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, Marc 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, Marc 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.

Research Interests

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

Education

Ph.D., Nuclear Engineering and Applied Plasma Physics, University of California Los Angeles

M.S., Nuclear Engineering, University of California Los Angeles

B.S., Nuclear Engineering, University of California Berkeley

Professional Experience

Group Manager, High Performance Algorithms and Complex Fluids Group, NREL (2020–present)

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)

Associations and Memberships

Executive Committee Member, Western States Section Combustion Institute

Member, Combustion Institute

Member, Society for Industrial and Applied Mathematics (SIAM)

Featured Work

Direct Numerical Simulation of a Spatially Developing N-Dodecane Jet Flame under Spray A Thermochemical Conditions: Flame Structure and Stabilisation Mechanism, Combustion and Flame (2020)

Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation, Data Analysis for Direct Numerical Simulations of Turbulent Combustion (2020)

A Spectral Deferred Correction Strategy for Low Mach Number Flows Subject to Electric Fields, Combustion Theory and Modeling (2019)

Numerical Simulations of Buoyancy-Driven Flows Using Adaptive Mesh Refinement: Structure and Dynamics of a Large-Scale Helium Plume, Theoretical and Computational Fluid Dynamics (2020)

Analysis of Chemical Pathways for N-Dodecane/Air Turbulent Premixed Flames, Combustion and Flame (2019)

Towards the Distributed Burning Regime in Turbulent Premixed Flames, Journal of Fluid Mechanics (2019)

A Bayesian Approach to Calibrating Hydrogen Flame Kinetics Using Many Experiments and Parameters, Combustion and Flame (2019)

Surrogate Optimization of Computationally Expensive Black-box Problems with Hidden Constraints, INFORMS Journal on Computing (2019)

A Conservative, Thermodynamically Consistent Numerical Approach for Low Mach Number Combustion. I. Single-Level Integration, Combustion Theory and Modeling (2018)

BoxLib with Tiling: An AMR Software Framework, SIAM J. Scientific Computing (2016)