Doctor of Philosophy

dissertationWhile boundary representations, such as nonuniform rational B-spline (NURBS) surfaces, have traditionally well served the needs of the modeling community, they have not seen widespread adoption among the wider engineering discipline. There is a common perception that NURBS are slow to evaluate and complex to implement. Whereas computer-aided design commonly deals with surfaces, the engineering community must deal with materials that have thickness. Traditional visualization techniques have avoided NURBS, and there has been little cross-talk between the rich spline approximation community and the larger engineering field. Recently there has been a strong desire to marry the modeling and analysis phases of the iterative design cycle, be it in car design, turbulent flow simulation around an airfoil, or lighting design. Research has demonstrated that employing a single representation throughout the cycle has key advantages. Furthermore, novel manufacturing techniques employing heterogeneous materials require the introduction of volumetric modeling representations. There is little question that fields such as scientific visualization and mechanical engineering could benefit from the powerful approximation properties of splines. In this dissertation, we remove several hurdles to the application of NURBS to problems in engineering and demonstrate how their unique properties can be leveraged to solve problems of interest

A methodology for evaluating the performance of tow-steered composite technology over a range of planform configurations

Tow-steered composite technology allows for composite fibers to be arranged in customized curving paths rather than in conventional straight lines. This additional design freedom can provide passive load alleviation and increased load path efficiency, which can lead to reduced structural weight, higher wing aspect ratio, and ultimately better vehicle performance. To best take advantage of this technology the weight reduction should be accounted for during the conceptual design stage, when the vehicle’s configuration is still fluid. Since the technology effect could depend on the planform it must be assessed across the range of potential planforms, motivating the development of the thesis methodology. Evaluating tow steering’s technology benefit presents a challenge: due to a lack of historical data the effects must be quantified with parametric physics-based analysis, incurring both development and computational expenses. Additionally, determining the benefit requires repeatedly performing the analysis to solve a high-dimensional constrained optimization problem. In order to better leverage existing weight estimation programs and lessen the impact of computational expense the methodology pursues a surrogate modeling approach. Two main research focuses were addressed while developing the methodology. The first explored how to cope with the large number of dimensions when making the surrogate and applied an active subspace approach to attempt to reduce the dimensionality of the associated constrained optimization problem. The second focus investigated how to most efficiently collect data to build the surrogate and led to the development of an adaptive sampling technique for families of related optimization problems. The findings from these efforts were synthesized to form the thesis methodology, which was then demonstrated in an example use case. The results from this use case were examined to assess the methodology’s successes and limitations, and to provide insight into the behavior of tow-steered composites.Ph.D

Integrated modeling and analysis methodologies for architecture-level vehicle design.

In order to satisfy customer expectations, a ground vehicle must be designed to meet a broad range of performance requirements. A satisfactory vehicle design process implements a set of requirements reflecting necessary, but perhaps not sufficient conditions for assuring success in a highly competitive market. An optimal architecture-level vehicle design configuration is one of the most important of these requirements. A basic layout that is efficient and flexible permits significant reductions in the time needed to complete the product development cycle, with commensurate reductions in cost. Unfortunately, architecture-level design is the most abstract phase of the design process. The high-level concepts that characterize these designs do not lend themselves to traditional analyses normally used to characterize, assess, and optimize designs later in the development cycle. This research addresses the need for architecture-level design abstractions that can be used to support ground vehicle development. The work begins with a rigorous description of hierarchical function-based abstractions representing not the physical configuration of the elements of a vehicle, but their function within the design space. The hierarchical nature of the abstractions lends itself to object orientation - convenient for software implementation purposes - as well as description of components, assemblies, feature groupings based on non-structural interactions, and eventually, full vehicles. Unlike the traditional early-design abstractions, the completeness of our function-based hierarchical abstractions, including their interactions, allows their use as a starting point for the derivation of analysis models. The scope of the research in this dissertation includes development of meshing algorithms for abstract structural models, a rigid-body analysis engine, and a fatigue analysis module. It is expected that the results obtained in this study will move systematic design and analysis to the earliest phases of the vehicle development process, leading to more highly optimized architectures, and eventually, better ground vehicles. This work shows that architecture level abstractions in many cases are better suited for life cycle support than geometric CAD models. Finally, substituting modeling, simulation, and optimization for intuition and guesswork will do much to mitigate the risk inherent in large projects by minimizing the possibility of incorporating irrevocably compromised architecture elements into a vehicle design that no amount of detail-level reengineering can undo

High dimensional data analysis for anomaly detection and quality improvement

Analysis of large-scale high-dimensional data with a complex heterogeneous data structure to extract information or useful features is vital for the purpose of data fusion for assessment of system performance, early detection of system anomalies, intelligent sampling and sensing for data collection and decision making to achieve optimal system performance. Chapter 3 focuses on detecting anomalies from high-dimensional data. Traditionally, most of the image-based anomaly detection methods perform denoising and detection sequentially, which affects detection accuracy and efficiency. In this chapter, A novel methodology, named smooth-sparse decomposition (SSD), is proposed to exploit regularized high-dimensional regression to decompose an image and separate anomalous regions simultaneously by solving a large-scale optimization problem. Chapter 4 extends this to spatial-temporal functional data by extending SSD to spatiotemporal smooth-sparse decomposition (ST-SSD), with a likelihood ratio test to detect the time of change accurately based on the detected anomaly. To enable real-time implementation of the proposed methodology, recursive estimation procedures for ST-SSD are also developed. The proposed methodology is also applied to tonnage signals, rolling inspection data and solar flare monitoring. Chapter 5 considers the adaptive sampling problem for high-dimensional data. A novel adaptive sampling framework, named Adaptive Kernelized Maximum-Minimum Distance is proposed to adaptively estimate the sparse anomalous region. The proposed method balances the sampling efforts between the space filling sampling (exploration) and focused sampling near the anomalous region (exploitation). The proposed methodology is also applied to a case study of anomaly detection in composite sheets using a guided wave test. Chapter 6 explores the penalized tensor regression to model the tensor response data with the process variables. Regularized Tucker decomposition and regularized tensor regression methods are developed, which model the structured point cloud data as tensors and link the point cloud data with the process variables. The performance of the proposed method is evaluated through simulation and a real case study of turning process optimization.Ph.D

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Development of a digital image correlation software for full-field strain analysis of CFRP

Le proprietà meccaniche sono tra le più importanti proprietà di ogni materiale usato in differenti applicazioni nell'industria, costruzione, medicina, etc. Il presente lavoro si è focalizzato sulla misurazione delle deformazioni. Questo studio inizia con un'indagine sui metodi di misurazione delle deformazioni. I metodi tradizionali più importanti sono introdotti. Questa indagine è stata realizzata principalmente su estensometri d estensimetri. E' presente anche una breve descrizione dei metodi a campo pieno. Il lavoro continua con un breve descrizione dei materiali compositi focalizzandosi principalmente sui materiali fibro rinforzati e specialmente sui materiali polimerici rinforzati in fibra di carbonio. La parte principale del lavoro riguarda la correlazione digitale delle immagini (DIC). Lo scopo è lo sviluppo di un software per la misurazione delle deformazioni usando il metodo DIC, presentando anche alcune applicazioni. Alcuni esperimenti sono stati progettati per i materiali compositi in fibra di carbonio, ghisa sferoidale e lega di alluminio. I risultati ottenuti con il software sono stati confrontati con un software commerciale e con alcuni campi di deformazione artificiali. Alla fine dello studio, considerando lo stretto legame che può esserci tra DIC ed elementi finiti, il lavoro si focalizza sullo sviluppo di un modello numerico, presentando un modello agli elementi finiti anisotropo e non lineare per materiali compositi multi-strato multi-assiali.Mechanical properties are one of the most important properties of every material being used in different applications in industries, construction, medicine, etc.The present work have focused on strain measurement. This study starts with an investigation on the strain measurement methods. The most important traditional strain measurement methods are introduced. This investigation has made mainly on extensometers and strain-gages. It has also a brief review about full-field techniques. Then the work continues with a brief summary on composite materials focusing mainly on fiber reinforced composite materials and specially on carbon fiber reinforced polymers. The main part of the work deals with digital image correlation (DIC). Its aim is the development of a software for the strain measurements by DIC method, presenting also some applications. Some tests have been designed for carbon fiber reinforced plastic materials, nodular cast iron and aluminum alloy. The results of applying this software have been compared with the results of a commercial software and with some artificial strain behavior. At the end of the study, regarding to the strong relation that can be established between DIC and finite element modeling, the work focuses on the development of a numerical model, presenting an anisotropic non-linear FE modeling of non-crimp fabric composites

Meshless methods: theory and application in 3D fracture modelling with level sets

Accurate analysis of fracture is of vital importance yet methods for effetive 3D calculations are currently unsatisfactory. In this thesis, novel numerical techniques are developed which solve many of these problems. This thesis consists two major parts: firstly an investigation into the theory of meshless methods and secondly an innovative numerical framework for 3D fracture modelling using the element-free Galerkin method and the level set method. The former contributes to some fundamental issues related to accuracy and error control in meshless methods needing to be addressed for fracture modelling developed later namely, the modified weak form for imposition of essential boundary conditions, the use of orthogonal basis functions to obtain shape functions and error control in adaptive analysis. In the latter part, a simple and efficient numerical framework is developed to overcome the difficulties in current 3D fracture modelling. Modelling cracks in 3D remains a challenging topic in computational solid mechanics since the geometry of the crack surfaces can be difficult to describe unlike the case in 2D where cracks can be represented as combinations of lines or curves. Secondly, crack evolution requires numerical methods that can accommodate the moving geometry and a geometry description that maintains accuracy in successive computational steps. To overcome these problems, the level set method, a powerful numerical method for describing and tracking arbitrary motion of interfaces, is used to describe and capture the crack geometry and forms a local curvilinear coordinate system around the crack front. The geometry information is used in the stress analysis taken by the element-Free Galerkin method as well as in the computation of fracture parameters needed for crack propagation. Examples are tested and studied throughout the thesis addressing each of the above described issues