Aerospace Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2737
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Item SCHLIEREN SEQUENCE ANALYSIS USING COMPUTER VISION(2013) Smith, Nathanial Timothy; Lewis, Mark J; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Computer vision-based methods are proposed for extraction and measurement of flow structures of interest in schlieren video. As schlieren data has increased with faster frame rates, we are faced with thousands of images to analyze. This presents an opportunity to study global flow structures over time that may not be evident from surface measurements. A degree of automation is desirable to extract flow structures and features to give information on their behavior through the sequence. Using an interdisciplinary approach, the analysis of large schlieren data is recast as a computer vision problem. The double-cone schlieren sequence is used as a testbed for the methodology; it is unique in that it contains 5,000 images, complex phenomena, and is feature rich. Oblique structures such as shock waves and shear layers are common in schlieren images. A vision-based methodology is used to provide an estimate of oblique structure angles through the unsteady sequence. The methodology has been applied to a complex flowfield with multiple shocks. A converged detection success rate between 94% and 97% for these structures is obtained. The modified curvature scale space is used to define features at salient points on shock contours. A challenge in developing methods for feature extraction in schlieren images is the reconciliation of existing techniques with features of interest to an aerodynamicist. Domain-specific knowledge of physics must therefore be incorporated into the definition and detec- tion phases. Known location and physically possible structure representations form a knowledge base that provides a unique feature definition and extraction. Model tip location and the motion of a shock intersection across several thousand frames are identified, localized, and tracked. Images are parsed into physically meaningful labels using segmentation. Using this representation, it is shown that in the double-cone flowfield, the dominant unsteady motion is associated with large scale random events within the aft-cone bow shock. Small scale organized motion is associated with the shock-separated flow on the fore-cone surface. We show that computer vision is a natural and useful extension to the evaluation of schlieren data, and that segmentation has the potential to permit new large scale measurements of flow motion.Item Automated Kinematic Extraction of Wing and Body Motions of Free Flying Diptera(2012) Kostreski, Nicholas Ivan; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the quest to understand the forces generated by micro aerial systems powered by oscillating appendages, it is necessary to study the kinematics that generate those forces. Automated and manual tracking techniques were developed to extract the complex wing and body motions of dipteran insects, ideal micro aerial systems, in free flight. Video sequences were captured by three high speed cameras (7500 fps) oriented orthogonally around a clear flight test chamber. Synchronization and image-based triggering were made possible by an automated triggering circuit. A multi-camera calibration was implemented using image-based tracking techniques. Three-dimensional reconstructions of the insect were generated from the 2-D images by shape from silhouette (SFS) methods. An intensity based segmentation of the wings and body was performed using a mixture of Gaussians. In addition to geometric and cost based filtering, spectral clustering was also used to refine the reconstruction and Principal Component Analysis (PCA) was performed to find the body roll axis and wing-span axes. The unobservable roll state of the cylindrically shaped body was successfully estimated by combining observations of the wing kinematics with a wing symmetry assumption. Wing pitch was determined by a ray tracing technique to compute and minimize a point-to-line cost function. Linear estimation with assumed motion models was accomplished by discrete Kalman filtering the measured body states. Generative models were developed for different species of diptera for model based tracking, simulation, and extraction of inertial properties. Manual and automated tracking results were analyzed and insect flight simulation videos were developed to quantify ground truth errors for an assumed model. The results demonstrated the automated tracker to have comparable performance to a human digitizer, though manual techniques displayed superiority during aggressive maneuvers and image blur. Both techniques demonstrated non-intrusive methods for establishing reference flight kinematics, which are being used to develop flight dynamics models in future work.