Добро пожаловать на регулярные семинары по актуальным темам исследований в области вычислительной механики!

Приглашённые лекторы из Сколтеха и других вузов выступают с докладами, чтобы познакомить студентов с текущими исследованиями и достижениями в различных областях современной механики жидкости и твёрдого тела, прикладной математики, вычислительной математики и промышленного применения механики. Студенты получают возможность узнать об актуальных проблемах механики у ведущих специалистов в области вычислительной механики.

Продолжительность доклада: 50 минут

Q&A: 10-15 минут

Семинары проводятся на английском языке.

Ведущий преподаватель: Аслан Касимов, доцент

Контакты: A.Kasimov@skoltech.ru

5 МАРТА, 14:00 | МЕТОДЫ ПРЕДИКТИВНОГО МОДЕЛИРОВАНИЯ НА ОСНОВЕ ДАННЫХ ДЛЯ ИДЕНТИФИКАЦИИ И УПРАВЛЕНИЯ ГИДРОДИНАМИЧЕСКИМИ СИСТЕМАМИ

Аудитория: R3-2009
Докладчик: Проф. Сергей Сафонов, Заместитель директора Проектного центра по энергопереходу, Сколтех

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.

19 МАРТА, 14:00 |  ПРИМЕНЕНИЕ ГЕОМЕХАНИКИ В НЕФТЯНОЙ ПРОМЫШЛЕННОСТИ

Аудитория: B2-3006
Докладчик: Светлана Жигульский, к.т.н., Руководитель по разработке продукта, Центр горного инжиниринга, Газпромнефть НТЦ

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.

2 АПРЕЛЯ, 14:00 | НОВЫЕ КАТАЛИЗАТОРЫ ДЛЯ УСТОЙЧИВОГО РАЗВИТИЯ И ДЕКАРБОНИЗАЦИИ

Аудитория: R3-2009 
Докладчик: Александра Радина, аспирантка программы «Науки о материалах», Сколтех

Nowadays the most widely used catalysts for organic synthesis are mainly made of noble and rare earth metals which significantly increases the cost of many products. Moreover, 80% of catalyst used in domestic industry including oil and chemical production are imported, and it can cause problems in the present political situation. To solve this problem, the Industry-Oriented Computational Discovery Group at Skoltech is currently conducting studies to find a new catalyst that would be as effective as those made from precious metals, but cheaper. Transition metal carbides and borides have been suggested as promising candidates for new catalysts. Recently, some of our theoretical predictions have been confirmed experimentally. It has been shown that higher transition metal borides have great catalytic activity for the conversion of CO2 into various fuels and hydrogen production processes.

9 АПРЕЛЯ, 14:00 | ИССЛЕДОВАНИЕ МЕХАНИЗМОВ ФОРМИРОВАНИЯ ПРОСТРАНСТВЕННО-ВРЕМЕННЫХ СТРУКТУР, ВОЗНИКАЮЩИХ НА ДВИЖУЩЕМСЯ ФРОНТЕ РЕАКЦИИ

Аудитория: R3-2009 
Докладчик: Эдуард Якупов, Младший научный сотрудник, Лаборатория нелинейной динамики и теоретической биофизики, Физический институт имени П. Н. Лебедева Российской академии наук

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.