Results 1  10
of
412
Aerodynamic design via control theory
 Journal of Scientific Computing
, 1988
"... wing design; optimization. flow; inverse I. INTRODUCTION AND HISTORICAL SURVEY Computers have had a twofold impact on the science of aerodynamics. On the one hand numerical simulation may be used to gain new insights into the physics of complex flows. On the other hand computational methods can be u ..."
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Cited by 231 (63 self)
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wing design; optimization. flow; inverse I. INTRODUCTION AND HISTORICAL SURVEY Computers have had a twofold impact on the science of aerodynamics. On the one hand numerical simulation may be used to gain new insights into the physics of complex flows. On the other hand computational methods can be used by engineers to predict the aerodynamic characteristics of
Optimum aerodynamic design using the NavierStokes equations
 Theoretical and Computational Fluid Dynamics
, 1998
"... The ultimate success of an aircraft design depends on the resolution of complex multidisciplinary tradeo s between factors such as aerodynamic eciency, structural weight, stability and control, and ..."
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Cited by 121 (46 self)
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The ultimate success of an aircraft design depends on the resolution of complex multidisciplinary tradeo s between factors such as aerodynamic eciency, structural weight, stability and control, and
Unified Notation for Data Assimilation: Operational, Sequential and Variational
, 1997
"... The need for unified notation in atmospheric and oceanic data assimilation arises from the field's rapid theoretical expansion and the desire to translate it into practical applications. Selfconsistent notation is proposed that bridges sequential and variational methods, on the one hand, and o ..."
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Cited by 118 (8 self)
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The need for unified notation in atmospheric and oceanic data assimilation arises from the field's rapid theoretical expansion and the desire to translate it into practical applications. Selfconsistent notation is proposed that bridges sequential and variational methods, on the one hand, and operational usage, on the other. Over various other mottoes for this risky endeavor, the authors selected: "When I use a word," Humpty Dumpty said, in rather a scornful voice tone, "it means just what I choose it to mean  neither more nor less." Lewis
Fluid Control Using the Adjoint Method
 ACM TRANS. GRAPH. (SIGGRAPH PROC
, 2004
"... We describe a novel method for controlling physicsbased fluid simulations through gradientbased nonlinear optimization. Using a technique known as the adjoint method, derivatives can be computed efficiently, even for large 3D simulations with millions of control parameters. In addition, we introdu ..."
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Cited by 76 (1 self)
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We describe a novel method for controlling physicsbased fluid simulations through gradientbased nonlinear optimization. Using a technique known as the adjoint method, derivatives can be computed efficiently, even for large 3D simulations with millions of control parameters. In addition, we introduce the first method for the full control of freesurface liquids. We show how to compute adjoint derivatives through each step of the simulation, including the fast marching algorithm, and describe a new set of control parameters specifically designed for liquids.
Infinitedimensional linear systems with unbounded control and observation: A functional analytic approach
 Transactions of the American Mathematical Society
, 1987
"... ABSTRACT. The object of this paper is to develop a unifying framework for the functional analytic representation of infinite dimensional linear systems with unbounded input and output operators. On the basis of the general approach new results are derived on the wellposedness of feedback systems and ..."
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Cited by 71 (1 self)
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ABSTRACT. The object of this paper is to develop a unifying framework for the functional analytic representation of infinite dimensional linear systems with unbounded input and output operators. On the basis of the general approach new results are derived on the wellposedness of feedback systems and on the linear quadratic control problem. The implications of the theory for large classes of functional and partial differential equations are discussed in detail. 1. Introduction. For
Realization Theory in Hilbert Space
 MATHEMATICAL SYSTEMS THEORY
, 1989
"... Abstract A representation theorem for infinitedimensional, linear control systems is proved in the context of strongly continuous semigroups in Hilbert spaces. The result allows for unbounded input and output operators and is used to derive necessary and sufficient conditions for the realizability ..."
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Cited by 56 (0 self)
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Abstract A representation theorem for infinitedimensional, linear control systems is proved in the context of strongly continuous semigroups in Hilbert spaces. The result allows for unbounded input and output operators and is used to derive necessary and sufficient conditions for the realizability in a Hilbert space of a timeinvariant, causal inputoutput operator F. The relation between inputoutput stability and stability of the realization is discussed. In the case of finitedimensional input and output spaces the boundedness of the output operator is related to the existence of a convolution kernel representing the operator F.
Optimum aerodynamic design using CFD and control theory. AIAA paper 951729
 AIAA 12th Computational Fluid Dynamics Conference
, 1995
"... This paper describes the implementation of optimization techniques based on control theory for airfoil and wing design. The theory is applied to a system defined by the partial differential equations of the flow, with control by the boundary as a free surface. The Frechet derivative of the cost func ..."
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Cited by 51 (24 self)
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This paper describes the implementation of optimization techniques based on control theory for airfoil and wing design. The theory is applied to a system defined by the partial differential equations of the flow, with control by the boundary as a free surface. The Frechet derivative of the cost function is determined via the solution of an adjoint partial differential equation, and the boundary shape is then modified in a direction of descent. This process is repeated until an optimum solution is approached. Each design cycle requires the numerical solution of both the flow and the adjoint equations, leading to a computational cost roughly equal to the cost of two flow solutions. The cost is kept low by using multigrid techniques, which yield a sufficiently accurate solution in about 15 iterations. Satisfactory designs are usually obtained with 1020 design cycles. 1 The Design Problem as a Control Problem Aerodynamic design has traditionally been carried out on a cut and try basis, with the aerodynamic expertise of the designer guiding the selection of each shape modification. Although considerable gains in aerodynamic performance have been achieved by this approach, continued improvement will most probably be much more difficult to attain. The subtlety and complexity of fluid flow is such that it is unlikely that repeated trials in an interactive analysis and design procedure can lead to a truly optimum design. Automatic design techniques are therefore needed in order to fully realize the potential improvements in aerodynamic efficiency. The simplest approach to optimization is to define the geometry through a set of design parameters, which may, for example, be the weights ai applied to a set of shape functions bi(x) so that the shape is represented as Then a cost function I is selected which might, for example, be the drag coefficient or the lift to drag ratio, and I is regarded as a function of the parameters ai. The sensitivities 5 may now be estimated by making
Distributed control of spatially invariant systems
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 2002
"... We consider distributed parameter systems where the underlying dynamics are spatially invariant, and where the controls and measurements are spatially distributed. These systems arise in many applications such as the control of vehicular platoons, flow control, microelectromechanical systems (MEMS), ..."
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Cited by 45 (0 self)
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We consider distributed parameter systems where the underlying dynamics are spatially invariant, and where the controls and measurements are spatially distributed. These systems arise in many applications such as the control of vehicular platoons, flow control, microelectromechanical systems (MEMS), smart structures, and systems described by partial differential equations with constant coefficients and distributed controls and measurements. For fully actuated distributed control problems involving quadratic criteria such as linear quadratic regulator (LQR), P and, optimal controllers can be obtained by solving a parameterized family of standard finitedimensional problems. We show that optimal controllers have an inherent degree of decentralization, and this provides a practical distributed controller architecture. We also prove a general result that applies to partially distributed control and a variety of performance criteria, stating that optimal controllers inherit the spatial invariance structure of the plant. Connections of this work to that on systems over rings, and systems with dynamical symmetries are discussed.
On exact controllability for the NavierStokes equations
 ESAIM: COCV
, 1998
"... Abstract. We study the local exact controllability problem for the NavierStokes equations that describe an incompressible fluid flow in a bounded domain Ω with control distributed in a subdomain! Ω Rn; n 2 f2; 3g. The result that we obtained in this paper is as follows. Suppose that v̂(t; x) is ..."
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Cited by 39 (1 self)
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Abstract. We study the local exact controllability problem for the NavierStokes equations that describe an incompressible fluid flow in a bounded domain Ω with control distributed in a subdomain! Ω Rn; n 2 f2; 3g. The result that we obtained in this paper is as follows. Suppose that v̂(t; x) is a given solution of the NavierStokes equations. Let v0(x) be a given initial condition and kv̂(0; )−v0k < " where " is small enough. Then there exists a locally distributed control u; suppu (0; T) ! such that the solution v(t; x) of the NavierStokes equations: