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Parallel mechanisms are characterized by their kinematic structure presented a closed kinematic chain. The end effector of mechanism is then bound to a base of over one arm. This design provides an advantage especially at high stiffness of mechanism and related properties. The disadvantage of such a structure is then usually very limited working space. In terms of simulation modeling is the construction due to its complexity rather problematic. The aim of work is design comprehensive simulation model chosen parallel mechanism and its analysis in selected working modes. Expected to use Adams simulation environment, if necessary. Matlab - SimMechanics and Ansys.

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No description available.

This research explores a hardware-based parallel simulation mechanism that can dramatically improve the speed of simulating flexible manufacturing systems (FMS) by applying appropriate enabling hardware technologies. The hardware-based parallel simulation refers to running a simulation on a multi-microprocessor integrated circuit board, called the simulator, which is specifically designed for the purpose of simulating a specific FMS. The board is composed of a collection of micro-emulators capable of mimicking the operation of equipment in FMS such as machining centers, transporters, and load/unload stations. To design possible architectures for the board, a mapping technology is applied by making use of the physical layout information of an FMS. Under such a mapping method, the simulation model is decomposed into a cluster of micro emulator on the board where each workstation is represented by one micro emulator. Three potential architectures for the proposed simulator, namely, the bus-based architecture, the shared-memory based architecture, and the parallel I/O port based architecture, are studied. To provide a suitable parallel computing platform, a prototype simulator based on the combination of the shared-memory and the parallel I/O port architecture is physically built. Besides the development of the hardware simulator, a time scaling simulation method is also developed for execution on the proposed simulator. The method uses the on-board digital clock to synchronize the parallel simulation being performed on different microprocessors. The advantage of the time scaling technology is that the sequence of simulation events is sorted naturally in consistent with the real events. In this way, no entangled waiting is needed as in the conservative parallel simulation methods so as to reduce the synchronization overhead and the danger of having deadlock. Experiments on the prototype simulator show that the time scaling simulation method, combined with the unique hardware features of the FMS specific simulator, achieves a large speedup compared to conventional software-based simulation methods. / Ph. D.

Scientific visualization is primarily concerned with the visual presentation of three-dimensional phenomena in domains like medicine, meteorology, astrophysics, etc. The emphasis in scientific visualization research has been on the efficient rendering of measured or simulated data points, surfaces, volumes, and a time component to convey the dynamic nature of the studied phenomena. With the explosive growth in the size of the data, interactive visualization of scientific data becomes a real challenge. In recent years, the graphics community has witnessed tremendous improvements in the performance capabilities of graphics processing units (GPUs), and advances in GPU-accelerated rendering have enabled data exploration at interactive rates. Nevertheless, the majority of techniques rely on the assumption that a true three-dimensional geometric model capturing physical phenomena of interest, is available and ready for visualization. Unfortunately, this assumption does not hold true in many scientific domains, in which measurements are obtained from a given scanning modality at sparsely located intervals in both space and time. This calls for the fusion of data collected from multiple sources in order to fill the gaps and tell the story behind the data. For years, data fusion has relied on machine learning techniques to combine data from multiple modalities, reconstruct missing information, and track features of interest through time. However, these techniques fall short in solving the problem for datasets with large spatio-temporal gaps. This realization has led researchers in the data fusion domain to acknowledge the importance of human-in-the-loop methods where human expertise plays a major role in data reconstruction. This PhD research focuses on developing visualization and interaction techniques aimed at addressing some of the challenges that experts are faced with when analyzing the spatio-temporal behavior of physical phenomena. Given a number of datasets obtained from different measurement modalities and from simulation, we propose a generalized framework that can guide research in the field of multi-sensor data fusion and visualization. We advocate the use of GPU parallelism in our developed techniques in order to emphasize interaction as a key component in the successful exploration and analysis of multi-sourced data sets. The goal is to allow the user to create a mental model that captures their understanding of the spatio-temporal behavior of features of interest; one which they can test against real data measurements to verify their model. This model creation and verification is an iterative process in which the user interacts with the visualization, explores and builds an understanding of what occurred in the data, then tests this understanding against real-world measurements and improves it. We developed a system as a reference implementation of the proposed framework. Reconstructed data is rendered in a way that completes the users' cognitive model, which encodes their understanding of the phenomena in question with a high degree of accuracy. We tested the usability of the system and evaluated its support for this cognitive model construction process. Once an acceptable model is constructed, it is fed back to the system in the form of a reference dataset, which our framework uses to guide the real-time tracking of measurement data. Our results show that interactive exploration tasks enable the construction of this cognitive model and reference set, and that real-time interaction is achievable during the exploration, reconstruction, and enhancement of multi-modal time-variant three-dimensional data, by designing and implementing advanced GPU-based visualization techniques. / Ph. D.

Fused Deposition Modeling is a rapid prototyping technique in which miniature extruders melt filaments of polymeric materials. The extruder is mounted to an X/Y stage, and a computer controls the machine so that the polymer is deposited in only the appropriate locations. Material is deposited on one layer at a time so that the desired shape is built from the bottom up. While Fused Deposition Modeling has many advantages, it is poorly suited for large parts or for parts with thick walls due to the amount of time that is required to fabricate them. One strategy to reduce the build time is to implement multiple independent extrusion-heads. This thesis addresses various issues and concerns that arise while designing a multiple independent extrusion-head Fused Deposition Modeling system. The greatest design challenges and most critical issues are identified, and then solutions are presented. Physical samples and experiments verify feasibility when possible. Suitable material deposition strategies have been formulated to allow multiple independent extrusion heads to work simultaneously to reduce build time while allowing for a larger build envelope. These strategies produce parts that have nearly identical mechanical properties as those made on a single-head machine. This work seeks to provide information that is useful for designing a multiple independent extrusion-head Fused Deposition Modeling, regardless the number of extrusion heads or machine configuration. Implementing multiple independent extrusion heads will greatly reduce the fabrication time while allowing for a larger build envelope. / Master of Science

This thesis presents the parallel version of Coliseum, the Air Force Research Laboratory plasma simulation framework. The parallel code was designed to run large simulations on the world fastest supercomputers as well as home mode clusters. Plasma simulations are extremely computationally intensive as they require tracking millions of particles and solving field equations over large domains. This new parallel version will allow Coliseum to run simulations of spacecraft-plasma interactions in domain large enough to reproduce space conditions. The parallel code ran on two of the world fastest supercomputers, the NASA JPL Cosmos supercomputer ranked 37th on the TOP500 list and Virginia Tech's System X, ranked 7th. DRACO, the Virginia Tech PIC module to Coliseum, was modified with parallel algorithms to create a full parallel PIC code. A parallel solver was added to DRACO. It uses a Gauss-Seidel method with SOR acceleration on a Red-Black checkerboard scheme. Timing results were obtained on JPL Cosmos supercomputer to determine the efficiency of the parallel code. Although the communication overhead limits the code's parallel efficiency, the speed up obtained greatly decreases the time required to run the simulations. A speed up of 51 was reached on 128 processors. The parallel code was also used to simulate the plume expansion of an ion thruster array composed of three NSTAR thrusters. Results showed that the multiple beams merge to form a single plume similar to the plume created by a single ion thruster. / Master of Science

Automatic generation of starvation-free semaphore solutions to general mutual exclusion problems is discussed. A reduction approach is introduced for recognizing edge-solvable problems, together with an O(N^2) algorithm for graph reduction, where N is the number of nodes. An algorithm for the automatic generation of starvation-free edge-solvable solutions is presented. The solutions are proved to be very efficient. For general problems, there are two ways to generate efficient solutions. One associates a semaphore with every node, the other with every edge. They are both better than the standard monitor—like solutions. Besides strong semaphores, solutions using weak semaphores, weaker semaphores and generalized semaphores are also considered. Basic properties of semaphore solutions are also discussed. Tools describing the dynamic behavior of parallel systems, as well as performance criteria for evaluating semaphore solutions are elaborated.