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Understand the mystery of why particles gather in turbulence

Kyle Pietrsik, a graduate student at Stanford University and working at LLNL as a summer intern in high-density physics in 2020, is the lead author of a study investigating why particles collect in turbulence.Credits: Lawrence Livermore National Laboratory

A new study conducted at the Lawrence Livermore National Laboratory (LLNL) analyzes isotropic turbulence containing three-dimensional particles to gain a better understanding of the dynamics of inertial particles in terms of kinetic energy.


Particle-bearing streams can be found in a variety of applications, including raindrops in clouds, wind dune sprays, fluidized bed reactors, and soot formation during the combustion process. For over 30 years, it has been established that particles do not move with the surrounding flow and tend to form clusters.

This work was selected as a feature article in the January issue. Fluid physics Co-authored by Jeremy Horwitz, Fady Najjar, and Roger Minich, a graduate student at Stanford University who was the lead author of LLNL as a summer intern in high-density physics in 2020 and a staff member of LLNL’s Design Physics Department. author.

“In the context of engineering, such as internal combustion engines, these clusters can reduce system efficiency by slowing down mixing and heat transfer, while in the atmospheric context, clustering causes small droplets to collide. It forms a large droplet. This is necessary for the droplet. It effectively falls from the clouds to the ground. ” “Therefore, the study of particle clustering remains an active field of study, and this study provides further physical insight into how and why clusters are formed.”

Pietrzyk conducted the study using a database of isotropic turbulence containing 3D particles from Stanford University. The team investigated particle clustering by studying isotropic turbulence containing particles. This is a matter of model that captures many physical processes that exist in clouds.The team also observed particle accumulation in low flow areas. Physical energy over time.

The paper outlines two important findings

Pietrzyk said in the first important finding that researchers would observe consistent particle behavior in a simulation of isotropic turbulence containing 3D particles over a range of Stokes numbers. This is the ratio of particle response time to the characteristic time scale of the fluid.

The team describes this behavior in the following steps: Particles that lose kinetic energy are common in the area of ​​low flow kinetic energy. Due to their energy loss, these particles will slowly begin to move in the low flow kinetic energy region, where they will spend the most time. Ultimately, particles spend more time statistically in such regions than in other regions of the flow, so behaviors such as accumulation in low-flow kinetic energy regions are displayed over time. ..

This behavior was extracted from the statistical evidence provided in the publication.

The team also provides supporting evidence by analyzing the kinetic energy equations of the derived particles. The team has shown that a single snapshot of turbulence may not be sufficient to observe behavior, especially at high Stokes numbers, but over time, statistics have been suggested by the team. It suggests that the behavior is maintained.

The second discovery is a probabilistic model such as the Fokker-Planck equation for the kinetic energy probability density function of a particle. The team investigated the colored noise of this model and used physically valid parameters to fit the model to the simulation data.Overall, this study provides a perspective on particle clustering in turbulence from a lens of motion. energy..

“We hope this will help us in our future work and encourage further research into prioritized focus from new perspectives,” says Najjar.

This study is of great benefit to LLNL as it is the key to understanding how turbulence affects particle evolution and clustering in complex multiphase flows. This effort provides an important component of predictive calculation tools for modeling detailed particle turbulence mixing processes.

“Particle clustering poses star and galaxy formation, sediment flow and erosion on riverbeds, as well as visibility and health risks, and in sandstorm applied research that can damage vehicle engines. It is considered a matter of basic research, “says Howitz.

Horwitz said it is worth emphasizing the benefits of this work from a communication perspective.

“The internship was completed in an unusual and stressful situation given the COVID pandemic,” Horwitz said. “The ability of Kyle and LLNL researchers to maintain regular communication, conduct rigorous research, and finally publish findings is a testament to Kyle’s determination, and everyone in the lab has these difficulties. It is a symbol of the widespread efforts that have contributed to the time. This is just one example. Despite these challenges, countless other LLNL staff members step up to reach the goals of the program. ”


A dimensionless mystery with great effects


For more information:
Kyle Pietrzyk et al, on the analysis and probabilistic modeling of particle kinetic energy equations in isotropic turbulence containing particles. Fluid physics (2022). DOI: 10.1063 / 5.0075650

Quote: Understand the mystery of why particles cluster in turbulence (February 16, 2022) February 16, 2022 https://phys.org/news/2022-02-mystery-particles-cluster-turbulent Get from .html

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Understand the mystery of why particles gather in turbulence

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