Wrist and forearm motion is governed both by its dynamics and the control strategies employed by the neuromuscular system to execute goal oriented movement. Two experiments were conducted to increase our understanding of wrist and forearm motion. The first experiment involved 10 healthy subjects executing planned movements to targets involving all three degrees of freedom (DOF) of the wrist and forearm, namely wrist flexion-extension (FE), wrist radial-ulnar deviation, and forearm pronation-supination (PS). A model of wrist and forearm dynamics was developed, and the recorded movements were fed into the model to analyze the movement torques. This resulted in the following key findings: 1) The main impedance torques affecting wrist and forearm movements are stiffness and gravity, with damping and inertial effects contributing roughly 10% of the total torque. 2) There is significant coupling between all degrees of freedom (DOF) of the wrist and forearm, with stiffness effects being the most coupled and inertial effects being the least coupled. 3) Neglecting these interaction torques results in significant error in the prediction of the torque required for wrist and forearm movements, suggesting that the neuromuscular system must account for coupling in movement planning. A second experiment was conducted in which 10 different healthy subjects pointed to targets arranged on a plane in front of the subjects. This pointing task required two DOF, but subjects were allowed to use all three DOF of the wrist and forearm. While subjects could have completed the task with FE and RUD alone, it was found that subjects recruited PS as well. Hypotheses regarding why subjects would recruit PS even though it was not necessary included the minimization of a number of cost functions (work, effort, potential energy, path length) as well as mechanical interaction between the DOF of the wrist and forearm. It was found that the pattern of PS recruitment predicted from the mechanical interaction hypothesis most closely resembled the observed pattern. According to this hypothesis, the neuromuscular system uses a simplified 2 DOF model of the joints most critical to the task (FE and RUD) to plan the task, while leaving the third DOF (PS) uncontrolled. The resulting interaction torques create the observed pattern of PS movement.

A six degree-of-freedom model for a tailless supersonic aircraft (TSA) concept was developed using MATLAB and Simulink. Aerodynamic data was provided through the computational fluid dynamics analysis of Techsburg, Inc. A three degree-of-freedom model of the configuration's longitudinal dynamics was completed first. Elevator control power was derived from the dynamic response requirements for pitch chosen by Techsburg. The propulsion model utilized General Electric F-414-400-like turbofan engines because an engine deck was readily available. Work on the six degree-of-freedom dynamic model began with determining the necessary rolling and yawing moment coefficients necessary to meet the rest of the chosen dynamic response requirements. These coefficients were then used to find the corresponding all-moving tip deflections. The CFD data showed that even at small all-moving tip deflections the rolling moment coefficient produced was much greater than the amount of yawing moment coefficient produced. This result showed that an additional roll effector was needed to counteract excess rolling moment at any given all-moving tip deflection and trim the aircraft. An angle of attack and pitch rate feedback controller was used to improve the longitudinal dynamics of the aircraft. Because this configuration lacked a vertical tail, a lateral-directional stability augmentation system was vital to its success. The lateral-directional dynamics were improved to Level 1 flying qualities through use of a modified roll/yaw damper. The modified controller fed yaw rate back to both the all-moving tips and roll effector. The six degree-of-freedom model was augmented with actuator dynamics for the elevator, roll effector, and all-moving tips. The actuators were modeled as first order lags. The all-moving tip actuator time constant was varied to determine the effect of actuator bandwidth on the lateral-directional flying qualities. After the actuator dynamics were successfully implemented, the six degree-of-freedom model was trimmed for both standard cruise and engine-out situations. The eccentuator concept from the DARPA Smart Wing program was selected as a possible conceptual design for the all-moving tip actuation system. The success of the TSA six degree-of-freedom dynamic model proved that morphing all-moving tips were capable of serving as effective control surfaces for a supersonic tailless aircraft. / Master of Science

<p> In this paper, empirical tests of a dynamic urban growth model are discussed. It is assumed that population change in any urban region is a function of the population size of the urban centres in the system and of the distances between them. A set of linear equations is simultaneously estimated by a least squares procedure. The parameters of the model; the equilibrium population of each urban region, the rate of natural increase, and the propensity to migrate between urban regions, are calculated from the regression coefficients. By estimating a series of equations at different times, a set of parameter estimates are obtained. The parameter estimates fluctuate erratically. Recommendations for further research include the redefinition of the model, and of the urban system. </p> / Thesis / Master of Arts (MA)

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The design of the freight train truck has gone relatively unchanged over the past 150 years. There has been relatively little change to the fundamental railway truck design because of the challenges of implementing a cost effective and reliable modification to designs that have proven effective in decades of operation. A common U. S. railway truck consists of two sideframes, a bolster, two spring nests, and four friction wedges. The two sideframes sit on the axels. The bolster rides on springs on top of the sideframes. The friction wedges also ride on springs on top of the sideframe, and are positioned between the bolster and sideframe, acting as a damping mechanism. Better understanding the dynamic behavior and forces on the bodies are critical in reducing unnecessary wear on the components, along with potential negative behavior such as loss of productivity and increase in operating costs. This thesis will investigate the dynamic behavior of the truck under warping conditions using a stand-alone model created in Virtual.Lab. This research covers two main areas. First, the full-truck model will be developed and its simulation results will be compared to test data from the Transportation Technology Center, Inc. (TTCI). Data was provided from warp testing performed at the TTCI facilities in the spring of 2008. Once validated, the model will be used to gain a better understanding of the forces and moments that are propagated through the system, and of the dynamics of all bodies. Due to costs and physical constraints, not every bogie component can be instrumented during test, so the computer model will be able to provide valuable information not easily obtained otherwise. Second, full-truck models using different contact geometry between the wedges, sideframes, and bolster will be compared. A model with extremely worn sideframes will allow for investigation into the effects of wear on the damping abilities and warp stiffness of the truck. Another model using split wedges will be compared with the previous model to investigate into the behavior differences in the truck using different types of wedges. By understanding the impact of different geometries on the overall performance of the truck, better decisions on design and maintenance can be made in the future. After creating the models, we found that the full-truck model created in LMS® Virtual.Lab compared well with the test data collected by TTCI. In the comparison with NUCARS® we determined that the stand-alone model, which incorporates the wedges as bodies, captures the warp dynamics of the truck better than NUCARS®, which models the wedges as connections. By creating a model with severely worn sideframes, we were able to determine that the truck loses its abilities to damp bounce in the system as well as to prevent warping when the components become sufficiently worn. The split-wedge model behaved similarly to the standard full-truck model for bounce inputs, but had a significantly different behavior in warp. Further development will be needed on the split-wedge model to be confident that it behaved as expected. / Master of Science

Yes / Multistage Flash Desalination (MSF) is currently facing an enormous challenge in cutting of the cost: within the last few years, the MSF experienced a gradual decline in investment compared to other techniques of desalting water and thus, a significant improvement is required to remain attractive for capital investors. Improved process control is a cost effective approach to energy conservation and increased process profitability. In this work, a dynamic model is presented using gPROMS model builder to optimize and control MSF process. The Proportional Integral Derivative Controller (PID) and Generic Model Control (GMC) are used successfully to control the Top Brine Temperature (TBT) and the Brine Level (BL) in the last stage at different times of the year. The objectives of this study are: firstly, to obtain optimum TBT and BL profiles for four different seasons throughout the year by minimizing the Total Seasonal Operating Cost (TSOC); secondly, to track the optimum TBT and BL profiles using PID and GMC controllers with and without the presence of constraints; thirdly, to examine how both types of controllers handle the disturbances which occur in the plant. The results are promising and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system.

The majority of automatic speech recognition (ASR) systems rely on hidden Markov models (HMM), in which the output distribution associated with each state is modelled by a mixture of diagonal covariance Gaussians. Dynamic information is typically included by appending time-derivatives to feature vectors. This approach, whilst successful, makes the false assumption of framewise independence of the augmented feature vectors and ignores the spatial correlations in the parametrised speech signal. This dissertation seeks to address these shortcomings by exploring acoustic modelling for ASR with an application of a form of state-space model, the linear dynamic model (LDM). Rather than modelling individual frames of data, LDMs characterize entire segments of speech. An auto-regressive state evolution through a continuous space gives a Markovian model of the underlying dynamics, and spatial correlations between feature dimensions are absorbed into the structure of the observation process. LDMs have been applied to speech recognition before, however a smoothed Gauss-Markov form was used which ignored the potential for subspace modelling. The continuous dynamical state means that information is passed along the length of each segment. Furthermore, if the state is allowed to be continuous across segment boundaries, long range dependencies are built into the system and the assumption of independence of successive segments is loosened. The state provides an explicit model of temporal correlation which sets this approach apart from frame-based and some segment-based models where the ordering of the data is unimportant. The benefits of such a model are examined both within and between segments. LDMs are well suited to modelling smoothly varying, continuous, yet noisy trajectories such as found in measured articulatory data. Using speaker-dependent data from the MOCHA corpus, the performance of systems which model acoustic, articulatory, and combined acoustic-articulatory features are compared. As well as measured articulatory parameters, experiments use the output of neural networks trained to perform an articulatory inversion mapping. The speaker-independent TIMIT corpus provides the basis for larger scale acoustic-only experiments. Classification tasks provide an ideal means to compare modelling choices without the confounding influence of recognition search errors, and are used to explore issues such as choice of state dimension, front-end acoustic parametrization and parameter initialization. Recognition for segment models is typically more computationally expensive than for frame-based models. Unlike frame-level models, it is not always possible to share likelihood calculations for observation sequences which occur within hypothesized segments that have different start and end times. Furthermore, the Viterbi criterion is not necessarily applicable at the frame level. This work introduces a novel approach to decoding for segment models in the form of a stack decoder with A* search. Such a scheme allows flexibility in the choice of acoustic and language models since the Viterbi criterion is not integral to the search, and hypothesis generation is independent of the particular language model. Furthermore, the time-asynchronous ordering of the search means that only likely paths are extended, and so a minimum number of models are evaluated. The decoder is used to give full recognition results for feature-sets derived from the MOCHA and TIMIT corpora. Conventional train/test divisions and choice of language model are used so that results can be directly compared to those in other studies. The decoder is also used to implement Viterbi training, in which model parameters are alternately updated and then used to re-align the training data.

The increased popularity of electric vehicles and e-mobility among the people, have encouraged many automotive companies and research organisations to develop good strategies for drivetrain designs involving batteries. As seen in the department of Alternative Powertrains research is carried out on hybrid fuel cell and electric vehicles. This thesis deals with the development of lithium ion battery model for electric vehicle simulations. A novel approach using black box modelling is developed for development of battery model using only the available battery measurements. Furthermore, a measurement test strategy is formulated providing the process direction and measurement parameters to be considered. Developed battery model provide voltage estimates for given Charge rate,temperature and State of Charge (SOC). The comparison of experimentally obtained and model estimated values. The model developed has a very good accuracy in estimation.

As the technology behind renewable energy sources becomes more advanced and cost-effective, these sources have become an ever-increasing portion of the generation portfolios of power systems across the country. While the shift away from non-renewable resources is generally considered beneficial, the fact remains that intermittent renewable sources present special challenges associated with their unique operating characteristics. Because of the high variability of intermittent renewables, the frequency performance of the system to which they are connected can degrade. Generators assigned to regulate frequency, keeping it close to the desired 60 Hz, are forced to ramp up and down quickly in order to offset the rise and fall of the variable resources (in addition to the rise and fall of load), causing transient frequency deviations, power swings, major interface transfer variations and other significant issues. This research measures the impact of intermittent renewable resource penetration level on power system frequency performance, and offers methods for managing that performance. Currently, the generally accepted amount of regulation (rapidly-dispatchable reserve, used as a supplement to base generation on a short time scale to avoid performance issues) is 1% of peak load. Because of the high variability associated with intermittent renewables, including wind generation (the focus of this thesis), it is expected that this amount of regulation must increase in order to maintain adequate system frequency performance. Thus, the primary objective of this thesis is to quantify the amount of regulation necessary to maintain adequate frequency performance as a function of the penetration level of wind generation. Presently, balancing resource requirements are computed, in both industry and in the research literature, using static models, which rely entirely on statistical manipulation of net load, failing to capture the intricacies of dynamic system and generator interactions. Using a dynamic model with high temporal resolution data, instead of these statistical models, this thesis confirms the need for additional regulation as wind generation penetration increases. But beyond that, our research demonstrates an exponentially increasing relationship between necessary regulation and wind generation percentage, indicating that, without further technological breakthroughs, there is a practical limit to the amount of wind generation that a typical system can accommodate. Furthermore, we compare our dynamic model results with those of the statistical models, and show that the majority of current statistical models substantially under-predict the necessary amount of regulation to accommodate significant amounts of wind generation. Finally, we verify that the ramping capability of the regulating generators impacts the amount of necessary regulation, although it is generally ignored in current analysis and related literature.