Performance characteristic curve insensitive to feedback fluidic oscillator configurations

Xu, Cong; Meng, Xiangda

doi:10.1016/j.sna.2012.09.031

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…The observed values of the Strouhal number are shown as a function of Reynolds number in Figure . It can be seen that the Strouhal number is almost independent of Reynolds number, which is in agreement with the published information . The average value of the Strouhal number was found to be 0.0026.…”

Section: Resultssupporting

confidence: 91%

“…From a liquid‐liquid extraction perspective, Xu and coworkers have contributed many such innovative designs. However, any attempts to further enhance the design effectiveness by fundamentally studying the flow features, are missing . It is essential to compare different designs and quantify the influence of key design and operating parameters on flow, mixing, residence time distributions, and heat transfer so that useful guidelines, necessary for expanding applications of these devices, may be developed.…”

Section: Introductionmentioning

confidence: 99%

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Flow, mixing, and heat transfer in fluidic oscillators

et al. 2018

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There is an increasing emphasis on process intensification and development of compact, intensified reactors and separators in recent years. Significant efforts are being made to develop such intensified reactors and separators without any moving parts. Some of the recent research studies have proven that a liquid‐liquid extractor based on the Coanda effect and feedback oscillations exhibit excellent mixing and liquid‐liquid contacting. These fluidic oscillators can potentially be used for a variety of other multiphase reactions and systems demanding enhanced mixing and heat and mass transfer. In this work, we have computationally investigated flow, mixing, and heat transfer in fluidic oscillators based on the Coanda effect. Available information on flow and mixing in fluidic oscillators was critically reviewed and key gaps in the available knowledge with respect to the design and optimization of fluidic oscillators were identified. Computational flow models were developed to characterize key flow features like unsteady flows, secondary vortices, and internal recirculation over a range of Reynolds number (Re = 90–1538) for three different oscillator designs. Systematic numerical studies were carried out to quantify different flow regimes, oscillations, and the influence of key geometric parameters on flow, mixing, and heat transfer. Simulated results were critically analyzed and are presented in the form of dimensionless numbers. The approach and results presented in this work will provide useful insights and a systematic basis for extending the applications of the Coanda‐based feedback oscillatory devices for a wide range of engineering applications.

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Flow, mixing, and heat transfer in fluidic oscillators

et al. 2018

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“…Kruger et al 38 performed a 2D simulation with the URANS SST k−ω turbulence model, and it was found that the model was sufficient to quantitatively and qualitatively describe the flow field and dynamics. Xu and Meng 39 studied the effect of different geometrical parameters on the performance of the FO and related jet oscillation frequency f to the Reynold number, Re.…”

Section: Introductionmentioning

confidence: 99%

“…Krüger et al performed a 2D simulation with the URANS SST k –ω turbulence model, and it was found that the model was sufficient to quantitatively and qualitatively describe the flow field and dynamics. Xu and Meng studied the effect of different geometrical parameters on the performance of the FO and related jet oscillation frequency f to the Reynold number, Re . Ostermann et al experimentally compared the flow dynamics of curved FO and angled FO and concluded that the curved flow oscillator performed better due to slightly more entrainment than the angled FO.…”

Section: Introductionmentioning

confidence: 99%

Jet Oscillations and Mixing in Fluidic Oscillators: Influence of Geometric Configuration and Scale

Madane

Ranade

2023

Ind. Eng. Chem. Res.

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Fluidic oscillators (FOs) harness inherently oscillating jets for improving mixing, heat, and mass transfer and offer an attractive device platform for a wide range of applications. Despite several promising applications of FOs, the influence of their design and scale on the resulting jet oscillations and mixing has not been systematically studied. In this work, we investigated the influence of geometric configuration and device scale on jet oscillation frequency, flow, mixing, and residence time distribution (RTD). Initially, flow in FO was simulated using a two-dimensional computational fluid dynamics (CFD) model for exploring a wider parameter space. These simulations indicated that the distance of the backflow limb from the inlet and device scale are the most important design parameters influencing the jet oscillation frequency. Detailed three-dimensional CFD simulations were then carried out for the shortlisted configurations of the FO. The shortlisted configurations were fabricated, and experimental measurements of jet oscillations were carried out. The jet oscillation frequency was measured using acquired pressure fluctuation data and fast Fourier transforms. The simulated results were compared with the experimental data. The validated CFD model was then used to evaluate various flow features of FO, mixing, and liquid and solid RTD considering potential applications. The presented results will be useful for identifying suitable design and operating parameters of the FO for a variety of processes and applications.

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“…The findings suggested that this type of fluidic oscillator generated oscillation frequencies of up to 8.2 kHz. Xu and Meng (2013) performed an experiment to investigate the effect of fluidic oscillator configuration on the oscillation frequency. The results indicated that the frequency was dependent on the exit nozzle width, feedback channel width, and chamber shape.…”

Section: Introductionmentioning

confidence: 99%

Shape optimization of a feedback-channel fluidic oscillator

Jeong

Kim

2017

Engineering Applications of Computational Fluid Mechanics

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In this study, a fluidic oscillator was optimized based on the three-dimensional unsteady Reynolds-averaged Navier-Stokes analysis to enhance peak jet velocity at the outlet and simultaneously reduce pressure drop. A multi-objective genetic algorithm performed the optimization with surrogate modeling. The ratios of the inlet nozzle width and the distance between the splitters to the throat width were chosen as the design variables. And, two objective functions related to peak jet velocity at the outlet and pressure drop through the fluidic oscillator were selected for the optimization. Ten design points were selected in the design space using a Latin hypercube sampling method; the objective functions were calculated by unsteady Reynolds-averaged Navier-Stokes analysis at these design points to construct surrogate models that were used to approximate the objective functions. Two different surrogate models, namely response surface approximation and Kriging models were tested. Pareto-optimal front representing a compromise between the two objective functions was obtained from the multi-objective optimization. The optimization results indicated that a jet velocity-oriented optimum design increased the peak jet velocity ratio at the outlet and the friction factor by 11.18% and 16.82%, respectively, when compared to those of a friction factor-oriented design.

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Performance characteristic curve insensitive to feedback fluidic oscillator configurations

Cited by 15 publications

References 13 publications

Flow, mixing, and heat transfer in fluidic oscillators

Flow, mixing, and heat transfer in fluidic oscillators

Jet Oscillations and Mixing in Fluidic Oscillators: Influence of Geometric Configuration and Scale

Shape optimization of a feedback-channel fluidic oscillator

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