Reduced-Order Modelling for Flow Control [electronic resource] /
International Centre for Mechanical Sciences.
Noack, Bernd R.editor.
Morzynski, Marek.editor.
Tadmor, Gilead.editor.
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 and outline the state of the art. Covered areas include optimization techniques, stability analysis, nonlinear reduced-order modelling, model-based control design as well as model-free and neural network approaches. The wake stabilization serves as unifying benchmark control problem.
Description based upon print version of record.
Title Page; Copyright Page; FOREWORD; PREFACE; Table of Contents; Flow control and constrained optimization problems; 1 Introduction; 2 Elements of control theory and model reduction; 3 Optimal control theory; 4 Linear quadratic optimal control; 5 Optimal growth perturbation; 6 Linearized Burgers equation; 7 Non-linear Burgers equation; 8 Conclusion; A Adjoint operator and inner product; Bibliography; Global Stability Analysis for Linear Dynamics; 1 Introduction; 2 Global flow stability analysis; 3 Finite Element Method discretization of the global flow stability problem
4 Numerical techniques to solution of the eigenvalue problem5 Stability results for the cylinder wake; 6 Wake control; 7 Weakly non-linear models; 8 Summary and perspectives - 3D flow stability analysis; Bibliography; Galerkin Method for Nonlinear Dynamics; 1 Introduction; 2 Galerkin method as foundation; 3 Statistical fluid mechanics as design tool; 4 Auxiliary models as key enablers; 5 Conclusions and Outlook; Bibliography; Galerkin Models Enhancements for Flow Control; 1 Introduction; 2 Benchmark Model Systems; 3 Low Order Galerkin Models: Some Added Concepts
4 Broadband Galerkin Models: Mean Field Models5 Broadband Galerkin Models: Subgrid Models; 6 Mode Deformation and Models on Nonlinear Manifolds; 7 Galerkin Models for Flows in Unsteady Geometry; 8 Feedback Design; 9 Concluding Remarks; Bibliography; Feedback Flow Control in Experiment and Simulation Using Global Neural Network Based Models; 1 Introduction; 2 Wake stabilization benchmark; 3 Description of transient coherent structure via DPOD; 4 Artificial neural network system identification to develop a numerical plant model; 5 Feedback control wake stabilization results
6 Key enablers / strategyBibliography; Optimal Closed-Loop Approaches in Flow Control; 1 Introduction; 2 Linear systems; 3 Methods in closed-loop control; 4 Applications; 5 Conclusions; Bibliography
Includes bibliographical references.
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 and outline the state of the art. Covered areas include optimization techniques, stability analysis, nonlinear reduced-order modelling, model-based control design as well as model-free and neural network approaches. The wake stabilization serves as unifying benchmark control problem.
Fluids.
Mathematical models.
Fluid mechanics.
Control engineering.
Fluid- and Aerodynamics.
Mathematical Modeling and Industrial Mathematics.
Engineering Fluid Dynamics.
Control and Systems Theory.
Courses and lectures /