DrSivaramakrishnan Balachandar

Center for Multiscale Modeling of Multiphase Combustion - A PSAAP-IV Predictive Science Center (PSC)

Machine Learning-Driven Decoding of Thin Liquid Film Hydrodynamics in Microchannel Flow Boiling

EAGER: Machine Learning-Driven Prediction of Transition to Elongated Bubbles Flow Regime

Collaborative Research: CDS&E: Data-driven next-generation Lagrangian models for swarms of deforming and interacting droplets

Machine Learning Models of Particle-Fluid-Particle Interactions

Elucidating Post-Detonation Particle-Turbulence Interactions

Hindcasting and forecasting geotechnical operational windows of seabed mobility and scour based on a global windfarm GIS, CFD modelling and ML/AI

Modeling of High-speed Multiphase Blast Effects

Improved Physics Modeling for Sand Particulate Tracking and Deposition in Gas Turbine Engines

Understanding Hydrodynamic Instabilities of Explosively-Driven Multiphase Fronts For Accurate Prediction of Their Complex Structures

Advancing the State of the Art in Particulate Dispersal Modeling in Underwater Explosion Application

Advancing the State of the Art in Particulate Dispersal Modeling in Underwater Explosion Applications through Physics Based Simulations Continuation Proposal

Predictive Methods for Emerging Ordnance Technologies

Ocean Energy Safety Institute 2

Estimation of Airborne Virus Transmission for LG Indoor Air-conditioner Systems

EAGER: Accurate Estimation of Indoor Airborne Virus Transmission based on a Novel Multiscale Data-Driven Framework

High-Speed Multiphase Interactions

Development of models for turbulent mixing, afterburning, and soot formation.

The development of methods and models including validation experiments and instrumentation to provide high quality data on multiphase blast properties and structural responses to structures

LES Predictions of Detonator Fireballs

Machine Learning & PIEP Model for High-Speed Multiphase Flows

(NURP) Advancing the State-of-the-Art in Particulate Dispersal Modeling in Underwater Explosion Applications through Physics-Based Simulations

(NURP) Advancing the State-of-the-Art in Particulate Dispersal Modeling in Underwater Explosion Applications through Physics-Based Simulations

Parallel Semi-supervised Machine Learning for Volumetric Datasets- EAGER

DEVELOPMENT OF MULTISCALE COUPLING TECHNIQUES FOR MODELING COMPRESSIBLE MULTIPHASE FLOWS