Inhalt des Dokuments
Data-based modelling and machine learning
- Gradient-free optimisation for the automatic suppression of instabilities
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The recent advent of powerful algorithms able to extract information from large sets of data without the need of accurately modelling physical systems is having a large impact on the engineering world. Gradient-free methods, dynamic mode decomposition, clustering and genetic algorithms are examples of the tools that the combustion and acoustic communities are exploiting to shed new light on available data, or to solve optimisation problems that require a too large amount of computational power with standard methods.
- Collaborators: Prof. Myles Bohon (TU Berlin), M. Reumschüssel
Related Publications
M. Reumschüssel, J. von Saldern, Y. Li, C.O. Paschereit, A. Orchini, "Gradient-Free Optimization in Thermoacoustics: Application to a Low-Order Model" Journal of Engineering for Gas Turbines and Power, 2021, Vol. , pp. GTP-21-1284. [link]
M. Bohon, A. Orchini, R. Bluemner, C.O. Paschereit, E. Gutmark, "Dynamic mode decomposition analysis of rotating detonation waves" Shock Waves, 2020, Vol. 921, pp. 1-13. [link]
M. Bohon, R. Bluemner, A. Orchini, C.O. Paschereit, E. Gutmark, "Analysis of rdc operation by dynamic mode decomposition (DMD)" AIAA Propulsion and Energy, 2019, AIAA 2019-4377. [link]
