Nicholas Wimer
Principal Engineer And Expert Witness at Burns & Wimer Consulting
Redlands, California, United States
Summary
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Senior🎓
Top SchoolNicholas Wimer is a principal engineer and expert witness translating advanced CFD, machine learning, and visualization into practical solutions for energy systems, hydrology, and fire dynamics. Based in Redlands, California, he leads Burns & Wimer Consulting while contributing as a Computational Scientist at NREL. His core work includes high-resolution CFD with adaptive mesh refinement and low-Mach-number models for heat transfer and wildland fire spread, complemented by postdoctoral ML applications in engine control and chemical kinetics. He is an experienced presenter and mentor with guest lectures and authorship in academic venues. He holds a Ph.D. in Thermo-Fluids (CFD) from CU Boulder, along with an M.S. in Mechanical Engineering, a B.Eng. in Mechanical Engineering, and an AB in Engineering Physics from Dartmouth, reflecting a strong blend of theory and hands-on engineering across eight years of cross-sector experience.
8 years of coding experience
7 years of employment as a software developer
Bachelor of Engineering (B.Eng.), Mechanical Engineering, Bachelor of Engineering (B.Eng.), Mechanical Engineering at Dartmouth CollegeDoctor of Philosophy (Ph.D.), Thermo-Fluids (Computational Fluid Dynamics), Doctor of Philosophy (Ph.D.), Thermo-Fluids (Computational Fluid Dynamics) at University of Colorado Boulder
Italian
Github Skills (30)
simulation10
mesh10
mach10
systems-biology10
physics9
mechanics9
hydrodynamics9
computational-fluid-dynamics9
thermodynamics9
gpu-acceleration8
databases8
simulations8
pde8
amr7
multiphysics7
Programming languages (4)
Github contributions (5)
nickwimer/ytScripts
Aug 2022 - Nov 2024
Collection of Python scripts that use "The yt Project" for data manipulation and visualization
Contributions:1 review, 43 PRs, 132 pushes in 2 years 3 months
manipulationpythondata-sciencedata-visualizationdata-manipulation
NREL/tf-melt
Oct 2023 - Sep 2024
TF-MELT (TensorFlow Machine Learning Toolbox) is a collection of architectures, processing, and utilities that are transferable over a range of ML applications.
Contributions:24 PRs, 45 pushes, 23 branches in 11 months
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