Reduced-Order Modelling for Flow Control
The book focuses on the physical and mathematical foundations of model-based turbulence control: reduced-order modelling and control design in simulations and experiments. Leading experts provide elementary self-consistent descriptions of the main methods
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Series Editors: The Rectors Giulio Maier - Milan Franz G. Rammerstorfer - Wien Jean Salençon - Palaiseau
The Secretary General
Executive Editor
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This volume contains 101 illustrations
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FOREWORD
Practical interests in flow control have no longer to be demonstrated. Flow control has motivated rapid developments in the past two decades in experiments, flow stability theory and computational fluid dynamics (CFD). Recent advances in experimental studies include applications of more and more sophisticated actuators and sensors. However, up to now, most of the results are predominantly related to open loop, at most, adaptive approaches. Early closed-loop applications of control methods were in noise control based on antinoise concepts. These studies established the pioneering link between fluid mechanics and control theory. However, in most aerodynamic applications, turbulent flows are encountered. Due to the intrinsic nonlinearities, turbulence gives rise to a large variety of temporal and spatial scales of more or less organized nature. Turbulence has remained one of the last not satisfactorily resolved physical phenomenon of practical importance in engineering sciences. It is obvious that the complexity of these flows is so pronounced that simpler – if this term can be used for turbulent flows – descriptions need to be derived. The encountered complexity is observed at three levels. First, the characterization of the flow itself is complex and depends on the type of available information (e.g. sensors). Any state information is by nature incomplete or of excessive extent for turbulent flows. Second, the effect of any actuator is by nature 3D and unsteady, thus difficult to characterize. Third, the complete modelling of the flow (CFD), its sensitivity to perturbations, etc. exceeds available computer power by many orders of magnitudes, particularly for online capability in experiment. In the same vein, the predominantl
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