The first part of this thesis broadly describes physics beyond jamming in granular materials. As granular flows pass through a narrow constriction they have a propensity to clog, like salt in a salt shaker. This process has been shown to be Poissonian, with the flow randomly sampling configurational microstates local to the outlet. We analyze 25,000 flow events from an automated, 2D hopper filled with tridisperse discs to identify structural features which correlate with the onset of clogging. We utilize a linear Support Vector Machine (SVM) and a Convolutional Neural Network (CNN) to classify states as either flowing or clogging. Using the SVM, we achieve a 58% accuracy on this task, increasing to 70% when identifying flowing states from the incipient arch formation, and 95% separating flowing states from empty stable arches. The non-linear CNN achieves a marginally higher accuracy, 61% in the clogging task, but still fails to predict the onset of clogging in an individual flow. However, the interpretable nature of the SVM decision boundary identifies the cornerstones as an important feature even in the lowest accuracy machine. We verify this with experimental evidence indicating the cornerstones functionally control the width of the outlet, and thus the probable range of arches. We also investigate the dynamics of a rotating impactor dropped into a static granular bed. Previous work has shown the forces affecting translation are primarily normal to the body of the impactor, but acknowledges the tangential forces must be present. Using a 5000 fps camera, we track the translational and rotational motion of a spherical impactor into a granular bed. We observe the overall penetration depth of the impactor is enhanced by rotation, while the translational stopping time is extended. We also found the rotational acceleration saturates to a constant, depth-dependent value after translation has stopped, suggesting the rotational dynamics of the impactor are dominated by the quasistatic interaction with the granular bulk. The second part of this thesis focuses on the modeling of strings under twist, and the exploitation of this geometry for hand powered high-frequency oscillation. First we test the standard model for the length contraction of a bundle of strings under twist, and find systematic deviation with opposing effects at medium and large twist angles. By including volume conservation, we achieve better fits to data for single-, double-, and triple-stranded bundles of Nylon monofilament as an ideal test case. This gives a well-defined procedure for extracting an effective twist radius that characterizes contraction behavior. While our approach accounts for the observed faster-than-expected contraction up to medium twist angles, we also find that the contraction is nevertheless slower than expected at large twist angles for both Nylon monofilament bundles and several other string types. The size of this effect varies with the individual-string braid structure and with the number of strings in the bundle. We speculate that it may be related to elastic deformation within the material. However, our first modeling attempt does not fully capture the observed behavior. A use case for the dynamics of twisted strings is powering a `buzzer'. Previous work characterized a novel geometry where the buzzer is turned vertical and a hanging mass is applied to one end to transfer some energy between periods. We characterize the time dependent forcing required to drive sinusoidal motion on this system: the vertical, taut-line buzzer. A damped taut-line system is constructed and its oscillatory properties measured. The predicted force profile is implemented by hand, sinusoidal motion is observed and the energy required per cycle to maintain steady state oscillations is found in good agreement with theory. An additional force profile to maximize oscillations and minimize operator effort is characterized, achieving a peak angular velocity of 11,000 RPM. These high velocity oscillations are compared to alternate hand-powered centrifuge systems for efficiency of energy input and predicted for use in comparatively high volume centrifugation tasks.