Marc Day manages the High-Performance Algorithms and Complex Fluids Group within the Computational Science Center at NREL. He dedicates his time to developing algorithms for large-scale scientific computations of complex fluid flow problems. Over his career, Day has contributed to projects associated with compressible and low Mach number astrophysics, compressible and low Mach number terrestrial combustion, and reacting multiphase flows in subsurface porous media. The common theme is the development of highly efficient algorithms that exploit known separations in scale (spatial and/or temporal). In addition, Day works with uncertainty propagation in large complex applications using techniques based on Markov chain Monte Carlo and implicit sampling, as well as in machine learning with applications in reacting and inert fluid flows.

Simulation and analysis of multi-scale reacting flows in low Mach number and compressible regimes using solution-adaptive algorithms for distributed high-performance computing hardware

Software architecture, validation, and verification

Turbulence-chemistry interactions in premixed and diffusion flames, including stabilization and control, localized extinction, and emissions

Machine learning enhanced solutions for partial differential equations

Uncertainty quantification/propagation

Bayesian parameter estimation

Senior Staff Scientist, Lawrence Berkeley National Laboratory (1998–2020)

Postdoctoral Researcher, Lawrence Berkeley National Laboratory (1996–1998)

Postdoctoral Researcher, Lawrence Livermore National Laboratory (1995–1996)