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Sivaramakrishnan Balachandar - University of Florida. Gainesville, FL, US

Sivaramakrishnan Balachandar

Distinguished Professor | University of Florida

Gainesville, FL, UNITED STATES

Balachandar studies data-driven solutions of industrial and environmental multiphase flow processes from volcanic eruptions to fuel spray.


Sivaramakrishnan Balachandar is the Ebaugh Professor and a distinguished professor in the Herbert Wertheim College of Engineering. He is the founding director of the college's Institute for Computational Engineering. He was the director of the Center for Compressible Multiphase Turbulence. He studies data-driven solutions of industrial and environmental multiphase flow processes from volcanic eruptions to fuel spray.

Areas of Expertise (8)

Large-Scale Simulations

Shock-Particle Interaction

Machine Learning for Multiphase Flow

Multiphase Flow

Turbulent Flow

Environmental Flows

High-Speed Multiphase Flows

Petascale and Exascale Computing

Media Appearances (3)

A River Runs Under It

UF News from Herbert Wertheim College of Engineering  online


“Rivers are sacred to humans because they deliver sediments and fresh soil to grow our food,” says Dr. S. “Bala” Balachandar, William F. Powers Professor and Distinguished Professor in the Department of Mechanical and Aerospace Engineering at the UF Herbert Wertheim College of Engineering. “Under-ocean turbidity currents serve as underwater rivers that transport sediments to sites where they can be useful.

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After a sneeze, 6 feet may not be enough to keep you safe from coronavirus

Tampa Bay Times  online


As a doctoral engineering student, Kai Lui had never really thought about what the invisible spray of a human sneeze looks like. But that’s what he spends most of his time analyzing lately. Working from his apartment, he punches numbers into a program linked to a supercomputer at the University of Florida. He follows a list of equations developed by an international team of researchers, tweaking measures to simulate how saliva particles move through the air.

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6-foot social distancing won't protect you from a sneeze, scientists find

The Week  online


Everything you've been told about social distancing was most certainly not a lie. But it could use some revisions. Health officials have been recommending people keep a 6-foot distance from others throughout the coronavirus pandemic, saying that will help stop the spread of COVID-19. But scientists have found that may not be far enough, especially when sneezes are involved, and are working on a new formula that will could keep everyone even safer, the Tampa Bay Times reports.

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Articles (3)

Persistent reshaping of cohesive sediment towards stable flocs by turbulence

Scientific Reports

Minglan Yu, et. al


Cohesive sediment forms flocs of various sizes and structures in the natural turbulent environment. Understanding flocculation is critical in accurately predicting sediment transport and biogeochemical cycles. In addition to aggregation and breakup, turbulence also reshapes flocs toward more stable structures. An Eulerian–Lagrangian framework has been implemented to investigate the effect of turbulence on flocculation by capturing the time-evolution of individual flocs.

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Improved guidelines of indoor airborne transmission taking into account departure from the well-mixed assumption

Physical Review Fluids

Jorge S. Salinas, et. al


The well-mixed assumption has been widely used in predicting the spread of infectious diseases in indoor spaces. It is to be expected that a perfect well-mixed state will not be achieved in an indoor space at any reasonable level of ventilation. This work evaluates the well-mixed assumption by comparing the theory with results from large eddy simulations. The robustness of the well-mixed theory is established by comparing at four different levels.

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Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks

Theoretical and Computational Fluid Dynamics

B. Siddani, et. al


Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper, we present a machine learning methodology using generative adversarial network framework and convolutional neural network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles.

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