Dr.Artem Korobenko
Dr. Artem Korobenko is a Professor and Associate Head for Research in the Department of Mechanical and Manufacturing Engineering at the University of Calgary, Canada. He holds a Schulich Research Chair and leads the Computational Fluid and Structural Mechanics Group (CFSMgroup). He received his PhD in 2014 from the University of California, San Diego, where he later completed a postdoctoral appointment in 2016. His research focuses on the development of multi-fidelity and data-driven computational methods for the analysis and design of complex systems in aerospace, wind, and marine engineering. Dr. Korobenko is a Fulbright alumnus, a recipient of the Alexander von Humboldt Research Fellowship, IACM Fellow and a founding member and current President of the Canadian Association for Computational Science and Engineering.
Dynamic Data Driven Applications Systems (DDDAS) provide a powerful framework for integrating computational models with dynamically acquired data, enabling simulations to adapt as new information becomes available and, in turn, informing what data should be collected next. This paradigm is particularly attractive for aerospace applications, where strongly coupled physics, uncertain operating environments, limited observations, and stringent computational requirements often challenge conventional simulation approaches.
This talk will first revisit our earlier work on DDDAS methodologies for aerospace structures, where computational models, structural response data, and adaptive model updating were combined to improve prediction and characterization of complex structural systems. These examples illustrate how dynamic interaction between models and measurements can enhance predictive capability while providing a foundation for real-time assessment and decision making.
The second part of the talk will explore opportunities for extending the DDDAS paradigm to hypersonic flight. High-fidelity simulations of hypersonic flows will be used to illustrate the extreme spatial and temporal scales, strong nonlinearities, shock interactions, and coupled aerodynamic and structural phenomena that must be resolved. Although such simulations provide exceptional physical insight, their computational cost limits their direct use for real-time prediction and operational decision making. DDDAS offers a pathway to bridge this gap by combining high-fidelity simulation with experimental and sensor data, reduced-order and surrogate models, uncertainty quantification, and adaptive multi-fidelity computation.
The talk will discuss how these components can form a continuously evolving computational framework in which data update the predictive model, simulations identify where additional information is most valuable, and computational resources are dynamically directed toward the physics that matter most. Such approaches have the potential to support the next generation of predictive digital environments for aerospace systems, from structural health assessment to hypersonic vehicle analysis and design.
