Multiscale wavelet analysis of 3D Lagrangian trajectories in a mechanically agitated vessel

Savari, Chiya; Li, Kun; Barigou, Mostafa

doi:10.1016/j.ces.2022.117844

Cited by 17 publications

(5 citation statements)

References 51 publications

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“…PEPT has been extensively used to study a variety of flows. More information about the technique, its hardware, and software has been published in our earlier papers. − In a pipe, flow imaging by PEPT usually consists of letting a single particle tracer flow in a closed loop until it maps the whole area of interest, thus requiring a statistically representative number of trajectories, usually >50. , In this study, to enhance data statistics and reduce experimental time, several tracers were sequentially introduced in the flow, thus yielding about 500 trajectories in each experiment. Both the solid and liquid phase were individually tracked in separate consecutive experiments.…”

Section: Methodsmentioning

confidence: 99%

Numerical and Experimental Investigations of Horizontal Turbulent Particle–Liquid Pipe Flow

Yang

Savari

Barigou

2022

Ind. Eng. Chem. Res.

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A Eulerian–Eulerian computational fluid dynamics approach is used in conjunction with appropriate auxiliary models for turbulence and solid dynamic properties to study the complex turbulent flow of particle–liquid suspensions in a horizontal pipe. Numerical simulations of the detailed flow field are fully and successfully validated using a unique experimental technique of positron emission particle tracking. The study includes nearly neutrally buoyant as well as dense particles, ranging from small to large at low to high concentrations, conveyed by a Newtonian liquid. Results are analyzed in terms of radial particle and liquid velocity profiles as well as particle distribution in the pipe. The approach provides predictions with a high degree of accuracy. Particle behavior can be classified into three categories depending on their size and particle–liquid density ratio. An analysis of the forces governing the two-phase flow is used to interpret the phenomena observed.

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Section: Methodsmentioning

confidence: 99%

Numerical and Experimental Investigations of Horizontal Turbulent Particle–Liquid Pipe Flow

Yang

Savari

Barigou

2022

Ind. Eng. Chem. Res.

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“…Full experimental details and protocols can be found in our previous papers. [35][36][37][38] The experimental particle-liquid flow conditions under which the data bank utilized for training the ML algorithm as well as assessing its performance was generated are listed in Table I.…”

Section: Experimental a Experimental Data Bank Acquisitionmentioning

confidence: 99%

Experimentally trained hybrid machine learning algorithm for predicting turbulent particle-laden flows in pipes

Yang,

Li,

Barigou

2023

Physics of Fluids

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A hybrid learning algorithm consisting of a preprocessor, a k-nearest neighbors regressor, a noise generator, and a particle–wall collision model is introduced for predicting features of turbulent single-phase and particle–liquid flows in a pipe. The hybrid learning algorithm has the ability to learn and predict the behavior of such complex fluid dynamic systems using experimental dynamic databases. Given a small amount of typical training data, the algorithm is able to reliably predict the local liquid and particle velocities as well as the spatial distribution of particle concentration within and without the limits of the range of training data. The algorithm requires an order of magnitude less training data than a typical full set of experimental measurements to give predictions on the same level of accuracy (typically, 20 cf. 100 trajectories for phase velocity distribution and 40 cf. 500 trajectories for phase concentration distribution), thus leading to huge reductions in experimentation and simulation. A feature importance analysis revealed the effects of the different experimental variables on the particle velocity field in a two-phase particulate flow, with particle–liquid density ratio and particle vertical radial position being the most influential and particle concentration the least. The algorithm is amenable to extension by using more complex databanks to address a much more comprehensive range of flow situations.

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“…PEPT can be applied to opaque experimental systems, as it relies on the detection of penetrating radiation from a tracer particle. The time-series location data produced by PEPT can also be used to plot high-resolution, time-averaged, or Eulerian maps of flow behaviour (e.g., [1,2]); additionally, it provides detailed Lagrangian paths of the tracer particle (e.g., [3,4]). For the three-phase mixture of mineral froth flotation, PEPT has revealed the paths and time-averaged flow profiles of particles of different mineral species (e.g., [5][6][7][8][9]) and water [10] inside a flotation vessel.…”

Section: Introductionmentioning

confidence: 99%

“…However, questions still remain about whether high-frequency components of flow paths can be tracked effectively with PEPT. Some applications have removed these components as noise to focus on macroscale behaviour (e.g., granular flows [12]), while others suggest that the high-frequency component of the motion displays microscale behaviour (e.g., liquid flows [3]). This discrepancy has potential impact for applications of PEPT: either useful information about particle behaviour is being disregarded unnecessarily or information is output that could be a misinterpretation of the underlying particle behaviour.…”

Section: Introductionmentioning

confidence: 99%

On the Ability of Positron Emission Particle Tracking (PEPT) to Track Turbulent Flow Paths with Monte Carlo Simulations in GATE

et al. 2023

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Positron emission particle tracking (PEPT) has offered important insights into the internal dynamics of multiphase flows. High precision and frequency measurements of the location of the tracer particle are required to resolve individual eddies at the millimetre scale or smaller. To explore the potential of PEPT to perform these measurements, a model was developed of the Siemens ECAT “EXACT3D” HR++ positron emission tomography (PET) scanner at the PEPT Cape Town facility in South Africa with the software Geant4 Application for Tomographic Emission (GATE) and was used to generate Lagrangian tracks from simulations of moving tracer particles. The model was validated with measurements from both experiment and simulation and was extended to two virtual scenarios inspired by turbulent flows. The location data from the simulation accurately captured linear portions of an oscillating path up to high speeds of 25 m s−1; however, tracking tended to undercut the turning points due to the high tracer acceleration. For a particle moving on a spiral path of decreasing radius, the location data tracked the path above a radius of 2.0 mm with an uncertainty equivalent to the radius of the tracer particle, 300 μm. Improvements to the measurement are required to track sub-millimetre flow structures, such as the application of PET scanners with higher spatial resolution and upgrades to the sampling processes used in location algorithms.

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Multiscale wavelet analysis of 3D Lagrangian trajectories in a mechanically agitated vessel

Cited by 17 publications

References 51 publications

Numerical and Experimental Investigations of Horizontal Turbulent Particle–Liquid Pipe Flow

Numerical and Experimental Investigations of Horizontal Turbulent Particle–Liquid Pipe Flow

Experimentally trained hybrid machine learning algorithm for predicting turbulent particle-laden flows in pipes

On the Ability of Positron Emission Particle Tracking (PEPT) to Track Turbulent Flow Paths with Monte Carlo Simulations in GATE

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