 38 publications

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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%

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

confidence: 99%

“…112−114 This method is widely used in underwater weapons, submarines, torpedoes, and other fields. 115,116 In recent years, a CAF technology combined with the idea of microbubble DR has been proposed for heavy oil transportation. 117 This new technology considers aqueous foam instead of a water film lubricating core oil.…”

Section: Oil−aqueous Foam Cafmentioning

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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“…But before going into detail, let's quickly come back to this concept called neural networks [21][22][23] A neural network extracts nonlinear patterns from data. it is therefore a set of interconnected formal neurons allowing the resolution of complex problems such as pattern recognition or natural language processing, thanks to the adjustment of the weighting coefficients in a learning phase [24][25][26][27][28].A neural network is inspired by the functioning of biological neurons and takes shape in a computer in the form of an algorithm. The neural network can modify itself according to the results of its actions, which allows learning and problem solving without an algorithm, therefore without classical programming [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

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One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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“…Selected subsequent studies include contributions by the type of drag-reducing agents (DRAs). These DRAs include DRPs 9 – 12 , surfactants 13 – 16 , fibers 17 , and microbubbles 18 . Most of them considered the effect of sudden injection of DRAs, either by homogenous or heterogenous method, while others attempted to find a correlation expression for the percentage drag reduction (%DR) achieved.…”

Section: Introductionmentioning

confidence: 99%

Experimental insights into energy savings and future directions of drag reducing polymers in multiphase flow pipelines

Alsurakji

Al–Sarkhi²,

El‐Qanni

et al. 2023

Sci Rep

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Frictional pressure drop has been grasping the attention of many industrial applications associated with multi-phase and academia. Alongside the United Nations, the 2030 Agenda for Sustainable Development calls for the exigency of giving attention to economic growth, a considerable reduction in power consumption is necessary to co-up with this vision and to adhere to energy-efficient practices. Thereinto, drag-reducing polymers (DRPs), which do not require additional infrastructure, are a much better option for increasing energy efficiency in a series of critical industrial applications. Therefore, this study evaluates the effects of two DRPs—polar water-soluble polyacrylamide (DRP-WS) and nonpolar oil-soluble polyisobutylene (DRP-OS)—on energy efficiency for single-phase water and oil flows, two-phase air–water and air-oil flows, and three-phase air–oil–water flow. The experiments were conducted using two different pipelines; horizontal polyvinyl chloride with an inner diameter of 22.5 mm and horizontal stainless steel with a 10.16 mm internal diameter. The energy-efficiency metrics are performed by investigating the head loss, percentage saving in energy consumption (both per unit pipe length), and throughput improvement percentage (%TI). The larger pipe diameter was used in experiments for both DRPs, and it was discovered that despite the type of flow or variations in liquid and air flow rates, there was a reduction in head loss, an increase in energy savings, and an increase in the throughput improvement percentage. In particular, DRP-WS is found to be more promising as an energy saver and the consequent savings in the infrastructure cost. Hence, equivalent experiments of DRP-WS in two-phase air–water flow using a smaller pipe diameter show that the head loss drastically increases. However, the percentage saving in power consumption and throughput improvement percentage is significantly compared with that found in the larger pipe. Thus, this study found that DRPs can improve energy efficiency in various industrial applications, with DRP-WS being particularly promising as an energy saver. However, the effectiveness of these polymers may vary depending on the flow type and pipe diameter.

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Numerical simulation of drag reduction by microbubbles in a vertical channel

Cited by 9 publications

References 38 publications

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

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

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Experimental insights into energy savings and future directions of drag reducing polymers in multiphase flow pipelines

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