Prediction and prevention of extreme events in fluid mechanics with Data Assimilation

The world of power generation is continuously changing and natural is expected to grow significantly for electricity generation in the coming decades. In view of environmental impacts, it is of utmost importance to further enhance the efficiency and reduce emissions of gas turbines. However, can we develop sufficiently flexible turbines that cope with the fluctuations of renewables and deal with hydrogen-enriched fuels obtained from renewable excess power? Modern ultralow emission gas turbines are designed to burn in a lean regime to reduce NOx emissions. However, lean flames burn very unsteadily because they are sensitive to the turbulent environment of the combustion chamber. This is a complex environment in which acoustics, aerodynamics, and flame dynamics interact, leading to the extreme events of thermoacoustic instabilities, engine noise, and rare events. Although modern designs should aim to eliminate or control these extreme phenomena, the development of cleaner turbines is bound to increase these effects, making their design particularly challenging. The objective of this research is to propose a new framework to predict and control the extreme events in fluid mechanics by applying novel computational techniques based on artificial intelligence, adjoint methods and weather forecasting techniques. Additionally, this research proposal includes the introduction of experimental data which will be used to develop hybrid physical-based and data-driven predictive models.