evgenypodryabinkin

Evgeny Podryabinkin

Senior Research Scientist
Laboratory of Artificial Intelligence for Materials Design
Center for Artificial Intelligence Technology

Professor: Alexander Shapeev

Evgeny has multidisciplinary expertise in numerical methods, mathematical modeling, computational fluid dynamics, and machine learning. In 2007, Evgeny graduated from the Mechanics and Mathematics Department of Novosibirsk State University, and in 2014, he received his Candidate of Sciences degree. 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 three parts: (1) development of models and algorithms describing complex hydrodynamic processes while drilling, and (2) systematic modeling of these processes, and (3) development of the machine learning algorithms 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, a computational core of a simulator of drilling hydraulics was developed and integrated into 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.  In 2015 Evgeny joined Skoltech where he is working on applications of machine learning to computational physics.

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. Besides that Evgeny has a joint project with Center for Hydracarbon Recovery. The project is devoted to development of a simulator of drilling hydraulics for oil&gas industry.

Main articles:

  1. Podryabinkin E.V., Rudyak V.Ya. Moment and Forces Exerted on the Inner Cylinder in Eccentric Annular Flow. Journal of Engineering Thermophysics. 2011. Vol. 20, No. 3. pp. 320–328.
  2. Podryabinkin E.V., Rudyak V.Ya. Modeling of Pseudoplastic and Viscoplastic Fluid Flows in the Annular Channel with Eccentricity. Proceedings of Higher School of Academy of Sciences. 2012. No. 2 (19). Pp. 112–122.
  3. Podryabinkin E.V., Rudyak V.Ya. Modeling of Turbulent Flows in the Annular Channel with Eccentricity and Inner Tube Rotation. Proceedings of NSU: Physics. 2012. V. 7, No. 4. Pp. 79–87.
  4. Podryabinkin E., Rudyak V., May R. Modeling of Drilling Fluids Flow in a Borehole for Drilling Optimization. Oil and Gas European Magazine. 2013. Vol. 39, No. 1. pp. 29–31.
  5. Podryabinkin E., Gavrilov A., Rudyak V., May R. Detailed Modeling of Drilling Fluid Flow in a Wellbore Annulus While Drilling. Canadian Energy Technology & Innovation. 2014. Vol. 1, No. 5. P. 27–36.
  6. Podryabinkin E., Tarasevich V., May R., Ahmed R. Modeling and Evaluating Surge Pressure while Tripping in a Borehole. Oil and Gas European Magazine. 2014. Vol. 40, No. 1. pp. 21–23.
  7. Podryabinkin E.V., Rudyak V.Ya. Modeling of turbulent annular flows of Hershel-Bulkley fluids with eccentricity and inner cylinder rotation. Journal of Engineering Thermophysics. 2014. Vol. 23, No. 2, pp. 137–147.
  8. Podryabinkin E., Ahmed R., Tarasevich V., May R. Evaluation of Pressure Change While Steady-State Tripping. Proceedings of the ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering OMAE2014. 2014. OMAE2014-23672.
  9. May R., Podryabinkin E., Tarasevich V., Bocharov O. Modelling of Pressure Fluctuations in a Wellbore While Tripping. Proceedings of the ASME 2015 34rd International Conference on Ocean, Offshore and Arctic Engineering OMAE2015. 2015. OMAE2015-41370.
  10. Gavrilov A.A., Finnikov K.A., Podryabinkin E.V. Modeling of Steady Herschel–Bulkley Fluid Flow Over a Sphere. Journal of Engineering Thermophysics. 2017. Vol. 26, No. 2, pp. 197–215.
  11. Podryabinkin, E., Tarasevich, V., May, R., Bocharov, O. Modelling of pressure fluctuations in a wellbore while tripping. Oil and Gas European Magazine. 2016. Vol. 43, No. 1. pp. 14–20.
  12. Podryabinkin E.V., Shapeev A.V. Active learning of linearly parametrized interatomic potentials. Computational Materials Science. 2017. Vol. 140, pp. 171–180.
  13. Gubaev K.V., Podryabinkin E.V., Shapeev A.V. Machine learning of molecular properties: Locality and active learning. The Journal of Chemical Physics. 2018. Vol. 148, 241727.
  14. Gubaev K.V., Podryabinkin E.V., Hart G.L.W., Shapeev A.V. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Computational Materials Science. 156 (2019). 148–156.