Malik Hassanaly
Researcher III-Computational Science
Malik.Hassanaly@nrel.gov
303-384-7176
https://orcid.org/0000-0002-0425-9090
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ResearchGate
Malik Hassanaly uses his expertise in statistical methods, machine learning, and computational fluid dynamics to develop more accurate and efficient numerical models. While obtaining his doctorate, Malik mostly worked on turbulent combustion applications with an emphasis on aircraft engines and continues to work on combustion-related topics. At NREL, he also works on manufacturing processes for solar cells, uncertainty quantification for weather prediction and battery modeling.
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
Data-assisted uncertainty quantification
Scientific machine learning
Multi-fidelity methods
Data reduction
Surface chemistry modeling
Anomaly detection/prevention
Education
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)
Intern, Managing Solutions, Areva NP (2010)
Associations and Memberships
American Institute for Aeronautics and Astronautics
Combustion Institute
American Physical Society
Society for Industrial and Applied Mathematics
American Institute of Chemical Engineers
Featured Work
Adversarial Sampling of Unknown and High-Dimensional Conditional Distributions, Journal of Computational Physics (2021)
Surface Chemistry Models for GaAs Epitaxial Growth and Hydride Cracking Using Reacting Flow Simulations, Journal of Applied Physics (2021)
Classification and Computation of Extreme Events in Turbulent Combustion, Progress in Energy and Combustion Science (2021)
Data-driven Analysis of Relight Variability of Jet Fuels Induced by Turbulence, Combustion and Flame (2020)
Lyapunov Spectrum of Forced Homogeneous Isotropic Turbulent Flows, Physics Review Fluids (2019)
A Self-Similarity Principle for the Computation of Rare Event Probability, Journal of Physics A: Mathematical and Theoretical (2019)
Ensemble-LES Analysis of Perturbation Response of Turbulent Partially-Premixed Flames, Proceedings of the Combustion Institute (2019)
Using Machine Learning to Construct Velocity Fields from OH-PLIF Images, Combustion Science and Technology (2019)
Large Eddy Simulation of Soot Formation in a Model Gas Turbine Combustor, Journal of Engineering for Gas Turbines and Power (2018)
A Minimally Dissipative Low-Mach Number Solver for Complex Reacting Flows in OpenFOAM, Computer and Fluids(2018)
Awards and Honors
Richard and Eleanor Towner Prize for Distinguished Academic Achievement (2019)
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