As the co-head of the flow modeling and control workgroup, my research interests include modeling, investigating and developing tools for flow estimation and for closed-loop flow control. Current applications are diverse and include:
Current research focuses mainly on 3 fields:
Flow modeling: Due to their low complexity, reduced order models (ROM) enable real-time flow control via fast sensing and actuation. Their relevance also relates to their ability to capture the important flow physics, while being simple enough for online control. Research activities include:
POD-Galerkin models and their calibration techniques.
Data-driven models e.g. Sparse identification of nonlinear dynamics (SINDY), machine-learning models.
Optimal sensor placement e.g. stochastic estimation, machine learning variable ranking.
Flow control: Model-based and model-free approaches are both pursued. The model-free control identifies the optimal nonlinear control law in an automatic (unsupervised) self-learning manner. This approach employs genetic programming and is termed Genetic Programming Control (GPC) or Machine Learning Control (MLC). Research activities include:
Further development of GPC algorithm.
Pre-testing and speed-up of GPC
Experimental testing and deployment
Numerical tools: The two above-mentioned research strands are supported by an array of in-house developed toolboxes and software packages for a range of usages. This include
SCOUT (Signal COrrection and Uncertainty quantification Toolbox) that allows to fully quantify signals, correct for distortions, and quantify the uncertainties.
Dr. Richard Semaan
Technische Universität Braunschweig
Institute of Fluid Mechanics
Telefon: +49 531391 94258+49 531391 94258
Fax: +49 531391 94254