Early detection of rail defects is necessary for preventing derailments and costly damage to the train and railway infrastructure. A rail surface flaw can quickly propagate from a small fracture to a broken rail after only a few train cars have passed over it. Rail defect detection is typically performed by using an instrumented car or a separate railway monitoring vehicle. Rail surface irregularities can be measured using accelerometers mounted to the bogie side frames or wheel axles. Typical signal processing algorithms for detecting defects within a vertical acceleration signal use a simple thresholding routine that considers only the amplitude of the signal. As a result, rail surface defects that produce low amplitude acceleration signatures may not be detected, and special track components that produce high amplitude acceleration signatures may be flagged as defects. The focus of this research is to develop an intelligent signal processing algorithm capable of detecting and classifying various rail surface irregularities, including defects and special track components. Three algorithms are proposed and validated using data collected from an instrumented freight car. For the first two algorithms, one uses a windowed Fourier Transform while the other uses the Wavelet Transform for feature extraction. Both of these algorithms use an artificial neural network for feature classification. The third algorithm uses the Wavelet Transform to perform a regularity analysis on the signal. The algorithms are validated with the collected data and shown to out-perform the threshold-based algorithm for the same data set. Proper training of the defect detection algorithm requires a large data set consisting of operating conditions and physical parameters. To generate this training data, a dynamic wheel-rail interaction model was developed that relates defect geometry to the side frame vertical acceleration signature. The model was generated by using combined systems dynamic modeling, and the system was solved with a developed combined lumped and distributed parameter system numerical approximation. The broken rail model was validated with real data collected from an instrumented freight car. The model was then used to train and validate the defect detection methodologies for various train and rail physical parameters and operating conditions. / Ph. D.

Autophagy is a conserved biological stress response in mammalian cells that is responsible for clearing damaged proteins and organelles from the cytoplasm and recycling their contents via the lysosomal pathway. In cases where the stress is not too severe, autophagy acts as a survival mechanism. In cases of severe stress, it may lead to programmed cell death. Autophagy is abnormally regulated in a wide-range of diseases, including cancer. To integrate the existing knowledge about this decision process into a rigorous, analytical framework, we built a mathematical model of cell fate decision mediated by autophagy. The model treats autophagy as a gradual response to stress that delays the initiation of apoptosis to give the cell an opportunity to survive. We show that our dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells we have provided a systems biology experimental framework to study dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative uorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells. / Ph. D.

The theory of Lagrangian Coherent Structures (LCS) enables prediction of material transport by turbulent winds, such as those observed in the Earth's Atmospheric Boundary Layer. In this dissertation, both theory and experimental methods are developed for utilizing small fixed-wing unmanned aircraft systems (UAS) in detecting these atmospheric coherent structures. The dissertation begins by presenting relevant literature on both LCS and airborne wind estimation. Because model-based wind estimation inherently depends on high quality models, a Flight Dynamic Model (FDM) suitable for a small fixed-wing aircraft in turbulent wind is derived in detail. In this presentation, some new theoretical concepts are introduced concerning the proper treatment of spatial wind gradients, and a critical review of existing theories is presented. To enable model-based wind estimation experiments, an experimental approach is detailed for identifying a FDM for a small UAS by combining existing computational aerodynamic and data-driven approaches. Additionally, a methodology for determining wind estimation error directly resulting from dynamic modeling choices is presented and demonstrated. Next, some model-based wind estimation results are presented utilizing the experimentally identified FDM, accompanied by a discussion of model fidelity concerns and other experimental issues. Finally, an algorithm for detecting LCS from a single circling fixed-wing UAS is developed and demonstrated in an Observing System Simulation Experiment. The dissertation concludes by summarizing these contributions and recommending future paths for continuing research. / Doctor of Philosophy / In a natural or man-made disaster, first responders depend on accurate predictions of where the wind might carry hazardous material. A mathematical theory of Lagrangian Coherent Structures (LCS) has shown promise in ocean environments to improve these predictions, and the theory is also applicable to atmospheric flows near the Earth’s surface. This dissertation presents both theoretical and experimental research efforts towards employing small fixed-wing unmanned aircraft systems (UAS) to detect coherent structures in the Atmospheric Boundary Layer (ABL). These UAS fit several “gaps” in available sensing technology: a small aircraft responds significantly to wind gusts, can be steered to regions of interest, and can be flown in dangerous environments without risking the pilot’s safety. A key focus of this dissertation is to improve the quality of airborne wind measurements provided by inexpensive UAS, specifically by leveraging mathematical models of the aircraft. The dissertation opens by presenting the motivation for this research and existing literature on the topics. Next, a detailed derivation of a suitable Flight Dynamic Model (FDM) for a fixed-wing aircraft in a turbulent wind field is presented. Special attention is paid to the theories for including aerodynamic effects of flying in non-uniform winds. In preparation for wind measurement experiments, a practical method for obtaining better quality FDMs is presented which combines theoretically based and data-driven approaches. A study into the wind-measurement error incurred solely by mathematical modeling is presented, focusing on simplified forms of the FDM which are common in aerospace engineering. Wind estimates which utilize our best available model are presented, accompanied by discussions of the model accuracy and additional wind measurement concerns. A method is developed to detect coherent structures from a circling UAS which is providing wind information, presumably via accurate model based estimation. The dissertation concludes by discussing these conclusions and directions for future research which have been identified during these pursuits.

The focus of this study is on the development of a state-of-the-art single-wheel roller rig for studying contact mechanics and dynamics in railroad applications. The use of indoor-based simulation tools has become a mainstay in vehicle testing for the automotive and railroad industries. In contrast to field-testing, roller rigs offer a controlled laboratory environment that can provide a successful path for obtaining data on the mechanics and dynamics of railway systems for a variety of operating conditions. The idea to develop a laboratory test rig started from the observation that there is a need for better-developed testing fixtures capable of accurately explaining the complex physics of wheel-rail contact toward designing faster, safer, and more efficient railway systems. A review of current roller rigs indicated that many desired functional requirements for studying contact mechanics currently are not available. Thus, the Virginia Tech Railway Technologies Laboratory (RTL) has embarked on a mission to develop a state-of-the-art testing facility that will allow experimental testing of contact mechanics in a dynamic, controlled, and consistent manner. VT roller rig will allow for closely replicating the boundary conditions of railroad wheel-rail contact via actively controlling all the wheel-rail interface degrees of freedom: cant angle, angle of attack, and lateral displacement. Two sophisticated independent drivelines are configured to precisely control the rotational speed of the wheels, and therefore their relative slip or creepage. A novel force measurement system, suitable for steel on steel contact, is configured to precisely measure the contact forces and moments at the contact patch. The control architecture is developed based on the SynqNet data acquisition system offered by Kollmorgen, the motors supplier. SynqNet provides a unified communication protocol between actuators, drives, and data acquisition system, hence eliminating data conversion among them. Various design analysis indicates that the rig successfully meets the set requirements: additional accuracy in measurements, and better control on the design of experiments. The test results show that the rig is capable of conducting various contact mechanics studies aimed for advancing the existing art. Beyond developing the experimental testing fixture for studying contact mechanics, this study provides a comprehensive review of the contact models. It discusses the simplifying assumptions for developing the models, compares the models functionality, and highlights the open areas that require further experimental and theoretical research. In addition, a multi-body dynamic model of the entire rig, using software package SIMPACK, is developed for conducting modal analysis of the rig and evaluating the performance of the rig's components. A MATLAB routine is also developed that provides a benchmark for developing creep curves from measurements of the rig and comparing them with existing creep curves. / Ph. D.

Exoskeletons and humanoid robots require high-power, low-weight, and back-driveable actuators. This paper describes the design and analysis of a high-force linear series elastic actuator for a lower body exoskeleton. The actuator is powered by two motors and utilize a liquid cooling system to increase its maximum continuous torque. The actuator is capable of outputting a maximum continuous force of 4800N and a maximum speed of 0.267 m/s with a maximum continuous motor current of 18A. The Titanium leaf spring was used in the actuator to provide compliance. The spring has a median stiffness of 587 N/mm with standard deviation of 38 N/mm, validated by experiments. Dynamic model was created to analyze the normal modes and can be used for developing model-based controllers. / Master of Science / Compliant Linear actuators with ball screw have become popular for humanoids robots and exoskeleton. The use of ball screw lead to high efficiency, high gear ratio and high back-drivability. The compliance or the ''softness'' of the actuator comes from Titanium leaf spring, which can storage energy during gait cycle and protect motor and the ball screw from impacts of walking. The custom liquid cooling system improves the force density for the actuator. Beam theory analysis, heat transfer analysis, and dynamics analysis were performed to provides insights for the actuator system.

Abstract In Paper [I], I develop the FOR-DICE model to analyze optimal global forest carbon management. The FOR-DICE is a simple framework for assessing the role of the boreal, tropical, and temperate forests as both a source of renewable energy and a resource to sequester and store carbon. I find that forests play an important role in reducing global emissions, especially under ambitious climate targets. At the global level, efforts should focus on increasing the stock of forest biomass rather than increasing the use of the forest for bioenergy production. The results also highlight the important role of reducing tropical deforestation to reduce climate change. In Paper [II], I develop the FRICE to investigate the role of two key efforts to increase the stock of forest biomass, namely, afforestation and avoided deforestation. FRICE is a multi-regional integrated assessment model that captures the dynamics of forest carbon sequestration in a transparent way and allows me to investigate the allocation of these actions across space and time. I find that global climate policy can benefit considerably from afforestation and avoided deforestation in tropical regions, and in particular in Africa. Avoided deforestation is particularly effective in the short run while afforestation provides the largest emissions reductions in the medium run. This paper also highlights the importance of not solely relying on avoided deforestation as its capacity to reduce emissions is more limited than afforestation, especially under more stringent temperature targets. In Paper [III], we investigate how uncertainties linked to the forest affect the optimal climate policy. We incorporate parameter uncertainty on the intrinsic growth rate and climate effects on the forest by using the state-contingent approach. Our results show that forest uncertainty matters. We find that the importance of including forest in climate policy increases when the forest is subject to uncertainty. This occurs because optimal forest response allows us to reduce the costs associated with uncertainty. In Paper [IV], we explore the implications of asymmetries in climate policy arising from not recognizing forest carbon emissions and sequestration in the decision-making process. We show that not fully including carbon values associated with the forest will have large effects on different forest controls and lead to an increase in emissions, higher carbon prices, and lower welfare.  We further find, by investigating the relative importance of forest emissions compared to sequestration, that recognizing forest emissions from bioenergy and deforestation is especially important for climate policy.

This article describes the development of a component-based technology robot system that can be rapidly configured to perform a specific manufacturing task. The system is conceived with standard and inter-operable components including actuator modules, rigid link connectors and tools that can be assembled into robots with arbitrary geometry and degrees of freedom. The reconfigurable "plug-and-play" robot kinematic and dynamic modeling algorithms are developed. These algorithms are the basis for the control and simulation of reconfigurable robots. The concept of robot configuration optimization is introduced for the effective use of the rapidly reconfigurable robots. Control and communications of the workcell components are facilitated by a workcell-wide TCP/IP network and device level CAN-bus networks. An object-oriented simulation and visualization software for the reconfigurable robot is developed based on Windows NT. Prototypes of the robot systems configured to perform 3D contour following task and the positioning task are constructed and demonstrated. Applications of such systems for biomedical tissue scaffold fabrication are considered. / Singapore-MIT Alliance (SMA)

The thermal control of a die is crucial for the development of high efficiency injection moulds. For successful thermal management, this research provides an effective control strategy to find sensor locations, identify thermal dynamic models, and design controllers. By applying a clustering method and sensitivity analysis, sensor locations are identified. The neural network and finite element analysis techniques enable the modeling to deal with various cycle-times for the moulding process and uncertain dynamics of a die. A combination of off-line training through finite element analysis and training using on-line learning algorithms and experimental data is used for the system identification. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-adaptive PID methods with backpropagation (BP) and radial basis function (RBF) neural networks to tune control parameters. Direct adaptive inverse control and additive feedforward control by adding direct adaptive inverse control to self-adaptive PID controllers are also provided. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times. Additionally, the improved cooling effectiveness of the conformal cooling channel designed in this study is presented by comparing with a conventional straight channel.

There is a need for smaller and more economic transportation systems. Two-wheeled inverted-pendulum machines, such as the Segway, have been proposed to address this need. However, the Segway places the operator on top of a naturally unstable platform that is stabilized by means of a control system. The control stability of the Segway can be severely affected when minor disturbances or unanticipated conditions arise. In this thesis, a dynamic model of a Segway is developed and used in simulations of various conditions that can arise during normal use. The dynamic model of a general two-wheeled inverted pendulum and human rider is presented. Initial estimates of the parameters were calculated or obtained from other references. The results from numerous experiments are presented and used to develop a better understanding of the dynamics of the vehicle. The experimental data was then used to adjust the model parameters to match the dynamics of a real Segway Human Transporter. Finally, the model was used to simulate various failure conditions. The simulations provide a better understanding of how these conditions arise, and help identify which parameters play an important role in their outcome.

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