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Michele Rosso

MIchele Rosso
Project Scientist
Fax: +1 510 486 6900

I am a project scientist in the Center for Computational Sciences and Engineering (CCSE) in the Computational Research Division of the Computing Sciences Directorate at the Lawrence Berkeley National Laboratory.

My research focuses on the development of a high-fidelity computational tool, called MFiX-Exa capable of efficiently simulating large-scale Chemical Looping Reactors (CLR).

CLR is a promising technology designed to increase combustion efficiency while capturing the Carbon Dioxide (CO2) produced by the combustion process. Large-scale commercial deployment of CLR technology will require an understanding of how to scale laboratory designs to industrial sizes. The direct-scale up of lab-scale reactors is known to be unreliable, thus requiring building and testing physical systems at increasingly larger intermediate scales.

MFiX-Exa is aimed at impacting the CLR design early in the developmental process to reduce the costs and development time associated with experiments-only design. To date, available computational tools have been mostly focused on the validation and the development of physical models in the context of a relatively basic computational framework. Consequently, existing software have allowed to simulate small-scale reactors containing millions of solid particles, whereas pilot-scale and large-scale reactors contain billions of particles.

MFiX-Exa is designed to efficiently leverage the computational power of the next generation of exascale supercomputers, i.e. supercomputer capable of performing billions of billions calculations per second. This is achieved on the one hand by exploiting the latest high-performance computing technologies, and on the other hand by developing highly efficient numerical algorithms for the solution of the physical models involved.

The long-term goal is to be able to simulate several minutes of physical time of a multiphase reactor containing several billions of particles in an acceptable amount of time. The capability to model billions of reacting particles will greatly aid in the design and optimization of CLR technology.