Three-dimensional computational modeling of momentum, heat, and mass transfer in a laser surface alloying process

A three- dimensional, transient model is developed for studying heat transfer, fluid flow, and mass transfer for the case of a single- pass laser surface alloying process. The coupled momentum, energy, and species conservation equations are solved using a finite volume procedure. Phase change processes are modeled using a fixed-grid enthalpy-porosity technique, which is capable of predicting the continuously evolving solid- liquid interface. The three- dimensional model is able to predict the species concentration distribution inside the molten pool during alloying, as well as in the entire cross section of the solidified alloy. The model is simulated for different values of various significant processing parameters such as laser power, scanning speed, and powder feedrate in order to assess their influences on geometry and dynamics of the pool, cooling rates, as well as species concentration distribution inside the substrate. Effects of incorporating property variations in the numerical model are also discussed.

COMPUTATIONAL MODELING OF A MULTI-LAYERED PIEZO-COMPOSITE BEAM MADE UP OF MFC

The Variational Asymptotic Method (VAM) is used for modeling a coupled non-linear electromechanical problem finding applications in aircrafts and Micro Aerial Vehicle (MAV) development. VAM coupled with geometrically exact kinematics forms a powerful tool for analyzing a complex nonlinear phenomena as shown previously by many in the literature 3 - 7] for various challenging problems like modeling of an initially twisted helicopter rotor blades, matrix crack propagation in a composite, modeling of hyper elastic plates and various multi-physics problems. The problem consists of design and analysis of a piezocomposite laminate applied with electrical voltage(s) which can induce direct and planar distributed shear stresses and strains in the structure. The deformations are large and conventional beam theories are inappropriate for the analysis. The behavior of an elastic body is completely understood by its energy. This energy must be integrated over the cross-sectional area to obtain the 1-D behavior as is typical in a beam analysis. VAM can be used efficiently to approximate 3-D strain energy as closely as possible. To perform this simplification, VAM makes use of thickness to width, width to length, width multiplied by initial twist and strain as small parameters embedded in the problem definition and provides a way to approach the exact solution asymptotically. In this work, above mentioned electromechanical problem is modeled using VAM which breaks down the 3-D elasticity problem into two parts, namely a 2-D non-linear cross-sectional analysis and a 1-D non-linear analysis, along the reference curve. The recovery relations obtained as a by-product in the cross-sectional analysis earlier are used to obtain 3-D stresses, displacements and velocity contours. The piezo-composite laminate which is chosen for an initial phase of computational modeling is made up of commercially available Macro Fiber Composites (MFCs) stacked together in an arbitrary lay-up and applied with electrical voltages for actuation. The expressions of sectional forces and moments as obtained from cross-sectional analysis in closed-form show the electro-mechanical coupling and relative contribution of electric field in individual layers of the piezo-composite laminate. The spatial and temporal constitutive law as obtained from the cross-sectional analysis are substituted into 1-D fully intrinsic, geometrically exact equilibrium equations of motion and 1-D intrinsic kinematical equations to solve for all 1-D generalized variables as function of time and an along the reference curve co-ordinate, x(1).

System reliability of randomly vibrating structures: Computational modeling and laboratory testing

The problem of determination of system reliability of randomly vibrating structures arises in many application areas of engineering. We discuss in this paper approaches based on Monte Carlo simulations and laboratory testing to tackle problems of time variant system reliability estimation. The strategy we adopt is based on the application of Girsanov's transformation to the governing stochastic differential equations which enables estimation of probability of failure with significantly reduced number of samples than what is needed in a direct simulation study. Notably, we show that the ideas from Girsanov's transformation based Monte Carlo simulations can be extended to conduct laboratory testing to assess system reliability of engineering structures with reduced number of samples and hence with reduced testing times. Illustrative examples include computational studies on a 10 degree of freedom nonlinear system model and laboratory/computational investigations on road load response of an automotive system tested on a four post Lest rig. (C) 2015 Elsevier Ltd. All rights reserved.

Computational modeling of reactive hot pressing of zirconium carbide

A model of reactive hot pressing of zirconium carbide (ZrCx, 0.5 < x < 1) has been constructed that incorporates four processes that occur in parallel: creep of zirconium (Zr), reaction of Zr and carbon (C), increase in volume fraction of hard phase with progressive reaction that reduces the creep of Zr and, finally, de-densification associated with volume reduction during reaction. The reasonable agreement of the model with experimental results verifies that plastic deformation of Zr is the main factor that is responsible for the low-temperature reactive densification of ZrC and that ZrC may be treated as a rigid inclusion that contributes little to densification. It predicts that densification is impaired by increasing carbon stoichiometry due to the increasing amount of starting hard phase and the greater contraction upon reaction. Additionally, the model predicts that mixtures of Zr and ZrC should show equal or better densification than Zr and C mixtures.

Computational modeling and psychophysics in low- and mid-level vision

This thesis addresses a series of topics related to the question of how people find the foreground objects from complex scenes. With both computer vision modeling, as well as psychophysical analyses, we explore the computational principles for low- and mid-level vision.

We first explore the computational methods of generating saliency maps from images and image sequences. We propose an extremely fast algorithm called Image Signature that detects the locations in the image that attract human eye gazes. With a series of experimental validations based on human behavioral data collected from various psychophysical experiments, we conclude that the Image Signature and its spatial-temporal extension, the Phase Discrepancy, are among the most accurate algorithms for saliency detection under various conditions.

In the second part, we bridge the gap between fixation prediction and salient object segmentation with two efforts. First, we propose a new dataset that contains both fixation and object segmentation information. By simultaneously presenting the two types of human data in the same dataset, we are able to analyze their intrinsic connection, as well as understanding the drawbacks of today’s “standard” but inappropriately labeled salient object segmentation dataset. Second, we also propose an algorithm of salient object segmentation. Based on our novel discoveries on the connections of fixation data and salient object segmentation data, our model significantly outperforms all existing models on all 3 datasets with large margins.

In the third part of the thesis, we discuss topics around the human factors of boundary analysis. Closely related to salient object segmentation, boundary analysis focuses on delimiting the local contours of an object. We identify the potential pitfalls of algorithm evaluation for the problem of boundary detection. Our analysis indicates that today’s popular boundary detection datasets contain significant level of noise, which may severely influence the benchmarking results. To give further insights on the labeling process, we propose a model to characterize the principles of the human factors during the labeling process.

The analyses reported in this thesis offer new perspectives to a series of interrelating issues in low- and mid-level vision. It gives warning signs to some of today’s “standard” procedures, while proposing new directions to encourage future research.

Computational modeling of defects in nanoscale device materials

This PhD thesis concerns the computational modeling of the electronic and atomic structure of point defects in technologically relevant materials. Identifying the atomistic origin of defects observed in the electrical characteristics of electronic devices has been a long-term goal of first-principles methods. First principles simulations are performed in this thesis, consisting of density functional theory (DFT) supplemented with many body perturbation theory (MBPT) methods, of native defects in bulk and slab models of In0.53Ga0.47As. The latter consist of (100) - oriented surfaces passivated with A12O3. Our results indicate that the experimentally extracted midgap interface state density (Dit) peaks are not the result of defects directly at the semiconductor/oxide interface, but originate from defects in a more bulk-like chemical environment. This conclusion is reached by considering the energy of charge transition levels for defects at the interface as a function of distance from the oxide. Our work provides insight into the types of defects responsible for the observed departure from ideal electrical behaviour in III-V metal-oxidesemiconductor (MOS) capacitors. In addition, the formation energetics and electron scattering properties of point defects in carbon nanotubes (CNTs) are studied using DFT in conjunction with Green’s function based techniques. The latter are applied to evaluate the low-temperature, low-bias Landauer conductance spectrum from which mesoscopic transport properties such as the elastic mean free path and localization length of technologically relevant CNT sizes can be estimated from computationally tractable CNT models. Our calculations show that at CNT diameters pertinent to interconnect applications, the 555777 divacancy defect results in increased scattering and hence higher electrical resistance for electron transport near the Fermi level.

Computational modeling techniques for reliability of electronic components on printed circuit boards

This paper describes modeling technology and its use in providing data governing the assembly and subsequent reliability of electronic chip components on printed circuit boards (PCBs). Products, such as mobile phones, camcorders, intelligent displays, etc., are changing at a tremendous rate where newer technologies are being applied to satisfy the demands for smaller products with increased functionality. At ever decreasing dimensions, and increasing number of input/output connections, the design of these components, in terms of dimensions and materials used, is playing a key role in determining the reliability of the final assembly. Multiphysics modeling techniques are being adopted to predict a range of interacting physics-based phenomena associated with the manufacturing process. For example, heat transfer, solidification, marangoni fluid flow, void movement, and thermal-stress. The modeling techniques used are based on finite volume methods that are conservative and take advantage of being able to represent the physical domain using an unstructured mesh. These techniques are also used to provide data on thermal induced fatigue which is then mapped into product lifetime predictions.

Computational modeling of mold filling and related free-surface flows in shape casting: an overview of the challenges involved

Accurate representation of the coupled effects between turbulent fluid flow with a free surface, heat transfer, solidification, and mold deformation has been shown to be necessary for the realistic prediction of several defects in castings and also for determining the final crystalline structure. A core component of the computational modeling of casting processes involves mold filling, which is the most computationally intensive aspect of casting simulation at the continuum level. Considering the complex geometries involved in shape casting, the evolution of the free surface, gas entrapment, and the entrainment of oxide layers into the casting make this a very challenging task in every respect. Despite well over 30 years of effort in developing algorithms, this is by no means a closed subject. In this article, we will review the full range of computational methods used, from unstructured finite-element (FE) and finite-volume (FV) methods through fully structured and block-structured approaches utilizing the cut-cell family of techniques to capture the geometric complexity inherent in shape casting. This discussion will include the challenges of generating rapid solutions on high-performance parallel cluster technology and how mold filling links in with the full spectrum of physics involved in shape casting. Finally, some indications as to novel techniques emerging now that can address genuinely arbitrarily complex geometries are briefly outlined and their advantages and disadvantages are discussed.

Growth mechanism of octahedron-like BaMoO4 microcrystals processed in microwave-hydrothermal: Experimental observations and computational modeling

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)