Roberto Porcu

Postdoctoral Researcher, Computational Research Division

Contact Information

Roberto Porcu
MS 50A-3111
Lawrence Berkeley National Lab
1 Cyclotron Rd.
Berkeley, CA 94720

Affiliation and Research Interests

I am a postdoctoral researcher in the Center for Computational Sciences and Engineering (CCSE) in the Computational Research Division of the Computing Sciences Area 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.


  • R. Porcu, E. Miglio, N. Parolini, M. Penati, and N. Vergopolan, "GPU parallelization of brownout simulations with a non-interacting particle dynamics model", The International Journal of High Performance Computing Applications, In publication.
  • R. Porcu, and M. Chiaramonte, "SPR + : a patch recovery technique for superconvergent computation of linear functionals", BIT Numerical Mathematics, January 6, 2020.
  • E. Miglio, N. Parolini, M. Penati, and R. Porcu, "High-order variational time integrators for particle dynamics", Communications in Applied and Industrial Mathematics, December 19, 2018.

Conference Presentation

  • A hybrid PIC-DEM approach for multi-phase computational fluid dynamics, 72nd Annual Meeting of the American Physical Society‚Äôs Division of Fluid Dynamics, Seattle, WA, November 23rd-26th, 2019.
  • Orthogonality Constrained Gradient Reconstruction, 13th World Congress on Computational Mechanics, New York City, NY, July 22nd-27th, 2018.
  • Nonlinear elastoplasticity for finite-strain deformations, SIMAI2016, congress of the Italian Society of Industrial and Applied Mathematics, Milan, Italy, September 13th-16th, 2016.

Conference Poster

  • SPR+: a Patch Recovery Technique for Super-convergent Computations of Quantities of Interest, PICSciE Research Computing Day, Princeton, NJ, November 7th, 2017.
  • Nonlinear elastoplasticity for finite-strain deformations, NetherMod 2017 Conference, XV International Workshop on Modelling of Mantle and Lithosphere Dynamics, Putten, The Netherlands, August 27th-31st, 2017.