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Malik Hassanaly is a postdoctoral researcher in the High-Performance Algorithms and Complex Fluids Group within the Computational Science Center at NREL. He uses his expertise in statistical methods, machine learning, and computational fluid dynamics to develop more accurate and efficient numerical models. In his research and professional experience, he has mostly worked on turbulent combustion applications with an emphasis on aircraft engines, and he continues to work on combustion-related topics. At NREL, he also works on manufacturing processes for solar cells and uncertainty quantification for wind prediction.

For additional information, see Malik Hassanaly'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

Design optimization

Deep-learning assisted uncertainty quantification 

Machine learning for physics modeling

Surface chemistry modeling

Anomaly detection/prevention 


Ph.D., Aerospace Engineering, University of Michigan 
M.S., Aerospace Engineering, University of Texas at Austin 
M.S., General Engineering, Ecole Centrale de Lille, France 

Professional Experience

Intern, R&D, Maia Eolis (2012-2013)

Intern, Software Development, RTE (2012-2012)

Intern, Managing Solutions, Areva NP (2010-2010)

Associations and Memberships

Member, American Institute for Aeronautics and Astronautics

Member, Combustion Institute 

Member, American Physical Society

Member, Society for Industrial and Applied Mathematics

Member, American Institute of Chemical Engineers

Featured Work

A Minimally-Dissipative Low-Mach Number Solver for Complex Reacting Flows in OpenFOAMComputer and Fluids (2018) 

Large Eddy Simulation of Soot Formation in a Model Gas Turbine CombustorJournal of Engineering for Gas Turbines and Power (2018)

Ensemble-LES Analysis of Perturbation Response of Turbulent Partially-Premixed FlamesProceedings of the Combustion Institute (2019)

Lyapunov Spectrum of Forced Homogeneous Isotropic Turbulent FlowsPhysics Review Fluids (2019)

A Self-Similarity Principle for the Computation of Rare Event ProbabilityJournal of Physics A: Mathematical and Theoretical (2019)

Numerical Convergence of the Lyapunov Spectrum Computed Using Low Mach Number SolversJournal of Computational Physics (2019)

Emerging Trends in Numerical Simulations of Combustion SystemsProceedings of the Combustion Institute (2019) 

Experimental Data Based Reduced Order Model for Analysis and Prediction of Flame Transition in Gas Turbine CombustorsCombustion Theory and Modelling (2019)

Using Machine Learning to Construct Velocity Fields from OH-PLIF ImagesCombustion Science and Technology (2019)

Data-Driven Analysis of Relight variability of Jet Fuels induced by TurbulenceCombustion and Flame (2020)

Awards and Honors

Richard and Eleanor Towner Prize for Distinguished Academic Achievement (2019)