3,113 research outputs found
Cluster-based feedback control of turbulent post-stall separated flows
We propose a novel model-free self-learning cluster-based control strategy
for general nonlinear feedback flow control technique, benchmarked for
high-fidelity simulations of post-stall separated flows over an airfoil. The
present approach partitions the flow trajectories (force measurements) into
clusters, which correspond to characteristic coarse-grained phases in a
low-dimensional feature space. A feedback control law is then sought for each
cluster state through iterative evaluation and downhill simplex search to
minimize power consumption in flight. Unsupervised clustering of the flow
trajectories for in-situ learning and optimization of coarse-grained control
laws are implemented in an automated manner as key enablers. Re-routing the
flow trajectories, the optimized control laws shift the cluster populations to
the aerodynamically favorable states. Utilizing limited number of sensor
measurements for both clustering and optimization, these feedback laws were
determined in only iterations. The objective of the present work is not
necessarily to suppress flow separation but to minimize the desired cost
function to achieve enhanced aerodynamic performance. The present control
approach is applied to the control of two and three-dimensional separated flows
over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of
, Reynolds number and free-stream Mach number . The optimized control laws effectively minimize the flight power
consumption enabling the flows to reach a low-drag state. The present work aims
to address the challenges associated with adaptive feedback control design for
turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure
Multivariable norm optimal iterative learning control with auxiliary optimization
The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously converging to the solution of a constrained quadratic optimization problem. The theory is presented in a general functional analytical framework using operators between chosen real Hilbert spaces. This is applied to solve problems in continuous time where tracking is only required at selected intermediate points of the time interval but, simultaneously, the solution is required to minimize a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. Applications to the discrete time case, including the case of multi-rate sampling, are also summarized. The algorithms are motivated by practical need and provide a methodology for reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear whilst maintaining the desired tracking accuracy necessary for task completion. Solutions in terms of NOILC methodologies involving both feedforward and feedback components offer the possibilities of greater robustness than purely feedforward actions. Robustness of the feedforward implementation is discussed and the work is illustrated by experimental results from a robotic manipulator
Designing structured tight frames via an alternating projection method
Tight frames, also known as general Welch-bound- equality sequences, generalize orthonormal systems. Numerous applications - including communications, coding, and sparse approximation- require finite-dimensional tight frames that possess additional structural properties. This paper proposes an alternating projection method that is versatile enough to solve a huge class of inverse eigenvalue problems (IEPs), which includes the frame design problem. To apply this method, one needs only to solve a matrix nearness problem that arises naturally from the design specifications. Therefore, it is the fast and easy to develop versions of the algorithm that target new design problems. Alternating projection will often succeed even if algebraic constructions are unavailable. To demonstrate that alternating projection is an effective tool for frame design, the paper studies some important structural properties in detail. First, it addresses the most basic design problem: constructing tight frames with prescribed vector norms. Then, it discusses equiangular tight frames, which are natural dictionaries for sparse approximation. Finally, it examines tight frames whose individual vectors have low peak-to-average-power ratio (PAR), which is a valuable property for code-division multiple-access (CDMA) applications. Numerical experiments show that the proposed algorithm succeeds in each of these three cases. The appendices investigate the convergence properties of the algorithm
An iterative multi-fidelity approach for model order reduction of multi-dimensional input parametric PDE systems
We propose a parametric sampling strategy for the reduction of large-scale
PDE systems with multidimensional input parametric spaces by leveraging models
of different fidelity. The design of this methodology allows a user to
adaptively sample points ad hoc from a discrete training set with no prior
requirement of error estimators. It is achieved by exploiting low-fidelity
models throughout the parametric space to sample points using an efficient
sampling strategy, and at the sampled parametric points, high-fidelity models
are evaluated to recover the reduced basis functions. The low-fidelity models
are then adapted with the reduced order models ( ROMs) built by projection onto
the subspace spanned by the recovered basis functions. The process continues
until the low-fidelity model can represent the high-fidelity model adequately
for all the parameters in the parametric space. Since the proposed methodology
leverages the use of low-fidelity models to assimilate the solution database,
it significantly reduces the computational cost in the offline stage. The
highlight of this article is to present the construction of the initial
low-fidelity model, and a sampling strategy based on the discrete empirical
interpolation method (DEIM). We test this approach on a 2D steady-state heat
conduction problem for two different input parameters and make a qualitative
comparison with the classical greedy reduced basis method (RBM), and further
test on a 9-dimensional parametric non-coercive elliptic problem and analyze
the computational performance based on different tuning of greedy selection of
points
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