59 citations · 160 across the 5 of their papers we have counts for
5 papers
Point-particle drag, lift, and torque closure models using machine learning: hierarchical approach and interpretability
B. Siddani, S. Balachandar
Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest in recent times from dispersed multiphase flow community.…
Rotational and Reflectional Equivariant Convolutional Neural Network for data-limited applications: Multiphase Flow demonstration
Bhargav Sriram Siddani, S. Balachandar, Ruogu Fang
This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles…
Host-to-Host Airborne Transmission As a Multiphase Flow Problem For Science-Based Social Distance Guidelines
S. Balachandar, S. Zaleski, A. Soldati +2
COVID-19 pandemic has strikingly demonstrated how important it is to develop fundamental knowledge related to generation, transport and inhalation of pathogen-laden droplets and th…
Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation
S. Balachandar, W. C. Moore, G. Akiki +1
This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolve…
Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks
B. Siddani, S. Balachandar, W. C. Moore +2
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 presen…