To enhance material performance at different length scales, this study strives to develop a reliable analytical and computational tool with the help of internal state variables spanning micro and macro-level behaviors. First, the practical relevance of a nonlocal damage integral added to an internal state variable (BCJ) model is studied to alleviate numerical instabilities associated within the post-bifurcation regime. The characteristic length scale in the nonlocal damage, which is mathematical in nature, can be calibrated using a series of notch tensile tests. Then the same length scale from the notch tests is used in solving the problem of a high-velocity (between 89 and 107 m/s) rigid projectile colliding against a 6061-T6 aluminum-disk. The investigation indicates that incorporating a characteristic length scale to the constitutive model eliminates the pathological mesh-dependency associated with material instabilities. In addition, the numerical calculations agree well with experimental data. Next, an effort is made rather to introduce a physically motivated length scale than to apply a mathematical-one in the deformation analysis. Along this line, a dislocation based plasticity model is developed where an intrinsic length scale is introduced in the forms of spatial gradients of mobile and immobile dislocation densities. The spatial gradients are naturally invoked from balance laws within a consistent kinematic and thermodynamic framework. An analytical solution of the model variables is derived at homogenous steady state using the linear stability and bifurcation analysis. The model qualitatively captures the formation of dislocation cell-structures through material instabilities at the microscopic level. Finally, the model satisfactorily predicts macroscopic mechanical behaviors - e.g., multi-strain rate uniaxial compression, simple shear, and stress relaxation - and validates experimental results.

The objective of this dissertation centers on gaining a better understanding of the structure - property - performance relations of nickel and copper through the advanced multiscale theoretical framework and integrated computational methods. The goal of this dissertation also includes to combine material science and computational mechanics to acquire a transformative understanding of how the different crystal orientations, size scales, and penetration velocities affect plastic deformation and damage behavior of metallic materials during high strain rate (> 103s-1) processes. A multiscale computational framework for understanding plasticity and shearing mechanisms of metallic materials during the high rate process was developed, which for the first time reveals micromechanical insights on how different crystal orientations, size scales, and penetration velocities affect the atomistic simulations which render structure property information for plasticity, shearing and damage mechanisms. The contributions of this dissertation include: (1) Comprehensive understanding of the plasticity and shearing mechanisms between the nickel penetrator and copper target under high strain rates (2) Development of a multiscale study of a nickel penetrator striking a copper plate by employing macroscale simulations and atomistic simulations to better understand the micromechanisms. (3) An essential description of how different crystal orientations, size scales, and strain rates affect the plasticity and shearing mechanisms.

Coupling of material, process, and performance models is an important step towards a fully integrated material-process-performance design of structural components. In this research, alternative approaches for introducing the effects of manufacturing and material microstructure in plasticity constitutive models are studied, and a cyberinfrastructure framework is developed for coupled process-performance simulation and optimization of energy absorbing components made of magnesium alloys. The resulting mixed boundary/initial value problem is solved using nonlinear finite element analysis whereas the optimization problem is decomposed into a hierarchical multilevel system and solved using the analytical target cascading methodology. The developed framework is demonstrated on process-performance optimization of a sheetormed, energy-absorbing component using both classical and microstructure-based plasticity models. Sheetorming responses such as springback, thinning, and rupture are modeled and used as manufacturing process attributes whereas weight, mean crush force, and maximum crush force are used as performance attributes. The simulation and optimization results show that the manufacturing effects can have a considerable impact on design of energy absorbing components as well as the optimum values of process and product design variables.

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No / Since batch reactors are generally applied to produce a wide variety of specialty products, there is a great deal of interest to enhance batch operation to achieve high quality and purity product while minimizing the conversion of undesired by-product. The use of process optimization in the control of batch reactors presents a useful tool for operating batch reactors efficiently and optimally. In this work, we develop an approach, based on an on-line dynamic optimization strategy, to modify optimal temperature set point profile for batch reactors. Two different optimization problems concerning batch operation: maximization of product concentration and minimization of batch time, are formulated and solved using a sequential optimization approach. An Extended Kalman Filter (EKF) is incorporated into the proposed approach in order to update current states from their delayed measurement and to estimate unmeasurable state variables. A nonlinear model-based controller: generic model control algorithm (GMC) is applied to drive the temperature of the batch reactor to follow the desired profile. A batch reactor with complex exothermic reaction scheme is used to demonstrate the effectiveness of the proposed approach. The simulation results indicate that with the proposed strategy, large improvement in batch reactor performance, in term of the amount of a desired product and batch operation time, can be achieved compared to the method where the optimal temperature set point is pre-determined.

The mechanical and physical properties of polymers are determined primarily by the underlying nano-scale structures and characteristics such as entanglements, crystallites, and molecular orientation. These structures evolve in complex manners during the processing of polymers into useful articles. Limitations of available and foreseeable computational capabilities prevent the direct determination of macroscopic properties directly from atomistic computations. As a result, computational tools and methods to bridge the length and time scale gaps between atomistic and continuum models are required. In this research, an internal state variable continuum model has been developed whose internal state variables (ISVs) and evolution equations are related to the nano-scale structures. Specifically, the ISVs represent entanglement number density, crystal number density, percent crystallinity, and crystalline and amorphous orientation distributions. Atomistic models and methods have been developed to investigate these structures, particularly the evolution of entanglements during thermo-mechanical deformations. A new method has been created to generate atomistic initial conformations of the polymer systems to be studied. The use of the hyperdynamics method to accelerate molecular dynamics simulations was found to not be able to investigate processes orders of magnitude slower that are typically measurable with traditional molecular dynamics simulations of polymer systems. Molecular dynamics simulations were performed on these polymer systems to determine the evolution of entanglements during uniaxial deformation at various strain rates, temperatures, and molecular weights. Two methods were evaluated. In the first method, the forces between bonded atoms along the backbone are used to qualitatively determine entanglement density. The second method utilizes rubber elasticity theory to quantitatively determine entanglement evolution. The results of the second method are used to gain a clearer understanding of the mechanisms involved to enhance the physical basis of the evolution equations in the continuum model and to derive the models material parameters. The end result is a continuum model that incorporates the atomistic structure and behavior of the polymer and accurately represents experimental evidence of mechanical behavior and the evolution of crystallinity and orientation.

This study aims to investigate the mechanical and fatigue behavior of additively manufactured metallic materials. Several challenges associated with different metal additive manufacturing (AM) techniques (i.e. laser-powder bed fusion and direct laser deposition) have been addressed experimentally and numerically. Experiments have been carried out to study the effects of process inter-layer time interval – i.e. either building the samples one-at-a-time or multi-at-a-time (in-parallel) – on the microstructural features and mechanical properties of 316L stainless steel samples, fabricated via a direct laser deposition (DLD). Next, the effect of building orientation – i.e. the orientation in which AM parts are built – on microstructure, tensile, and fatigue behaviors of 17-4 PH stainless steel, fabricated via a laser-powder bed fusion (L-PBF) method was investigated. Afterwards, the effect of surface finishing – here, as-built versus machined – on uniaxial fatigue behavior and failure mechanisms of Inconel 718 fabricated via a laser-powder bed fusion technique was sought. The numerical studies, as part of this dissertation, aimed to model the mechanical behavior of AM materials, under monotonic and cyclic loading, based on the observations and findings from the experiments. Despite significant research efforts for optimizing process parameters, achieving a homogenous, defectree AM product – immediately after fabrication – has not yet been fully demonstrated. Thus, one solution for ensuring the adoption of AM materials for application should center on predicting the variations in mechanical behavior of AM parts based on their resultant microstructure. In this regard, an internal state variable (ISV) plasticity-damage model was employed to quantify the damage evolution in DLD 316L SS, under tensile loading, using the microstructural features associated with the manufacturing process. Finally, fatigue behavior of AM parts has been modeled based on the crack-growth concept. Using the FASTRAN code, the fatigue-life of L-PBF Inconel 718 was accurately calculated using the size and shape of process-induced voids in the material. In addition, the maximum valley depth of the surface profile was found to be an appropriate representative of the initial surface flaw for fatigue-life prediction of AM materials in an as-built surface condition.

any experiments demonstrate that isotropic ductile materials used in engineering applications develop anisotropic damage and shows significant variation in elongation to failure. This anisotropic damage is manifest by material microstructural heterogeneities and morphological changes during deformation. The variation in elongation to the failure could be attributed to the uncertainties in the material microstructure and loading conditions. To study this deformation induced anisotropy arising from the initial material heterogeneities, we first performed uncertainty analysis using current form on an internal state variable plasticity and isotropic damage model (Bammann, 1984; Horstemeyer, 2001) to quantify the effect due to variations in material microstructure and loading conditions on elongation to failure. We extend the current isotropic damage form of theory into an anisotropic damage form for ductile material in which material heterogeneities are introduced based on damage distribution functions converted into a damage tensor of second rank. The outcome of this research is a physically motivated, uncertainty-based, anisotropic damage constitutive model that links microstructural features to mechanical properties. This was accomplished by pursuing three sub goals: (1) develop and quantify uncertainty related to material heterogeneities, (2) develop a methodology related to a higher order tensorial rank of damage for void nucleation and void growth, and (3) integrate thermodynamically constrained damage with a rate dependent plasticity constitutive material model. Later, we also proposed a new ISV theory that physically and strongly couples deformation due to damage-related internal defects to metal plasticity.

This work is concerned with posibility of measuring a wind flow by Von Karman vortex sheed structure. The bluff body is situated in the way of air flow propagation and consequentally vortexes will be appeared. Important part of speedmeter design is measurment chamber must allow to vortex sheed propagation. The transient and the reciever are situated vertically to propagation of flow.The Ultrasonic carrier is transmitted and modulated by freqency of vortex sheeding in measurment chamber.Demodulator uses PLL to “focusing“ detection of the ultrasonic beam. This can be indicated like lock and unlock phase loop. From known value of sheed frequency can be directly calculated speed of flow.

Regression testing of an evolving software system may involve significant challenges. While, there would be a requirement of maximising the probability of finding out if the latest changes to the system has broken some existing feature, it needs to be done as economically as possible. A particularly important class of software systems are API libraries. Such libraries would typically constitute a very important component of many software systems. High quality requirements make it imperative to continually optimise the internal implementation of such libraries without affecting the external interface. Therefore, it is preferred to guide the regression testing by some kind of formal specification of the library. The testing problem comprises of three parts: computation of test data, execution of test, and analysis of test results. Current research mostly focuses on the first part. The objective of test data computation is to maximise the probability of uncovering bugs, and to do it with as few test cases as possible. The problem of test data computation for regression testing is to select a subset of the original test suite running which would suffice to test for bugs probably inserted in the modifications done after the last round of testing. A variant of this problem is that of regression testing of API libraries. The regression testing of an API is usually done by making function calls in such a way that the sequence of function calls thus made suffices a test specification. The test specification in turn embodies some concept of completeness. In this thesis, we focus on the problem of test sequence computation for the regression testing of API libraries. At the heart of this method lies the creation of a state space model of the API library by reverse engineering it by executing the system, with guidance from an formal API specification. Once the state space graph is obtained, it is used to compute test sequences for satisfying some test specification. We analyse the theoretical complexity of the problem of test sequence computation and provide various heuristic algorithms for the same. State space explosion is a classical problem encountered whenever there is an attempt of creating a finite state model of a program. Our method also faces this limitation. We explore a simple and intuitive method of ameliorating this problem – by simply reducing the size of the state vector. We develop the theoretical insights into this method. Also, we present experimental results indicating the practical effectiveness of this method. Finally, we bring all this together into the design and implementation of a tool called Modest.