Bruce Perry joined the High-Performance Algorithms and Complex Fluids Group within the Computational Science Center after completing his doctoral research at Princeton University, where his research focused on development of physics-based models for large eddy simulation (LES) of turbulent combustion systems. At NREL, Bruce has expanded his work on turbulence and combustion model development to include the use of machine learning to augment or replace physics-based models in regimes where the established models do not perform well.

The overarching goal of his research is to enable high-fidelity simulation of emerging combustion-based clean energy technologies for both transportation and electricity-generation. Bruce’s work spans the range from fundamental to applied, including conceptual development and proof-of-concept studies for novel modeling approaches, implementation of the models in advanced computational fluid dynamics solvers, and application in simulations of real-world combustion systems to aid in their development.

For additional information, see Bruce Perry's LinkedIn profile. 

Disclaimer: Any opinions expressed on LinkedIn are the author’s own, made in the author's individual capacity, and do not necessarily reflect the views of NREL.

Research Interests

Machine learning models for turbulence and combustion  
 
Development of computational fluid dynamics codes for next-generation high-performance computing systems 
 
Simulations of supercritical carbon dioxide power systems  

Education

Ph.D., Mechanical and Aerospace Engineering, Princeton University 
 
M.A., Mechanical and Aerosapce Engineering, Princeton University 
 
M.S., Chemical Engineering, Northwestern University
 
B.S., Chemical Engineering, Northwestern University 

Associations and Memberships

Member, The Combustion Institute  
 
Member, American Physical Society, Division of Fluid Dynamics 

Featured Work

Deep Learning-Based Model for Progress Variable Dissipation Rate in Turbulent Premixed Flames, Proceedings of the Combustion Institute (2020) 
 
Data-Driven Dimension Reduction in Turbulent Combustion: Utility and Limitations, AIAA Scitech 2019 Forum (2019) 
 
Effect of Multiscalar Subfilter PDF models in LES of Turbulent Flames with Inhomogeneous Inlets, Proceedings of the Combustion Institute (2019) 
 
Joint Probability Density Function Models for Multiscalar Turbulent Mixing, Combustion and Flame (2018) 
 
A Two Mixture Fraction Flamelet Model for Large Eddy Simulation of Turbulent Flames with Inhomogeneous Inlets, Proceedings of the Combustion Institute (2017) 

Awards and Honors

European Union Marie Sklodowska-Curie Actions Seal of Excellence (2019) 
 
Princeton University Martin Summerfield Fellowship (2016) 
 
National Science Foundation Graduate Research Fellowship (2015) 
 
Princeton University Gordon Y.S. Wu Fellowship (2014)