Turbulence with Large Thermal and Compositional Density Variations

Livescu, Daniel

doi:10.1146/annurev-fluid-010719-060114

Cited by 68 publications

(68 citation statements)

References 143 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, these studies have allowed broadly to validate and verify turbulence mix-models [24][25][26][27][28][29][30][31][32] for buoyancydriven flows and provide insights to solve several long-standing problems in the field. The developments in modeling and simulations in RTI can be found in a companion review by Schilling [33], a review of the theoretical modeling techniques and issues related to variable-density flows with large thermal and density fluctuations can be found in [34]. No attempt is also made to cover the experiments related to the formation of RTI in the highenergy-density regime or RTI due to change in viscosity or chemical reactions; the reader can find those in other recent reviews [35,36].…”

Section: Introductionmentioning

confidence: 99%

Rayleigh-Taylor Instability: A Status Review of Experimental Designs and Measurement Diagnostics

Banerjee

2020

Journal of Fluids Engineering

View full text Add to dashboard Cite

The focus of experiments and the sophistication of diagnostics employed in Rayleigh Taylor Instability (RTI) induced mixing studies have evolved considerably over the past seven decades. The first theoretical analysis by Taylor and experimental results by Lewis on RTI in 1950 examined two-dimensional, single-mode RTI using conventional photographic techniques. Over the next 70 years, several experimental designs have been used to creating an RTI unstable interface between two materials of different densities. These early experiments though innovative, were arduous to diagnose and provided little information on the internal, turbulent structure and initial conditions of the RT mixing layer. Coupled with the availability of high-fidelity diagnostics, the experiments designed and developed in the last three decades allow detailed measurements of various turbulence statistics that have allowed broadly to validate and verify late-time non-linear models and mix-models for buoyancy-driven flows. Besides, they have provided valuable insights to solve several long-standing disagreements in the field. This review serves as an opportunity to discuss the understanding of the RTI problem and highlight valuable insights gained into the RTI driven mixing process through experiments. For the current review, the focus is on low to high Atwood number (> 0.1) experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

Rayleigh-Taylor Instability: A Status Review of Experimental Designs and Measurement Diagnostics

Banerjee

2020

Journal of Fluids Engineering

View full text Add to dashboard Cite

show abstract

“…The governing equations in the variable-density incompressible and compressible approximation used in simulations of miscible RT mixing experiments are briefly summarized here (summation over repeated indices is implied) (see Ref. [88]). In binary mixing the sum of the mass fractions is m 1 þ m 2 ¼ 1.…”

Section: Numericalmentioning

confidence: 99%

“…The compressible equations expressing mass, momentum, internal energy, and species conservation solved numerically for ideal gases are[88,91,92] Eqs. (22)-(24) together with…”

mentioning

confidence: 99%

Progress on Understanding Rayleigh–Taylor Flow and Mixing Using Synergy Between Simulation, Modeling, and Experiment

Schilling

2020

Journal of Fluids Engineering

View full text Add to dashboard Cite

Simultaneous advances in numerical methods and computing, theoretical techniques, and experimental diagnostics have all led independently to better understanding of Rayleigh-Taylor (RT) instability, turbulence, and mixing. In particular, experiments have provided significant motivation for many simulation and modeling studies, as well as validation data. Numerical simulations, in turn, have also provided data not currently measurable or very difficult to measure accurately in RT unstable flows. Thus, simulations have motivated new measurements in this class of buoyancy-driven flows. This overview discusses simulation and modeling studies synergistic with experiments and examples of how experiments have motivated simulations and models of RT instability, flow, and mixing. First, a brief summary of measured experimental and calculated simulation quantities, of experimental approaches, and of issues and challenges in the simulation and modeling of RT experiments is presented. Implicit large-eddy, direct numerical, and large-eddy simulations validated using RT experimental data are then discussed. This is followed by a discussion of modeling using analytical, modal, buoyancy-drag, and turbulent transport models of RT mixing experiments. The discussion will focus on three-dimensional RT mixing arising from multimode perturbations. Finally, this review concludes with a perspective on future simulation, modeling, and experimental directions for further research. Research in simulation and modeling of RT unstable flows, coupled with experiments, has made significant progress over the past several decades. This overview serves as an opportunity to both discuss progress and to stimulate future research on simulation and modeling of this class of hydrodynamically-unstable turbulent flows.

show abstract

“…Aside from the fact that viscosity and molecular diffusion are not accounted for in the model -in which case the spectra would become time-dependent functions after the passage of the detonation (Sinha 2012;Sethuraman & Sinha 2020), the small-scale regime must be taken with caution as the thin-detonation hypothesis may not be fulfilled. Therefore, the properties of the turbulent flow behind the detonation front predicted by the linear interaction theory should be further extended to include the effects of finite reaction lengths, as noted in and , and nonlinear contributions, as noted in Larsson & Lele (2009), Prakash & Raman (2019), Livescu (2020) and Tian et al (2020). The latter is unavoidable if multi-phase environments are under consideration (Watanabe et al 2019(Watanabe et al , 2020.…”

Section: Turbulence Scales Characterizationmentioning

confidence: 99%