Olga Doronina

Olga Doronina

Postdoctoral Reseacher

National Renewable Energy Laboratory

Hello, there!

My research interests include modeling and simulating turbulent flows with an emphasis on the development of new data science and machine learning modeling approaches.

Interests

  • Machine learning
  • Statistics
  • Data Analysis
  • Turbulence modeling

Education

  • Ph.D./M.S. in Mechanical Engineering, 2020

    University of Colorado, Boulder

  • M.S./B.S. in Applied Mathematics and Physics, 2014

    Moscow Institute of Physics and Technology (Phystech)

Skills

Python

Research

Data Analysis

Statistics

CFD

Machine Learning

Experience

 
 
 
 
 

Postdoctoral Researcher

National Renewable Energy Laboratory

December 2020 – Present Golden, CO
 
 
 
 
 

Research Assistant

University of Colorado, Boulder

January 2017 – November 2020 Boulder, CO
Turbulence and Energy Systems Laboratory (TESLa). Created a flexible tool for turbulence model calibration utilizing Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC)
 
 
 
 
 

Teaching Assistant

University of Colorado, Boulder

August 2016 – December 2016 Boulder, CO
  • Computational Methods MCEN 3030

  • Finite Element Analysis MCEN 4173/5173

 
 
 
 
 

Instructor of Record

Moscow Institute of Physics and Technology

February 2015 – June 2016 Dolgoprudny, Russia
  • Numerical Methods I / Numerical Methods II
 
 
 
 
 

Research Assistant

Keldysh Institute of Applied Mathematics (KIAM RAS)

September 2011 – July 2016 Moscow, Russia

Projects

Using ABC for turbulence model calibration

Turbulence model development using Approximate Bayesian Computation.

Publications

(2021). Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation. Experiments in Fluids.

Conference Presentations

Recent & Upcoming Talks

Approximate Bayesian Computation for Parameter Estimation in RANS Turbulence Models

RANS model parameters estimation in inhomogeneous turbulent flow (axisymmetric transonic bump) using Approximate Bayesian Computation (ABC).