Mechanical Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2795
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Item Influence of Noise on Response Localizations in Mechanical Oscillator Arrays(2022) Cilenti, Lautaro Daniel; Balachandran, Balakumar; Cameron, Maria; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The dynamics of mechanical systems such as turbomachinery and vibration energy harvesting systems (VEH) consisting of one or multiple cantilever structures is often modeled by arrays of periodically driven coupled nonlinear oscillators. It is known that such systems may have multiple stable vibration steady states. Some of these steady states are localized vibrations that are characterized by high amplitude vibrations of a subset of the system, with the rest of the system being in a state of either low amplitude vibrations or no vibrations. On one hand, these localized vibrations can be detrimental to mechanical integrity of turbomachinery, while on the other hand, the vibrations can be potentially desirable for increasing energy yield in VEHs. Transitions into or out of localized vibrations may occur under the influence of random factors. A combination of experimental and numerical studies has been performed in this dissertation to study the associated transition times and probability of transitions in these mechanical systems. The developments reported here include the following: (i) a numerical methodology based on the Path Integral Method to quantify the probability of transitions due to noise, (ii) a numerical methodology based on the Action Plot Method to quantify the quasipotential and most probable transition paths in nonlinear systems with periodic external excitations, and (iii) experimental evidence and stochastic simulations of the influence of noise on response localizations of rotating macro-scale cantilever structures. The methodology and results discussed in this dissertation provide insights relevant to the stochastic nonlinear dynamics community, and more broadly, designers of mechanical systems to avoid potentially undesirable stochastic nonlinear behavior.Item PHYSICS-BASED AND DATA-DRIVEN MODELING OF HYBRID ROBOT MOVEMENT ON SOFT TERRAIN(2020) Wang, Guanjin; Balachandran, Balakumar; Riaz, Amir; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Navigating an unmapped environment is one of the ten biggest challenges facing the robotics community. Wheeled robots can move fast on flat surfaces but suffer from loss of traction and immobility on soft ground. However, legged machines have superior mobility over wheeled locomotion when they are in motion over flowable ground or a terrain with obstacles but can only move at relatively low speeds on flat surfaces. A question to answer is as follows: If legged and wheeled locomotion are combined, can the resulting hybrid leg-wheel locomotion enable fast movement in any terrain condition? To investigate the rich physics during dynamic interactions between a robot and a granular terrain, a physics-based computational framework based on the smoothed particle hydrodynamics (SPH) method has been developed and validated by using experimental results for single robot appendage interaction with the granular system. This framework has been extended and coupled with a multi-body simulator to model different robot configurations. Encouraging agreement is found amongst the numerical, theoretical, and experimental results, for a wide range of robot leg configurations, such as curvature and shape. Real-time navigation in a challenging terrain requires fast prediction of the dynamic response of the robot, which is useful for terrain identification and robot gait adaption. Therefore, a data-driven modeling framework has also been developed for the fast estimation of the slippage and sinkage of robots. The data-driven model leverages the high-quality data generated from the offline physics-based simulation for the training of a deep neural network constructed from long short-term memory (LSTM) cells. The results are expected to form a good basis for online robot navigation and exploration in unknown and complex terrains.