Anton Surtaev

Kutateladze Institute of Thermophysics

E-mail: surtaevas@gmail.com

Keynote report at the “The 6th International Symposium on Thermal-Fluid Dynamics” (ISTFD 2025), Qingdao, China, July  25-28, 2025.

Bio

Dr. Anton Surtaev is a Senior Researcher at the Kutateladze Institute of Thermophysics and Novosibirsk State University (Russia). He received his Ph.D. from Kutateladze Institute of Thermophysics in 2011. Dr. Anton Surtaev has published over 70 peer-reviewed journal articles. He is an RSF expert and supervisor of projects supported by Russian Science Foundation (RSF) and Russian Foundation for Basic Research (RFBR), including those carried out with foreign partners. He has been awarded the Novosibirsk City Scholarship for Ph.D. Students (2009); the V.E. Alemasov Memorial Award (2012); the S.S. Kutateladze Memorial Award (2012); the Russian President's Scholarship (2015-2017); the Novosibirsk City Award in the Field of Science and Innovation (2017). His current research interests include two-phase flows and heat transfer, phase change phenomena (boiling, condensation and evaporation), development of new techniques for enhancement of heat-mass transfer and for experimental diagnostics, energy efficiency and micro/nanotechnology, various applications of multiphase flows, including biomedicine.

Abstract

Understanding phase change processes with high precision is critical for advancing thermal management systems, energy conversion technologies, and industrial cooling applications. This keynote explores the integration of cutting-edge experimental techniques with artificial intelligence (AI) analysis to achieve unprecedented accuracy in characterizing local boiling characteristics, heat transfer dynamics, and crisis phenomena development. Key experimental approaches include infrared (IR) thermography for temperature field mapping, total reflection techniques for interfacial behavior analysis, and high-speed visualization for capturing transient boiling events in real time. By leveraging AI-driven data processing and pattern recognition, we enhance the interpretation of complex experimental datasets, enabling deeper insights into phase transition mechanisms. This fusion of direct measurement methods with AI-based analytics paves the way for improved predictive models, optimization of heat transfer performance, and the development of next-generation thermal systems.