Current Search: computer simulation modeling (x)
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 Title
 Personal Computer Simulation Program for Step Motor Drive Systems.
 Creator

Koos, William M., Harden, Richard C., Engineering
 Abstract / Description

University of Central Florida College of Engineering Thesis; A system of equations modeling a class of step motors known as the permanent magnet rotor step motor is presented. The model is implemented on a APPLE personal computer in a version of BASIC. Measurements are then made on an existing motor and input to the program for validation. A special test fixture is utilized to take performance data on the motor to facilitate comparisons with the predictions of the program. The comparisons...
Show moreUniversity of Central Florida College of Engineering Thesis; A system of equations modeling a class of step motors known as the permanent magnet rotor step motor is presented. The model is implemented on a APPLE personal computer in a version of BASIC. Measurements are then made on an existing motor and input to the program for validation. A special test fixture is utilized to take performance data on the motor to facilitate comparisons with the predictions of the program. The comparisons show the model is indeed valid for design of step motor drive systems and emphasize the practical nature of using personal computers and simulations for design
Show less  Date Issued
 1982
 Identifier
 CFR0008163, ucf:53067
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFR0008163
 Title
 USING COMPUTER SIMULATION MODELING TO EVALUATE THE BIOTERRORISMRESPONSE PLAN AT A LOCAL HOSPITAL FACILITY.
 Creator

Bebber, Robert, Liberman, Aaron, University of Central Florida
 Abstract / Description

The terrorist attacks of September 11th, 2001 and the subsequent anthrax mail attack have forced health care administrators and policy makers to place a new emphasis on disaster planning at hospital facilitiesspecifically bioterrorism planning. Yet how does one truly "prepare" for the unpredictable? In spite of accreditation requirements, which demand hospitals put in to place preparations to deal with bioterrorism events, a recent study from the General Accounting Office (GAO) concluded...
Show moreThe terrorist attacks of September 11th, 2001 and the subsequent anthrax mail attack have forced health care administrators and policy makers to place a new emphasis on disaster planning at hospital facilitiesspecifically bioterrorism planning. Yet how does one truly "prepare" for the unpredictable? In spite of accreditation requirements, which demand hospitals put in to place preparations to deal with bioterrorism events, a recent study from the General Accounting Office (GAO) concluded that most hospitals are still not capable of dealing with such threats (Gonzalez, 2004). This dissertation uses computer simulation modeling to test the effectiveness of bioterrorism planning at a local hospital facility in Central Florida, Winter Park Memorial Hospital. It is limited to the response plan developed by the hospital's Emergency Department. It evaluates the plan's effectiveness in dealing with an inhalational anthrax attack. Using Arena computer simulation software, and grounded within the theoretical framework of Complexity Science, we were able to test the effectiveness of the response plan in relation to Emergency Department bed capacity. Our results indicated that the response plan's flexibility was able to accommodate an increased patient load due to an attack, including an influx of the "worried well." Topics of future work and study are proposed.
Show less  Date Issued
 2007
 Identifier
 CFE0001712, ucf:47293
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFE0001712
 Title
 AN IMPROVED THERMOREGULATORY MODEL FOR COOLING GARMENT APPLICATIONS WITH TRANSIENT METABOLIC RATES.
 Creator

Westin, Johan, Kapat, Jayanta, University of Central Florida
 Abstract / Description

Current stateoftheart thermoregulatory models do not predict body temperatures with the accuracies that are required for the development of automatic cooling control in liquid cooling garment (LCG) systems. Automatic cooling control would be beneficial in a variety of space, aviation, military, and industrial environments for optimizing cooling efficiency, for making LCGs as portable and practical as possible, for alleviating the individual from manual cooling control, and for improving...
Show moreCurrent stateoftheart thermoregulatory models do not predict body temperatures with the accuracies that are required for the development of automatic cooling control in liquid cooling garment (LCG) systems. Automatic cooling control would be beneficial in a variety of space, aviation, military, and industrial environments for optimizing cooling efficiency, for making LCGs as portable and practical as possible, for alleviating the individual from manual cooling control, and for improving thermal comfort and cognitive performance. In this study, we adopt the Fiala thermoregulatory model, which has previously demonstrated stateoftheart predictive abilities in air environments, for use in LCG environments. We validate the numerical formulation with analytical solutions to the bioheat equation, and find our model to be accurate and stable with a variety of different grid configurations. We then compare the thermoregulatory model's tissue temperature predictions with experimental data where individuals, equipped with an LCG, exercise according to a 700 W rectangular type activity schedule. The root mean square (RMS) deviation between the model response and the mean experimental group response is 0.16°C for the rectal temperature and 0.70°C for the mean skin temperature, which is within stateoftheart variations. However, with a mean absolute body heat storage error (e_BHS_mean) of 9.7 W·h, the model fails to satisfy the ±6.5 W·h accuracy that is required for the automatic LCG cooling control development. In order to improve model predictions, we modify the blood flow dynamics of the thermoregulatory model. Instead of using step responses to changing requirements, we introduce exponential responses to the muscle blood flow and the vasoconstriction command. We find that such modifications have an insignificant effect on temperature predictions. However, a new vasoconstriction dependency, i.e. the rate of change of hypothalamus temperature weighted by the hypothalamus error signal (DThy·dThy/dt), proves to be an important signal that governs the thermoregulatory response during conditions of simultaneously increasing core and decreasing skin temperatures, which is a common scenario in LCG environments. With the new DThy·dThy/dt dependency in the vasoconstriction command, the e_BHS_mean for the exercise period is reduced by 59% (from 12.9 W·h to 5.2 W·h). Even though the new e_BHS_mean of 5.8 W·h for the total activity schedule is within the target accuracy of ±6.5 W·h, e_BHS fails to stay within the target accuracy during the entire activity schedule. With additional improvements to the central blood pool formulation, the LCG boundary condition, and the agreement between model setpoints and actual experimental initial conditions, it seems possible to achieve the strict accuracy that is needed for automatic cooling control development.
Show less  Date Issued
 2008
 Identifier
 CFE0002460, ucf:47707
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFE0002460
 Title
 MATHEMATICAL MODELING OF SMALLPOX WITHOPTIMAL INTERVENTION POLICY.
 Creator

LAWOT, NIWAS, ROLLINS, DAVID, University of Central Florida
 Abstract / Description

In this work, two differential equation models for smallpox are numerically solved to find the optimal intervention policy. In each model we look for the range of values of the parameters that give rise to the worst case scenarios. Since the scale of an epidemic is determined by the number of people infected, and eventually dead, as a result of infection, we attempt to quantify the scale of the epidemic and recommend the optimum intervention policy. In the first case study, we mimic a densely...
Show moreIn this work, two differential equation models for smallpox are numerically solved to find the optimal intervention policy. In each model we look for the range of values of the parameters that give rise to the worst case scenarios. Since the scale of an epidemic is determined by the number of people infected, and eventually dead, as a result of infection, we attempt to quantify the scale of the epidemic and recommend the optimum intervention policy. In the first case study, we mimic a densely populated city with comparatively big tourist population, and heavily used mass transportation system. A mathematical model for the transmission of smallpox is formulated, and numerically solved. In the second case study, we incorporate five different stages of infection: (1) susceptible (2) infected but asymptomatic, non infectious, and vaccinesensitive; (3) infected but asymptomatic, noninfectious, and vaccineinsensitive; (4) infected but asymptomatic, and infectious; and (5) symptomatic and isolated. Exponential probability distribution is used for modeling this case. We compare outcomes of mass vaccination and trace vaccination on the final size of the epidemic.
Show less  Date Issued
 2006
 Identifier
 CFE0001193, ucf:46848
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFE0001193
 Title
 Modeling Transport and Protein Adsorption in Microfluidic Systems.
 Creator

Finch, Craig, Hickman, James, Kincaid, John, Lin, KuoChi, Behal, Aman, Cho, Hyoung, University of Central Florida
 Abstract / Description

This work describes theoretical advances in the modeling and simulation of microfluidic systems and demonstrates the practical application of those techniques. A new multiscale model of the adsorption of hard spheres was formulated to bridge the gap between simulations of discrete particles and continuum fluid dynamics. A whispering gallery mode (WGM) biosensor was constructed and used to measure the kinetics of adsorption for two types of proteins on four different surfaces. Computational...
Show moreThis work describes theoretical advances in the modeling and simulation of microfluidic systems and demonstrates the practical application of those techniques. A new multiscale model of the adsorption of hard spheres was formulated to bridge the gap between simulations of discrete particles and continuum fluid dynamics. A whispering gallery mode (WGM) biosensor was constructed and used to measure the kinetics of adsorption for two types of proteins on four different surfaces. Computational fluid dynamics was used to analyze the transport of proteins in the flow cell of the biosensor. Kinetic models of protein adsorption that take transport limitations into account were fitted to the experimental data and used to draw conclusions about the mechanisms of adsorption. Transport simulations were then applied to the practical problem of optimizing the design of a microfluidic bioreactor to enable (")plugs(") of fluid to flow from one chamber to the next with minimal dispersion. Experiments were used to validate the transport simulations. The combination of quantitative modeling and simulation and experiments led to results that could not have been achieved using either approach by itself. Simulation tools that accurately predict transport and protein adsorption will enable the rational design of microfluidic devices for biomedical applications.
Show less  Date Issued
 2011
 Identifier
 CFE0004474, ucf:49313
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFE0004474
 Title
 Information Propagation Algorithms for Consensus Formation in Decentralized MultiAgent Systems.
 Creator

Hollander, Christopher, Wu, Annie, Shumaker, Randall, Wiegand, Rudolf, Turgut, Damla, Song, Zixia, University of Central Florida
 Abstract / Description

Consensus occurs within a multiagent system when every agent is in agreement about the value of some particular state. For example, the color of an LED, the position or magnitude of a vector, a rendezvous location, the most recent state of data within a database, or the identity of a leader are all states that agents might need to agree on in order to execute their tasking.The task of the decentralized consensus problem for multiagent systems is to design an algorithm that enables agents to...
Show moreConsensus occurs within a multiagent system when every agent is in agreement about the value of some particular state. For example, the color of an LED, the position or magnitude of a vector, a rendezvous location, the most recent state of data within a database, or the identity of a leader are all states that agents might need to agree on in order to execute their tasking.The task of the decentralized consensus problem for multiagent systems is to design an algorithm that enables agents to communicate and exchange information such that, in finite time, agents are able to form a consensus without the use of a centralized control mechanism. The primary goal of this research is to introduce and provide supporting evidence for Stochastic Local Observation/Gossip (SLOG) algorithms as a new class of solutions to the decentralized consensus problem for multiagent systems that lack a centralized controller, with the additional constraints that agents act asynchronously, information is discrete, and all consensus options are equally preferable to all agents. Examples of where these constraints might apply include the spread of social norms and conventions in artificial populations, rendezvous among a set of specific locations, and task assignment.This goal is achieved through a combination of theory and experimentation. Information propagation process and an information propagation algorithm are derived by unifying the general structure of multiple existing solutions to the decentralized consensus problem. They are then used to define two classes of algorithms that spread information across a network and solve the decentralized consensus problem: buffered gossip algorithms and local observation algorithms. Buffered gossip algorithms generalize the behavior of many pushbased solutions to the decentralized consensus problem. Local observation algorithms generalize the behavior of many pullbased solutions to the decentralized consensus problem. In the language of object oriented design, buffered gossip algorithms and local observation algorithms are abstract classes; information propagation processes are interfaces. SLOG algorithms combine the transmission mechanisms of buffered gossip algorithms and local observation algorithms into a single "hybrid" algorithm that is able to push and pull information within the local neighborhood. A common mathematical framework is constructed and used to determine the conditions under which each of these algorithms are guaranteed to produce a consensus, and thus solve the decentralized consensus problem. Finally, a series of simulation experiments are conducted to study the performance of SLOG algorithms. These experiments compare the average speed of consensus formation between buffered gossip algorithms, local observation algorithms, and SLOG algorithms over four distinct network topologies.Beyond the introduction of the SLOG algorithm, this research also contributes to the existing literature on the decentralized consensus problem by: specifying a theoretical framework that can be used to explore the consensus behavior of pushbased and pullbased information propagation algorithms; using this framework to define buffered gossip algorithms and local observation algorithms as generalizations for existing solutions to the decentralized consensus problem; highlighting the similarities between consensus algorithms within control theory and opinion dynamics within computational sociology, and showing how these research areas can be successfully combined to create new and powerful algorithms; and providing an empirical comparison between multiple information propagation algorithms.
Show less  Date Issued
 2015
 Identifier
 CFE0005629, ucf:50229
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFE0005629
 Title
 Network Partitioning in Distributed AgentBased Models.
 Creator

Petkova, Antoniya, Deo, Narsingh, Hughes, Charles, Bassiouni, Mostafa, Shaykhian, Gholam, University of Central Florida
 Abstract / Description

AgentBased Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as selforganizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in realtime is critical.The realtime requirement...
Show moreAgentBased Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as selforganizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in realtime is critical.The realtime requirement necessitates the use of inmemory computing, as it is dif?cult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use parallel and distributed computing platforms, such as MPIenabled CPU clusters. Consequently, the agent population must be "partitioned" across different CPUs in a cluster. Further, the typically high volume of interactions among agents can quickly become a signi?cant bottleneck for realtime or largescale simulations. The problem is exacerbated if the underlying ABM network is dynamic and the interprocess communication evolves over the course of the simulation. Therefore, it is critical to develop topologyaware partitioning mechanisms to support such large simulations.In this dissertation, we demonstrate that distributed agentbased model simulations bene?t from the use of graph partitioning algorithms that involve a local, neighborhoodbased perspective. Such methods do not rely on global accesses to the network and thus are more scalable. In addition, we propose two partitioning schemes that consider the bottomup individualcentric nature of agentbased modeling. The ?rst technique utilizes labelpropagation community detection to partition the dynamic agent network of an ABM. We propose a latencyhiding, seamless integration of community detection in the dynamics of a distributed ABM. To achieve this integration, we exploit the similarity in the process flow patterns of a labelpropagation communitydetection algorithm and selforganizing ABMs.In the second partitioning scheme, we apply a combination of the Guided Local Search (GLS) and Fast Local Search (FLS) metaheuristics in the context of graph partitioning. The main driving principle of GLS is the dynamic modi?cation of the objective function to escape local optima. The algorithm augments the objective of a local search, thereby transforming the landscape structure and escaping a local optimum. FLS is a local search heuristic algorithm that is aimed at reducing the search space of the main search algorithm. It breaks down the space into subneighborhoods such that inactive subneighborhoods are removed from the search process. The combination of GLS and FLS allowed us to design a graph partitioning algorithm that is both scalable and sensitive to the inherent modularity of realworld networks.
Show less  Date Issued
 2017
 Identifier
 CFE0006903, ucf:51706
 Format
 Document (PDF)
 PURL
 http://purl.flvc.org/ucf/fd/CFE0006903