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

FEBRUARY 5, 2:00 PM | MECHANICAL PROPERTIES OF SINGLE AND POLYCRYSTALLINE SOLIDS FROM MACHINE LEARNING

Location: R3-2009
Speaker: Faridun Jalolov, PhD student of Materials Science, Skoltech 

Many industrial materials are synthesized as polycrystals or multiphase systems. They contain both a single crystal and amorphous components between single crystal grains. The large number of atoms makes it hard to calculate the properties of these systems using modern quantum-mechanical methods. Density functional theory can only be applied to materials with a few hundred atoms. To address the problem, we use a machine-learning approach based on Moment Tensor Potentials (MTP). As compared to other solutions, the potential of the new method learned in active learning on local atomic environments. When calculating a large structure with many hundreds of thousands of atoms, the MTP identifies which atom makes a mistake in the calculations, or is calculated incorrectly. The reason for this could be the limited training dataset, which prevents all possible system configurations from being considered. A local environment of this atom is then “cut out” and its energy calculated using quantum mechanics. Afterwards, the data is added back to the training set for further learning. As the on-the-fly learning progresses, the calculations continue until they come across another configuration that needs to be included in the training process. Other known machine-learning potentials cannot learn on small local parts of large structures, which limits their applicability and accuracy.

FEBRUARY 12, 2:00 PM | MODELLING OF FLOWBACK IN HYDRAULICALLY FRACTURED OIL WELLS OF BAZHENOV FORMATION

Location: R3-2009
Speaker: Gleb Strizhnev, PhD student of Engineering Systems, Skoltech 

The report explores the modeling and optimization of flowback operations in wells with unconventional hydrocarbon reserves, highlighting their unique characteristics compared to traditional reservoirs. The primary objective of this work is to enhance the flowback process following multi-stage hydraulic fracturing in such fields. The report is structured into three main sections. The first section provides a detailed analysis of the cleanup model employed to describe the flowback process. The second section examines the sensitivity analysis of flowback modeling results concerning key input parameters, enabling the identification of the most significant factors influencing the process. The third section discusses optimization methods and historical data matching to achieve a more accurate correspondence between modeling and actual field data. Model adaptation to field data is conducted using a gradient descent algorithm, ensuring a mean squared error (MSE) of less than 10% for the first 30 days of flowback. The results from adapting the model to data from an unconventional hydrocarbon reservoir in Western Siberia indicate the necessity of considering several additional physical phenomena, such as increased permeability in the near-fracture zone due to the presence of natural fractures and the formation of an oil-water emulsion. The report also presents the evolution of the model and its functionality, including the application of machine learning approaches for parameter tuning. The actual modeling is based on real data from the field, underscoring the capability to qualitatively and quantitatively describe flowback processes in wells with unconventional hydrocarbon reserves.

FEBRUARY 19, 2:00 PM | PROPPANT TRANSPORT IN HYDRAULIC FRACTURES BY VISCOELASTIC FLUIDS

Location: R3-2009
Speaker: Sergei Boronin, Assistant Professor, Project Center for Energy Transition and ESG, Skoltech

Hydraulic fracturing is a key well stimulation technology used by oilfield service companies to increase production of hydrocarbons oil and gas in low-permeability hydrocarbon-bearing formations. It is based on injection of a particle-laden fluid into the formation at pressures exceeding the minimal rock stress to create a high-conductivity channel filled with solid particles (proppant) and improve significantly the flow of hydrocarbons into the well.

Typical hydraulic fracturing jobs are made using water as a base (or carrier) fluid with several chemical additives to meet counter-directional technological demands, which, in terms of fluid mechanics, are linked with unsteady behavior of fluid viscosity as the suspension travels along the wellbore and inside the fracture channel. These properties of the fracturing fluids are obtained by adding polymer additives, for which the industry standard is a guar gum made of a certain plant. Aim of the current study is to study experimentally the viscoelastic properties of a polyacrylamide (PAA) as an alternative to guar gum and formulate the mathematical model of suspension flow in a hydraulic fracture that considers its elasticity properties on proppant transport and improves the accuracy of existing simplified models implemented into industrial fracturing simulators based on power-law fluid rheology model.

FEBRUARY 26, 2:00 PM | INVESTIGATION OF THE MECHANISMS OF FORMATION OF SPATIOTEMPORAL STRUCTURES ARISING AT THE PROPAGATING REACTION FRONT

Location: R3-2009
Speaker: Edward 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.