Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data

Magolan, Ben; Baglietto, Emilio; Brown, C. S.; Bolotnov, Igor A.; Tryggvason, Grétar; Lu, Jiacai

doi:10.1016/j.net.2017.08.001

Cited by 15 publications

(7 citation statements)

References 23 publications

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“…The results shown in figures 1 and 2, as well of data from many other simulations, some of which has been made available to other researchers [11], allows us to collect essentially any average and statistical quantity that we desire. This allows us to explore how closure terms, for example, depend on the various average quantities.…”

Section: Resultsmentioning

confidence: 99%

“…Studies of incompressible bubbles in turbulent flows include a demonstration of the importance of deformability for bubble induced drag reduction in turbulent flows [6], and several investigations of turbulent bubbly flows in vertical channels (see [8,9], for example). Although most of those simulations are still at modest Reynolds numbers, a few investigators have started to explore how the data can be used to help improve two-fluid models for the average flow [10,11]. Others have initiated work on using DNS data to help develop large eddy simulation (LES) models for multiphase flows [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct Numerical Simulations of Gas-Liquid Flows

Tryggvason¹,

Lu²,

Ma³

2018

Proceedings of the 10th International Symposium on Cavitation (CAV2018)

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Recent progress in direct numerical simulations (DNS) of gas-liquid flows is discussed. We start by reviewing briefly DNS of cavitating and non-cavitating flows and then address two advanced topics: How to use results for bubbly flows to help generate improved models for the large-scale or average flow, and simulations of flows undergoing massive topology changes. For the first topic we have started to experiment with the use of machine learning methods to extract complex correlations from the DNS data and for the second we are exploring how to diagnose the flow and describe its structure.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direct Numerical Simulations of Gas-Liquid Flows

Tryggvason¹,

Lu²,

Ma³

2018

Proceedings of the 10th International Symposium on Cavitation (CAV2018)

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“…Recently, a significant research effort has been dedicated to the development of more advanced CFD models for bubbly flows. Significant improvements have been made in interface tracking/resolving methods which are able to resolve all the interfacial details of each individual bubble and that are helping to improve understanding of many still poorly known physical details (Santarelli and Fröhlich, 2016;Feng and Bolotnov, 2017;Magolan et al, 2017;Mehrabani et al, 2017;Chen et al, 2019;du Cluzeau et al, 2019). However, these methods are computationally expensive and remain prohibitive when more than a few hundred bubbles are present.…”

Section: Introductionmentioning

confidence: 99%

“…At the present time, the most often adopted strategy consists in adding specific source terms to the turbulence model equations to account for bubble-induced turbulence. Although significant advances have been made in recent years, particularly in modeling based on the conversion of energy from drag to turbulence kinetic energy in bubble wakes (Troshko and Hassan, 2001;Rzehak and Krepper, 2013;Ma et al, 2017;Magolan et al, 2017), turbulence modeling still often relies on the eddy viscosity assumption (Yao and Morel, 2004;Rzehak and Krepper, 2013;Sugrue et al, 2017;Liao et al, 2018). In contrast, second moment closures have only been applied in a few studies (Lopez de Bertodano et al, 1990;Lahey et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

Multi-Fluid Computational Fluid Dynamic Predictions of Turbulent Bubbly Flows Using an Elliptic-Blending Reynolds Stress Turbulence Closure

Colombo

Fairweather

2020

Front. Energy Res.

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The accurate prediction of bubbly flows is critical to many areas of nuclear reactor thermal hydraulics, mainly, but not only, in relation to the key role bubble behavior plays in boiling flows. Large scale computations of flows with hundreds of thousands of bubbles are possible at a reasonable computational cost using computational fluid dynamic, multi-fluid Eulerian-Eulerian models. The main limitation of these models is the need to entirely model interfacial transfer processes with proper closure relations. Here, the capabilities and advantages provided by a model that includes an elliptic-blending Reynolds stress turbulence closure (EB-RSM), allowing fine resolution of the velocity field in the near-wall region, are tested over a large database. This database includes mostly monodispersed bubbly flows over a wide range of operating conditions and geometrical parameters, including upward and downward pipe flows, large diameter pipes and a square duct. The model shows encouraging accuracy and robustness, with good agreement over most void fraction distributions and accurate prediction of the magnitude and position of the near-wall void fraction peak. The model does not include any wall force, avoiding all the related uncertainties, and the prediction of the void fraction peak relies on the fine resolution of the near-wall pressure gradient induced by the turbulence field. Overall, the EB-RSM allows accurate resolution of the velocity and turbulence field near the wall, and the transition to this and similar turbulence closures is of value in assisting the ongoing quest for thermal hydraulic models that are accurate and of general applicability. Additional modifications to the near-wall modeling approach, which is still based on its single-phase counterpart, may be required to deal with high void fraction conditions and, in the overall model, additional improvements to momentum and, most importantly, bubble-induced turbulence closures are desirable.

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“…The results provide a quantitative assessment of the impact of coalescence and breakup events on microbubble behavior, as well as some of the much required additional physical understanding of bubble coalescence and breakup that can be used to underpin the development of better macroscopic modeling closures. In this context, results from highly‐resolved simulations are increasingly being used to improve the interfacial closures implemented in multi‐fluid Eulerian–Eulerian models 37,38 …”

Section: Introductionmentioning

confidence: 99%

Computational modeling of microbubble coalescence and breakup using large eddy simulation and Lagrangian tracking

et al. 2020

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The flow of dispersed microbubbles was studied with an Eulerian-Lagrangian technique using large eddy simulation to predict the continuous liquid flow and Lagrangian tracking to compute bubble trajectories. The model fully accounts for bubble coalescence and breakup and was applied to horizontal and vertical channel flows. With low levels of turbulence, gravity in horizontal, and lift in vertical, channel flows govern the bubble spatial and collision distribution. When turbulence is sufficiently high to, at least partially, oppose bubble preferential concentration, more uniform collision and coalescence distributions are found, although these remain peaked near the wall in both configurations. Almost 100% coalescence efficiency was always found, due to bubbles colliding along similar trajectories, with breakup only recorded in a flow of low surface tension refrigerant R134a. Models like this can provide the required quantitative understanding of the microbubbles complex behavior, as well as supporting the development of more macroscopic modeling closures.

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Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data

Cited by 15 publications

References 23 publications

Direct Numerical Simulations of Gas-Liquid Flows

Direct Numerical Simulations of Gas-Liquid Flows

Multi-Fluid Computational Fluid Dynamic Predictions of Turbulent Bubbly Flows Using an Elliptic-Blending Reynolds Stress Turbulence Closure

Computational modeling of microbubble coalescence and breakup using large eddy simulation and Lagrangian tracking

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