Experimental Data Analysis of the Vortex Structures in the Wakes of Flapping Wings
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The Department of Aerospace Engineering seeks to instruct the design, analysis, testing, and operation of vehicles which operate in air, water, or space, including studies of aerodynamics, structure mechanics, propulsion, and the like.
History
The Department of Aerospace Engineering was organized as the Department of Aeronautical Engineering in 1942. Its name was changed to the Department of Aerospace Engineering in 1961. In 1990, the department absorbed the Department of Engineering Science and Mechanics and became the Department of Aerospace Engineering and Engineering Mechanics. In 2003 the name was changed back to the Department of Aerospace Engineering.
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1942-present
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- Department of Aerospace Engineering and Engineering Mechanics (1990-2003)
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- College of Engineering (parent college)
- Department of Engineering Science and Mechanics (merged with, 1990)
The Department of Electrical and Computer Engineering (ECpE) contains two focuses. The focus on Electrical Engineering teaches students in the fields of control systems, electromagnetics and non-destructive evaluation, microelectronics, electric power & energy systems, and the like. The Computer Engineering focus teaches in the fields of software systems, embedded systems, networking, information security, computer architecture, etc.
History
The Department of Electrical Engineering was formed in 1909 from the division of the Department of Physics and Electrical Engineering. In 1985 its name changed to Department of Electrical Engineering and Computer Engineering. In 1995 it became the Department of Electrical and Computer Engineering.
Dates of Existence
1909-present
Historical Names
- Department of Electrical Engineering (1909-1985)
- Department of Electrical Engineering and Computer Engineering (1985-1995)
Related Units
- College of Engineering (parent college)
- Department of Physics and Electrical Engineering (predecessor)
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Abstract
The objective of this paper is to compare the existing methods and develop novel approaches for the experimental data analysis of the unsteady aerodynamics of the flapping wing microaerial-vehicle. These methods are developed for the purpose of identification of the beneficial dynamics and for the development of reduced order models for control design. We first employ Proper Orthogonal Decomposition (POD) method for the data analysis of the PIV measurements in the wakes of piezoelectric flapping wings. The basic idea behind POD based data analysis method is to decompose the time series snapshots of PIV measurements into high energy, POD, modes. The POD modes obtained from the PIV measurement data with different control inputs, such as flapping amplitude, angle of attack and flight speed, are compared to identify high energy modes that are invariant across the range of operating conditions. Similarly the modes that are responsible for maximum energy transfer between the control inputs and the desired output such as lift and thrust are identified. The second method that we propose for the PIV data analysis is inspired from our recent work to develop a novel approach for the spectral analysis of the nonlinear flows. This new method is based on the spectral analysis of the linear transfer operator, the so called Koopman operator, associated with any nonlinear flows. The motivation for this work comes from the desire to perform frequency-based decomposition of the snapshot data as opposed to energy based decomposition in the POD method. While POD-based data analysis method captures all high energy content modes, it ignores the low energy content modes. These low energy modes might play an important role from the dynamics point of view and hence cannot be ignored. We perform the spectral analysis of the linear transfer, Perron-Frobenius (P-F) operator, which is dual to the Koopman operator to obtain the frequency based decomposition of the time series snapshot data. The basic idea behind this approach is to construct the finite dimensional approximation of the linear transfer (P-F) operator that best describes the time evolution of the snapshots data. The eigenvalues and eigenvectors of this transfer operator carry useful information about the system dynamics.
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This proceeding was published as Kai Wang, Umesh Vaidya, Hui Hu, and Baskar Ganapathysubramanian. "Experimental Data Analysis of the Vortex Structures in the Wakes of Flapping Wings", 28th AIAA Applied Aerodynamics Conference, Fluid Dynamics and Co-located Conferences, doi:10.2514/6.2010-5078 . Posted with permission.