Control and estimation of fluid systems is a challenging problem that requires approximating high-dimensional, nonlinear dynamics with computationally tractable models. A number techniques, such as proper orthogonal decomposition (POD) dynamic mode (DMD) have been developed to derive reduced-order In this letter, the selecting dynamically important modes control (DMDc) addressed. Similar sparsity-promoting DMD, method described in letter solves convex optimization order determine most modes....

In this work, we consider the problem of steering first two moments uncertain state a discrete-time nonlinear stochastic system to prescribed goal quantities at given final time. We propose tractable and intuitive approach which relies on greedy control policy is comprised elements policies that solve sequence corresponding linearized covariance problems. Each latter problems associated with (finite-dimensional) convex program. At each stage, information statistics updated by computing...

Turbulent boundary layers are dominated by large-scale motions (LSMs) of streamwise momentum surplus and deficit that contribute significantly to the statistics flow. In particular, high-momentum LSMs residing in outer region layer have potential re-energize flow delay separation if brought closer wall. This work explores effect selectively manipulating a moderate Reynolds number turbulent for via well-resolved large-eddy simulations. Toward goal, model predictive control scheme is developed...

Abstract In this work, we consider the problem of learning a reduced-order model high-dimensional stochastic nonlinear system with control inputs from noisy data. particular, develop hybrid parametric/nonparametric that learns “average” linear dynamics in data using dynamic mode decomposition (DMDc) and nonlinearities uncertainties Gaussian process (GP) regression compare it total least-squares (tlsDMD), extended here to systems (tlsDMDc). The proposed approach is also compared existing...

In this paper, a model predictive control algorithm for selectively steering material volumes in boundary layer is proposed. Using direct numerical simulations of laminar with Gaussian-distributed force field as the actuator, reduced-order linear wall-normal velocity dynamics on grid covering neighborhood actuator derived using sparsity-promoting dynamic mode decomposition control. The spatial evolution target volume probabilistically described by Gaussian mixture propagated Taylor's...

In this paper, we address the problem of steering a team agents under stochastic linear dynamics to prescribed final state means and covariances.The operate in common environment where inter-agent constraints may also be present.In order for our method scalable largescale systems computationally efficient, approach distributed control framework using Alternating Direction Method Multipliers (ADMM).Each agent solves its own covariance parallel, while additional copy variables closest...

Turbulent boundary layers are largely influenced by spatiotemporally developing coherent structures known as Large-Scale Motions (LSMs). This work examines the idea of creating synthetic hairpin trains, a model for LSMs, generated in nominal zero pressure gradient laminar layer. The study investigates agreement between experimentally measured flow field and vortices its simulated counterpart with hybrid 2D inlet region. approach uses time-varying unsteady spatially discrete velocity data...

In this work, we consider the problem of steering first two moments uncertain state an unknown discrete-time stochastic nonlinear system to a given terminal distribution in finite time. Toward that goal, first, non-parametric predictive model is learned from set available training data points using variational Gaussian process regression: powerful and scalable machine learning tool for distributions over arbitrary functions. Second, formulate tractable covariance algorithm utilizes compute...

Coherent structures in wall-bounded flows, such as large-scale motions (LSMs), contribute significantly to the dynamics of turbulent boundary layers: they contain a large fraction kinetic energy, add average Reynolds shear stresses, and transport momentum. Therefore, one may wish control turbulence by manipulating movement these LSMs order achieve desired performance gain (such drag reduction, noise or mixing enhancement). In present work, we approach above problem an abstract way developing...

In this work, we present a method for estimating the unsteady flowfield of fluid system with unknown model parameters (such as angle attack or Reynolds number) in real time from limited number sensor measurements using "bank" Dynamic Mode Decomposition (DMD) models. First, set DMD models is computed at sample values parameter. Then, bank Kalman filters run each one models, yielding state estimate and, thus, corresponding estimate. Finally, minimum mean-squared error (MMSE) actual weighted...

In this work, we consider the problem of steering first two moments uncertain state an unknown discrete-time stochastic nonlinear system to a given terminal distribution in finite time. Toward that goal, first, non-parametric predictive model is learned from set available training data points using variational Gaussian process regression: powerful and highly scalable machine learning tool for distributions over arbitrary functions. Second, formulate tractable covariance algorithm utilizes...

For aerodynamic and conjugate heat transfer problems, the work reported in this article investigated effect of grid displacement model on derivatives an objective function with respect to design variables. A comparison was made first step study between reference sensitivity computed by finite differences different models (volumetric B-splines, Laplace partial differential equations, Delaunay graphs inverse distance weighting). Then, a continuous adjoint method including used recompute...

In this work, we consider the problem of steering first two moments uncertain state a discrete time nonlinear stochastic system to prescribed goal quantities at given final time. principle, latter can be formulated as density tracking problem, which seeks for feedback policy that will keep probability function close, in terms an appropriate metric, density. The solution infinite-dimensional be, however, complex and computationally expensive task. Instead, propose more tractable intuitive...

Large Scale Motions (LSMs) are coherent structures that naturally occur in a turbulent boundary layer. These vortical carry significant portion of the kinetic energy (TKE) and have potential to actively change dynamics We, therefore, argue by systematically manipulating these structures, one can leverage their for technological benefits, such as separation control, drag reduction, or mixing enhancement. In our previous work, it was shown steering laminar layer increase near wall (Tsolovikos...