Welcome to the regular seminars on current research topics in computational mechanics!

Presentations are given by invited lecturers from Skoltech as well as from outside to introduce students to current trends and advances in diverse areas of modern fluid and solid mechanics, applied mathematics, computational science, and industrial applications of mechanics. Students have the opportunity to learn from and interact with leading experts in computational mechanics and to enjoy exposure to cutting-edge topics and open problems in the field.

Speaker's report: 50 min.

Q&A: 10-15 min.

Seminars are held in English.

Lead Instructor: Aslan Kasimov, Associate Professor

Contacts: A.Kasimov@skoltech.ru

MARCH 5, 2:00 PM | DATA-DRIVEN PREDICTIVE MODELING FOR HYDRODYNAMIC SYSTEM IDENTIFICATION AND CONTROL

Location: R3-2009
Speaker: Sergey Safonov, Professor of the Practice, Deputy Director of the Project Center for Energy Transition ans ESG, Skoltech

Recently the problem of predicting the dynamic behavior of hydrodynamic systems using measured spatiotemporal data has attracted a great attention due to both extraordinary growth of machine learning applications and the appearance of novel data-driven approaches. The strong interest for modeling hydrodynamic behavior is also supported by a critical need for predicting and controlling unsteady flow in nuclear power generation, chemical technology, coal extraction and oil and gas production processes. To demonstrate the practical usability and robustness of the various data-driven approaches and machine learning algorithms for dynamic system identification, the application of these methods to model and predict the spatiotemporal behavior of compressible gas in a one-dimensional shock tube (known as the Riemann problem) is considered. Based on the numerical spatiotemporal solutions of the Riemann problem, the comparative analysis of the predictive capabilities and application features of various data-driven methods, such as dynamic mode decomposition and sparse identification of nonlinear dynamics, as well as machine learning algorithms is thoroughly performed. It is found out that, unlike machine learning methods, the considered data-driven methods require significantly less computational power and time for model training and subsequent prediction, so that they can be applied on relatively small training data. This makes it possible to use these data-driven prediction algorithms in real time to identify more quickly sudden changes in the system dynamics and thus potentially utilize these methods for more efficient system control.

MARCH 19, 2:00 PM | APPLICATIONS OF GEOMECHANICS IN PETROLEUM INDUSTRY

Location: B2-3006
Speaker: Svetlana Zhigulskiy, PhD, Head of Product Development, Mining Engineering Center, Gazpromneft Science and Technology Center

In this seminar, we will dive into petroleum geomechanics, discover what the purpose and applications of integrated reservoir geomechanics are for drilling, production of crude oil and natural gas, well stimulation for improved oil and gas recovery, "sweet spots" prediction in naturally fractured reservoir and risk assessment in carbon capture utilization storage (CCUS). We will discuss theoretical and experimental findings on the development of a geomechanical approach to creating new hydrocarbon technologies. The seminar provides insights about challenges facing the oil and gas industry and the implications of reservoir geomechanics in that.

APRIL 2, 2:00 PM | NEW CATALYSTS FOR SUSTAINABLE DEVELOPMENT AND DECARBONIZATION

Location: R3-2009
Speaker: Aleksandra Radina, PhD student of Materials Science, Skoltech 

APRIL 9, 2:00 PM | INVESTIGATION OF THE MECHANISMS OF FORMATION OF SPATIOTEMPORAL STRUCTURES ARISING AT THE PROPAGATING REACTION FRONT

Location: R3-2009
Speaker: Eduard Yakupov, Junior research scientist, Laboratory for Nonlinear Dynamics and Theoretical Biophysics, P.N.Lebedev Physical Institute of the Russian Academy of Sciences

In experimental studies of combustion wave propagation in gaseous media, it has been found that under certain conditions autowaves - spirals or targets - patterns appear at the wave front. This study aims to solve pressing problems in nonlinear dynamics by examining autowave and dissipative structures emerging at a propagating reaction front. It explores the formation and evolution of complex spatiotemporal structures in distributed dynamic systems, focusing on processes occurring at the reaction front. The research employs an approach that considers the hierarchical nature of spatial-temporal self-organization via block models. Key outcomes include developing a novel methodology for analyzing the mechanisms underlying these structures' formation, detailing conditions necessary for the emergence of autowave and Turing structures, creating a reduced model for high-pressure hydrogen-air combustion, and establishing quantitative criteria for the formation of different structure types based on system parameters.