Mechanical Engineering Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2795

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Subject: search.filters.subject.Engineering, Robotics

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    DESIGN, ANALYSIS, AND TESTING OF A FLAPPING WING MINIATURE AIR VEHICLE
    (2010) Gerdes, John William; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Flapping wing miniature air vehicles (MAVs) offer several advantageous performance benefits, relative to fixed-wing and rotary-wing MAVs. The goal of this thesis is to design a flapping wing MAV that achieves improved performance by focusing on the flapping mechanism and the spar arrangement in the wings. Two variations of the flapping mechanism are designed and tested, both using compliance as a technique for improved functionality. In the design of these mechanisms, kinematics and dynamics simulation is used to evaluate how forces encountered during wing flapping affect the mechanism. Finite element analysis is used to evaluate the stress and deformation of the mechanism, such that a lightweight yet functional design can be realized. The wings are tested using experimental techniques. These techniques include high speed photography, stiffness measurement, and lift and thrust measurements. Experimentally measured force results are validated with a series of flight tests. A framework for iterative improvement of the MAV is described, that uses the results of physical testing and simulations to investigate the underlying causes of MAV performance aspects; and seeks to capture those beneficial aspects that will allow for performance improvements. Wings and flapping mechanisms designed in this thesis are used to realize a bird-inspired flapping wing miniature air vehicle. This vehicle is capable of radio controlled flights indoors and outdoors in winds up to 6.7m/s with controlled steering, ascent, and descent, as well as payload carrying abilities.
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    Real-Time Path Planning for Automating Optical Tweezers based Particle Transport Operations
    (2009) Banerjee, Ashis Gopal; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Optical tweezers (OT) have been developed to successfully trap, orient, and transport micro and nano scale components of many different sizes and shapes in a fluid medium. They can be viewed as robots made out of light. Components can be simply released from optical traps by switching off laser beams. By utilizing the principle of time sharing or holograms, multiple optical traps can perform several operations in parallel. These characteristics make optical tweezers a very promising technology for creating directed micro and nano scale assemblies. In the infra-red regime, they are useful in a large number of biological applications as well. This dissertation explores the problem of real-time path planning for autonomous OT based transport operations. Such operations pose interesting challenges as the environment is uncertain and dynamic due to the random Brownian motion of the particles and noise in the imaging based measurements. Silica microspheres having diameters between (1-20) µm are selected as model components. Offline simulations are performed to gather trapping probability data that serves as a measure of trap strength and reliability as a function of relative position of the particle under consideration with respect to the trap focus, and trap velocity. Simplified models are generated using Gaussian Radial Basis Functions to represent the data in a compact form. These metamodels can be queried at run-time to obtain estimated probability values accurately and efficiently. Simple trapping probability models are then utilized in a stochastic dynamic programming framework to compute optimum trap locations and velocities that minimizes the total, expected transport time by incorporating collision avoidance and recovery steps. A discrete version of an approximate partially observable Markov decision process algorithm, called the QMDP_NLTDV algorithm, is developed. Real-time performance is ensured by pruning the search space and enhancing convergence rates by introducing a non-linear value function. The algorithm is validated both using a simulator as well as a physical holographic tweezer set-up. Successful runs show that the automated planner is flexible, works well in reasonably crowded scenes, and is capable of transporting a specific particle to a given goal location by avoiding collisions either by circumventing or by trapping other freely diffusing particles. This technique for transporting individual particles is utilized within a decoupled and prioritized approach to move multiple particles simultaneously. An iterative version of a bipartite graph matching algorithm is also used to assign goal locations to target objects optimally. As in the case of single particle transport, simulation and some physical experiments are performed to validate the multi-particle planning approach.
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    Automatic Generation of Generalized Event Sequence Diagrams for Guiding Simulation Based Dynamic Probabilistic Risk Assessment of Complex Systems
    (2007-11-27) Nejad-Hosseinian, Seyed Hamed; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past two decades to evaluate the risks associated with complex systems such as nuclear power plants, space missions, chemical plants, and military systems. A critical step in DPRA is generating risk scenarios which are used to enumerate and assess the probability of different outcomes. The classical approach to generating risk scenarios is not, however, sufficient to deal with the complexity of the above-mentioned systems. The primary contribution of this dissertation is in offering a new method for capturing different types of engineering knowledge and using them to automatically generate risk scenarios, presented in the form of generalized event sequence diagrams, for dynamic systems. This new method, as well as several important applications, is described in detail. The most important application is within a new framework for DPRA in which the risk simulation environment is guided to explore more interesting scenarios such as low-probability/high-consequence scenarios. Another application considered is the use of the method to enhance the process of risk-based design.