Sharp convolution and multiplication estimates in weighted spaces

2015 ◽  
Vol 13 (05) ◽  
pp. 457-480 ◽  
Author(s):  
Joachim Toft ◽  
Karoline Johansson ◽  
Stevan Pilipović ◽  
Nenad Teofanov
Keyword(s):  
Differential Equations ◽  
Stokes Equations ◽  
Weighted Spaces ◽  
Modulation Spaces ◽  
Local Analysis ◽  
Navier Stokes ◽  
Heat Equations ◽  
Navier Stokes Equations ◽  
Nonlinear Heat Equations ◽  
Hyperbolic Differential Equations

We establish sharp convolution and multiplication estimates in weighted Lebesgue, Fourier Lebesgue and modulation spaces. We cover, especially some results in [L. Hörmander, Lectures on Nonlinear Hyperbolic Differential Equations (Springer, Berlin, 1997); S. Pilipović, N. Teofanov and J. Toft, Micro-local analysis in Fourier Lebesgue and modulation spaces, II, J. Pseudo-Differ. Oper. Appl.1 (2010) 341–376]. The results are also related to some results by Iwabuchi in [T. Iwabuchi, Navier–Stokes equations and nonlinear heat equations in modulation spaces with negative derivative indices, J. Differential Equations248 (2010) 1972–2002].

Similarity Solutions for the Interaction of a Potential Vortex With Free Stream Sink Flow and a Stationary Surface

1974 ◽  
Vol 96 (1) ◽  
pp. 49-54 ◽  
Author(s):  
J. A. Hoffmann
Keyword(s):  
Boundary Layer ◽  
Differential Equations ◽  
Free Stream ◽  
Stokes Equations ◽  
Similarity Solutions ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Stationary Surface ◽  
Direct Numerical Integration ◽  
Potential Vortex

Similarity equations, using an assumed transformation which reduces the partial differential equations to sets of ordinary differential equations, are obtained from the boundary layer and the complete Navier-Stokes equations for the interaction of vortex flows with free stream sink flows and a stationary surface. Solutions to the boundary layer equations for the case of the potential vortex that satisfy the prescribed boundary conditions are shown to be nonexistent using the assumed transformation. Direct numerical integration is used to obtain solutions to the complete Navier-Stokes equations under a potential vortex with equal values of tangential and radial free stream velocities. Solutions are found for Reynolds numbers up to 2.0.

Using Functional Gains for Optimal Sensor Placement in Fluid-Structure Interaction

2009 ◽  
Author(s):  
Imran Akhtar ◽  
Jeff Borggaard ◽  
John A. Burns ◽  
Lizette Zietsman
Keyword(s):  
Differential Equations ◽  
Stokes Equations ◽  
Reduced Order Model ◽  
Navier Stokes ◽  
Cylinder Surface ◽  
Mean Flow ◽  
Order Model ◽  
Navier Stokes Equations ◽  
Reduced Order ◽  
Functional Gains

Functional gains are integral kernels of the standard feedback operator and are useful in control of partial differential equations (PDEs). These functional gains provide physical insight into how the control mechanism is operating. In some cases, these functional gains can provide information about the optimal placement of actuators and sensors. The study is motivated by fluid flow control and focuses on the computation of these functions. However, for practical purposes, one must be able to compute these functions for a wide variety of PDEs. For higher dimensional systems, computing these gains is at least as challenging as the original simulation problem. To reduce the complexity of the governing equations, reduced-order models are often developed by reducing the PDEs to ordinary-differential equations (ODEs). In this study, we use proper orthogonal decomposition (POD)-Galerkin based approach and develop a reduced-order model of a bluff body wake. We solve the incompressible Navier-Stokes equations, simulate the flow past a circular cylinder, and record the snapshots of the flow field. We compute the POD eigenfunctions and project the Navier-Stokes equations onto these few of these eigenfunctions to develop a reduced-order model. Later, we modify the model by introducing a control function simulating suction actuation on the cylinder surface. We linearize the model about the mean flow and apply feedback control to suppress vortex shedding. We then compute the functional gains for the applied control. We identify these gains at various stations in the wake region and suggest optimum locations for the sensors.

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