Drag and Noise Reduction of Flatback Airfoil by Span-wise Wavy Trailing Edge

The flatback airfoil is a promising idea for future large wind turbine blade structure design; however, it causes notable drag increase and low frequency tonal noise due to the presence of span-wise coherent standing flow and Karman vortex shedding at the trailing edge. Current dissertation proposes a span-wise wavy trailing edge design as a solution to flatback airfoil drag and noise, and provides relevant CFD results. Proposed span-wise wavy trailing edge prevents the span-wise coherent standing flow and vortex shedding, and results in a decrease of the tonal noise and pressure drag of the airfoil. Delayed Detached Eddy Simulation is employed in HPC environments. In-house developed N-S solvers, OVERTURNS (CPU-based) and GPURANS3D (GPGPU) are used for the computation. A design parametric study for the span-wise wavy trailing edge is conducted in the first half of the dissertation. Aerodynamic and aeroacoustic performance of a particular flatback airfoil (FB3500-1750, tTE is 17.5% of a chord length) and various span-wise wavy trailing edge modifications are investigated, regarding the influence of the major wave design parameters. A total of 16 design variations of the wavy trailing edge are investigated. For a Reynolds number 666,000 and Mach number 0.3, the best trailing edge wave design is a combination of the less portion - more than 0.25c length - shallow wave depth; the best design reduces 60% (maximum) of the flatback airfoil with only 7% lift loss, and results in about 150% (maximum) of lift/drag ratio increase. Measured tonal noise is also reduced by about 20-25dB(SPL) with the best performance design. In the second half of the dissertation, the isolated rotor simulation is performed for a straight-flat trailing edge wind turbine blade and its wavy trailing edge modification. In the simulation, the Blunt-Wavy trailing edge (a combination of trailing edge augmentation and wavy modification) is proposed and applied at the inboard of baseline blade (SNL100-03FB). Applying the best performance wavy design, overall turbine power generation is increased by 2.62% and tonal noise is decreased by 5-15dB

Estimation of Perceptual Surface Property Using Deep Networks with Attention Models

How we perceive property of surfaces with distinct geometry and reflectance under various illumination conditions is not fully understood. One widely studied approach to understanding perceptual surface property is to derive statistics from images of surfaces with the goal of constructing models that can estimate surface property attributes. This work presents machine learning-based methods to estimate the lightness and glossiness of surfaces. Instead of deriving image statistics and building estimation models on top of them, we use deep networks to estimate the perceptual surface property directly from surface images. We adopt the attention models in our networks, to allow the networks to estimate the surface property based on features in certain parts of images. This approach can rule out image variations due to geometry, reflectance, and illumination when making the estimations. The networks are trained with perceptual lightness and glossiness data obtained from psychophysical experiments. The trained deep networks provide accurate estimations of surface property that correlate well with human perception. The network performances are compared with various image statistics derived for estimation of perceptual surface property

Modeling nonstationary lens blur using eigen blur kernels for restoration

Images acquired through a lens show nonstationary blur due to defocus and optical aberrations. This paper presents a method for accurately modeling nonstationary lens blur using eigen blur kernels obtained from samples of blur kernels through principal component analysis. Pixelwise variant nonstationary lens blur is expressed as a linear combination of stationary blur by eigen blur kernels. Operations that represent nonstationary blur can be implemented efficiently using the discrete Fourier transform. The proposed method provides a more accurate and efficient approach to modeling nonstationary blur compared with a widely used method called the efficient filter flow, which assumes stationarity within image regions. The proposed eigen blur kernel-based modeling is applied to total variation restoration of nonstationary lens blur. Accurate and efficient modeling of blur leads to improved restoration performance. The proposed method can be applied to model various nonstationary degradations of image acquisition processes, where degradation information is available only at some sparse pixel locations. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Virtual portraits from rotating selfies

Selfies are a popular form of photography. However, due to physical constraints, the compositions of selfies are limited. We present algorithms for creating virtual portraits with interesting compositions from a set of selfies. The selfies are taken at the same location while the user spins around. The scene is analyzed using multiple selfies to determine the locations of the camera, subject, and background. Then, a view from a virtual camera is synthesized. We present two use cases. After rearranging the distances between the camera, subject, and background, we render a virtual view from a camera with a longer focal length. Following that, changes in perspective and lens characteristics caused by new compositions and focal lengths are simulated. Second, a virtual panoramic view with a larger field of view is rendered, with the user's image placed in a preferred location. In our experiments, virtual portraits with a wide range of focal lengths were obtained using a device equipped with a lens that has only one focal length. The rendered portraits included compositions that would be photographed with actual lenses. Our proposed algorithms can provide new use cases in which selfie compositions are not limited by a camera's focal length or distance from the camera

Modeling Non-Stationary Asymmetric Lens Blur By Normal Sinh-Arcsinh Model

Department of Electrical EngineeringImages acquired by a camera show lens blur due to imperfection in the optical system. Lens blur is non-stationary in a sense the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a sense the amount of blur is different in the radial and tangential directions, and also in the inward and outward radial directions. This paper presents parametric blur kernel models based on the normal sinh-arcsinh distribution function. The proposed models can provide flexible shapes of blur kernels with different symmetry and skewness to model complicated lens blur accurately. Blur of single focal length lenses is estimated and the accuracy of the models is compared with existing parametric blur models. Advantage of the proposed models is demonstrated through deblurring experiments.ope

ADMM in Krylov Subspace and Its Application to Total Variation Restoration of Spatially Variant Blur

In this paper we propose an efficient method for a convex optimization problem which involves a large nonsymmetric and non-Toeplitz matrix. The proposed method is an instantiation of the alternating direction method of multipliers applied in Krylov subspace. Our method offers significant advantages in computational speed for the convex optimization problems involved with general matrices of large size. We apply the proposed method to the restoration of spatially variant blur. The matrix representing spatially variant blur is not block circulant with circulant blocks (BCCB). Efficient implementation based on diagonalization of BCCB matrices by the discrete Fourier transform is not applicable for spatially variant blur. Since the proposed method can efficiently work with general matrices, the restoration of spatially variant blur is a good application of our method. Experimental results for total variation restoration of spatially variant blur show that the proposed method provides meaningful solutions in a short time.clos

A Case of Nasal Septum Gossypiboma 14 Years After Septorhinoplasty

Gossypiboma, an infrequent surgical complication, describes a mass of cotton material inadvertently left in the body cavity after an operation. It is an extremely rare iatrogenic complication of nasal surgery, with only a few cases reported in literature to date. Here we present a case of gossypiboma in the nasal septum of a 35-year-old male patient who previously underwent septorhinoplasty fourteen years prior. He was treated by endoscopic endonasal surgery to remove the lesion. Pathologic findings showed a foreign body (gauze filament) with a giant cell reaction. This report will be helpful for treating patients with similar histories in the future