Multivariable optimization of liquid rocket engines using Particle Swarm algorithms
dc.contributor | Olcmen, Semih M. | |
dc.contributor | Ray, Paul S. | |
dc.contributor.advisor | Baker, John | |
dc.contributor.author | Jones, Daniel | |
dc.contributor.other | University of Alabama Tuscaloosa | |
dc.date.accessioned | 2017-03-01T16:53:34Z | |
dc.date.available | 2017-03-01T16:53:34Z | |
dc.date.issued | 2013 | |
dc.description | Electronic Thesis or Dissertation | en_US |
dc.description.abstract | Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables. | en_US |
dc.format.extent | 103 p. | |
dc.format.medium | electronic | |
dc.format.mimetype | application/pdf | |
dc.identifier.other | u0015_0000001_0001423 | |
dc.identifier.other | Jones_alatus_0004M_11561 | |
dc.identifier.uri | https://ir.ua.edu/handle/123456789/1888 | |
dc.language | English | |
dc.language.iso | en_US | |
dc.publisher | University of Alabama Libraries | |
dc.relation.hasversion | born digital | |
dc.relation.ispartof | The University of Alabama Electronic Theses and Dissertations | |
dc.relation.ispartof | The University of Alabama Libraries Digital Collections | |
dc.rights | All rights reserved by the author unless otherwise indicated. | en_US |
dc.subject | Aerospace engineering | |
dc.subject | Engineering | |
dc.subject | Mechanical engineering | |
dc.title | Multivariable optimization of liquid rocket engines using Particle Swarm algorithms | en_US |
dc.type | thesis | |
dc.type | text | |
etdms.degree.department | University of Alabama. Department of Aerospace Engineering and Mechanics | |
etdms.degree.discipline | Aerospace Engineering | |
etdms.degree.grantor | The University of Alabama | |
etdms.degree.level | master's | |
etdms.degree.name | M.S. |
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