Materials Science
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Materials Science Experts

Evgeny Podryabinkin

Research Scientist
Material Science
Skoltech
Reunion

Evgeny Podryabinkin

Biography

Evgeny has multidisciplinary expertise in numerical methods, mathematical modeling, fluid mechanics, heat and mass transfer. In 2007, Evgeny graduated from the Mechanics and Mathematics Department of Novosibirsk State University, and in 2014, he received his Candidate of Sciencesdegree from Tomsk State University. In his Master’s thesis Evgeny applied a numerical method for modeling laser welding processes. After graduation, he joined the Russian Science Center of Baker Hughes, an oilfield service company, collocated with the Siberian Branch of the Russian Academy of Sciences in Novosibirsk. There Evgeny led and participated in projects that developed an automated system for optimization and control of the drilling process. These projects consisted of two parts: (1) development of models and algorithms describing complex hydrodynamic processes while drilling, and (2) systematic modeling of these processes and development of the system for fast prediction of the key parameters. The studies involved Newtonian and non-Newtonian fluids, their laminar and turbulent flows, multiphase problems associated with efficient cutting transport and hydraulic effects in the long channels. As a result of the project, an algorithm for fast prediction of the key flow parameters while drilling was developed and integrated into drilling engineer’s software. Some results of this work related to modeling of non-Newtonian fluids flows in the turbulent regime were reflected in Evgeny’s Candidate of Sciences thesis.

Research Interest

Evgeny is working on the project led by A.Shapeev that is focused on development of machine-learning-based models of interatomic interactions. The main idea of the project is to apply machine-learning techniques for construction of interatomic potentials in a way that allows combination of accuracy and performance. In particular, Evgeny focuses his efforts on active learning and learning-on-the-fly of machine-learning interatomic potentials.

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