Name: Ishan Srivastava
Pronouns:
Biography:
Ishan Srivastava is a Research Scientist in the Applied Mathematics Department within the Computing Sciences Area at Lawrence Berkeley National Laboratory, and affiliated with the Center for Computational Sciences and Engineering (CCSE). His research aims to apply theory and simulation to understand the structure and dynamics of multiphase flows and complex fluids. A particular area of interest is the physics of granular materials and non-Newtonian fluid mixtures, which are of enormous technological and natural importance. His research uses a variety of computational methods such as molecular dynamics, discrete element method, and continuum fluid and solid modeling, along with data-driven methods for multiscale coupling. Such a multiscale approach to simulate these materials allows the identification of particle scale processes that govern macroscale material behavior. The overarching goal is to develop novel computational methods in particle-scale modeling to predict microstructure-aware constitutive relationships that eventually inform the continuum modeling of multiphase flows and complex fluids. Some motivating applications for his research include advanced manufacturing, bioreactor efficiency optimization, and various DOE mission areas involving energy and environment.
Institution/Lab: Lawrence Berkeley National Laboratory
Website: https://ccse.lbl.gov/people/isriva/index.html
SRP Collaboration Topic/Title: Multiscale Modeling of Complex Fluids and Multiphase Flows
Field or research area: Computational Fluid Dynamics
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.); High-Performance Computing; Machine Learning and AI
Brief Abstract:
We are developing numerical algorithms and computational models to simulate the dynamics of particulate materials, complex fluids and multiphase flows. The proposed approach involves integrating a particle-scale representation such as the discrete element method (DEM), a coarse-grained representation such as particle-in-cell (PIC), and a continuum-scale PDE representation of these materials using the tools of adaptive mesh and algorithm refinement, and data-driven machine learning methods. The overarching goal will be a multiscale modeling framework that can simulate a wide variety of particulate materials and complex fluids, and is performant on manycore/GPU-based high performance computing (HPC) platforms. Another goal of this project is to conduct large-scale simulations of complex fluids and multiphase flows on HPC platforms in application spaces such as bioreactors and advanced manufacturing.
Desired relevant skills, background, or interests:
Basic knowledge of C++ programming and Python scripting. Background in Applied Mathematics, Physics, or Physical Sciences/Engineering. Basic understanding of applied mathematics, computational methods, scientific computing, and fluid dynamics.
Other comments:
Do any special requirements apply? In-Person Only; Permanent Resident OK; International OK
Other, specify:
Keywords:
multiscale modeling; complex fluids; multiphase flows; non-Newtonian fluids; discrete element method; particle-in-cell; fluctuating hydrodynamics; microscale flows
Lightning Talk Title: Multiscale Modeling of Complex Fluids and Multiphase Flows