A study on energy harvesting through the use of electromagnetic dampers in motion control schemes

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 29).In recent years, there is a trend in most fields toward more environmentally friendly products and processes. This trend toward sustainable living is often dubbed the "Green Revolution". Because the Green Revolution is concerned with environmentally friendly ways of energy production, and structural engineering often has the task of controlling and dissipating energy, the logical step would be to unite the two concepts. This study investigates the use of the electromagnetic damper as an energy harvesting device in multiple damping schemes. It is shown that the use of the electromagnetic damper in a tuned mass damper scheme produces the most available energy to be harvested.by Geoffrey Bomarito.M.Eng

Formulation of Plasticity Models through Symbolic Regression

No abstract availabl

The Effect of Microstructural Heterogeneity on Ductile Failure

No abstract availabl

Scalable Implementation of Finite Elements by NASA _ Implicit (ScIFEi)

Scalable Implementation of Finite Elements by NASA (ScIFEN) is a parallel finite element analysis code written in C++. ScIFEN is designed to provide scalable solutions to computational mechanics problems. It supports a variety of finite element types, nonlinear material models, and boundary conditions. This report provides an overview of ScIFEi (\Sci-Fi"), the implicit solid mechanics driver within ScIFEN. A description of ScIFEi's capabilities is provided, including an overview of the tools and features that accompany the software as well as a description of the input and output le formats. Results from several problems are included, demonstrating the efficiency and scalability of ScIFEi by comparing to finite element analysis using a commercial code

A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model

A Computational Investigation Of Ductile Failure In Al5083-H116 And The Shear Strengths Of Pure Aluminum Grain Boundaries

Computational models, besides their relatively low cost, offer the benefit of complete control over all testing variables (e.g., atmospheric conditions, loading rates, applied stress states), which can be difficult to control in experiments. This control can be used to identify key controlling parameters and improve our understanding of the deformation and failure processes. This dissertation investigates how modern computational resources can be leveraged to improve both the understanding and prediction of material deformation and failure. Because of its widespread use and variability of application, aluminum and its alloys are the focus of the investigation. Specifically, the applications of ductile failure and grain boundary shear strength were chosen for this dissertation. Though these phenomena are quite different, the same theme is present in the approach to both problems. In both cases, we were able to run numerous simple simulations of the phenomenon in concern. A large computational effort was required in all cases to run the sets of simulations, but the results of these simulations were synthesized into a simple model which can be used at larger scales. In the case of ductile failure, a unit cell was designed to simulate the microstructural evolution of an aluminum alloy. The population of second phase particles in the alloy was represented as a spherical void surrounded by an alu- minum matrix. Many different loadings were applied to the cell which were characterized by a stress state and orientation. The results of these tests were used to form a simple model for the dependence of ductile failure on applied stress state. By refining the model microstructure, it was found that increasing the fidelity of the model microstructure leads to increased predictive capability of the model. In the case of grain boundary shear strength, atomistic models of interface structures were subjected to shear in many directions in the boundary plane. The simulation of a large number of these interface structures showed that shear yield strengths were relatively independent of the macroscopic parameters describing each interface. Subsequently, it was shown that a statistical approach to predicting grain boundary shear strengths could be used