In this work the effect of elasticity on turbulent drag reduction due to polymers is investigated using a hybrid Direct Numerical Simulation (D.N.S) and Langevin dynamics approach. Simulations are run at a friction Reynolds number of Re_&tau = 560 for 960.000 dumbbells with Deborah numbers of De = 0, De = 1, and De = 10. The conclusions are that it is possible to simulate a drag reduced flow using hybrid D.N.S. with Langevin dynamics, that polymers, like other occurrences of drag reduction, reduce drag through streak stabilization, and that the essential property of polymers and fibers in having a drag reducing effect is their ability to exert a torqueon the solvent when they orientate in the boundary layer of the turbulent flow.

Two-thirds of repetitive strain injuries affect the wrist joint. Although force is believed to be one of the major factors, the forces involved in wrist movements have not been thoroughly characterized in vivo. Computer simulations with a musculoskeletal model of the wrist have been used to estimate wrist muscle forces, but only at maximum voluntary contraction and only involving a single degree of freedom (DOF). In this study we present a method for creating a subject-specific model that can be used to estimate muscle forces and joint torques in both degrees of freedom of the wrist over a range of torques applicable to activities of daily living. Ten young, healthy subjects applied three levels of isometric wrist torque (about 7, 15, and 25% of maximum torque) in combinations of wrist flexion-extension and radial-ulnar deviation while joint torque in both DOF and surface electromyograms (sEMG) in the five major wrist muscles were measured. To find subject-specific parameters, we followed a two-step process. First, a pre-existing, generic musculoskeletal model of the wrist was scaled to individual subjects' height. Second, we compared joint torques predicted from measured sEMG using forward simulations of muscular dynamics to measured torques and minimized this error to optimize for subject-specific model parameter values. The model parameters optimized were the maximum isometric force and tendon slack length of each muscle. Optimization constraints were added to ensure physiologically plausible combinations of parameter values. The optimization produced model parameters that 1) were in a reasonable physiological range for each test subject and 2) significantly improved the accuracy of the model’s torque estimation. Scaling the generic model reduced the root mean squared (RMS) error between predicted and measured joint torques by 2.8±4.6% (mean±SD), whereas optimizing the scaled model further reduced the RMS error by 51.4±18.9% for the torque level at which the model was optimized. Testing the optimized model at other torque levels still significantly reduced the error between predicted and measured torques compared to the scaled model (43.7±28.0% and 25.0±24.0% for lower and higher torque levels, respectively). The mean error between predicted and measured torque was 0.23±0.04, 0.30±0.04, and 1.17±0.26 Nm at the low-, mid-, and high-torque levels, respectively. The method generally reduced the error in flexion-extension (FE) more than radial-ulnar deviation (RUD), likely in part because sEMG and torque were larger in FE than in RUD. Optimizing for subject-specific model parameters significantly improved prediction over both the generic and scaled models, in both degrees of freedom of the wrist, and at all three torque levels. The presented method for creating subject-specific models can be used in future studies to quantify muscle forces and joint torques of natural wrist movements in vivo.

Switched reluctance machines (SRMs) are gaining increasing interest in industrial applications due to their low manufacturing cost, simple and robust structure, excellent fault-tolerant capability, and reliable operation in high-temperature operating environments. However, the inherent pulsative torque and radial force lead to the well-known torque ripples and acoustic noise issues. Although there are numerous advanced methods to address the above two issues, the high nonlinearity inevitably brings difficulties in controlling the SRMs. Since the linear SRM voltage and toque equations are simple, it would be beneficial to explore the control algorithm by using such simple linear model. The application of the linear torque model is firstly explored. To utilize such simple model, the connections between the linear toque model and the nonlinear torque model are built through the mapping. The features of these mapping curves are studied in detail. Applying the linear torque equation to generate the reference currents in the optimization-based torque sharing function method shows a significant reduction of the time consumption in solving the bi-optimization problem. Later, the complete equivalent linear SRM model is constructed by introducing the linear voltage equation and corresponding mapping. Since the linear model is easy to predict the behaviour of SRMs, it is beneficial to apply such model in the model predictive torque control (MPTC) methods. The application of the equivalent linear model in the finite control set (FCS) MPTC method shows a low computational burden and occupies less storage space. Besides, the improved switching table in the proposed FCS MPTC method also enhances the torque control performance in high-speed operation conditions. To further reduce the torque ripples, the continuous control set (CCS) MPTC method is developed based on the constructed equivalent linear SRM model. The impossibility in analytically solving the optimization problem in the CCS MPTC method if using the original nonlinear SRM model is innovatively addressed by using the equivalent linear SRM model and properly modifying the cost function. Extensive simulation and experimental results prove the low-ripple feature of the proposed CCS MPTC method in a wide speed range. The high nonlinearity also makes the current control of SRM drives difficult. An intersection-method-based current controller is presented to ensure good current tracking performance for SRMs. The employed adaptive flux-linkage observer makes this current controller show robust performance when there is a deviation on the employed flux-linkage characteristics. Finally, the key but unmeasurable radial force information for the advanced acoustic reduction method is reconstructed based on the measured flux-linkage curves and some core relationship. This core relationship, which is between the square root of the radial force and the flux-linkage, is explored in detail. Simulation results reveal that the proposed method shows good radial force estimation accuracy when there is even 50% airgap length variation. / Thesis / Doctor of Philosophy (PhD)

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The relationship between sEMG signals and muscle force, and associated joint torque, is an object of study for clinical applications such as rehabilitation robotics and commercial applications as wearable motion control devices. The information type and quality obtained by sEMG can impact the classification and prediction accuracy of ankle joint torque. In this thesis project, HD-sEMG based data was collected together with ankle joint torque measurements from 5 subjects during MVIC of plantarflexors and dorsiflexors. Machine learning approaches ideally suited for nonlinear regression tasks, such as MLP and LSTM, have been implemented and evaluated to best predict joint torque profiles given extracted features from sEMG data. An evaluation of machine learning performances using HD-sEMG data over bipolar sEMG data has been conducted in intra-session, inter-subjective and intra-subjective study cases.