Title:
A Hybrid Optimization Scheme for Helicopters with Composite Rotor Blades

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dc.contributor.advisor Schrage, Daniel P.
dc.contributor.advisor Hodges, Dewey H.
dc.contributor.author Ku, Jieun en_US
dc.contributor.committeeMember Nygren, Kip P.
dc.contributor.committeeMember Bauchau, Olivier A.
dc.contributor.committeeMember Volovoi, Vitali
dc.contributor.department Aerospace Engineering en_US
dc.date.accessioned 2007-08-16T17:56:05Z
dc.date.available 2007-08-16T17:56:05Z
dc.date.issued 2007-05-18 en_US
dc.description.abstract Rotorcraft optimization is a challenging problem due to its conflicting requirements among many disciplines and highly coupled design variables affecting the overall design. Also, the design process for a composite rotor blade is often ambiguous because of its design space. Furthermore, analytical tools do not produce acceptable results compared with flight test when it comes to aerodynamics and aeroelasticity unless realistic models are used, which leads to excessive computer time per iteration. To comply these requirements, computationally efficient yet realistic tools for rotorcraft analysis, such as VABS and DYMORE were used as analysis tools. These tools decompose a three-dimensional problem into a two-dimensional cross-sectional and a one-dimensional beam analysis. Also, to eliminate the human interaction between iterations, a previously VABS-ANSYS macro was modified and automated. The automated tool shortened the computer time needed to generate the VABS input file for each analysis from hours to seconds. MATLAB was used as the wrapper tool to integrate VABS, DYMORE and the VABS-ANSYS macro into the methodology. This methodology uses Genetic Algorithm and gradient-based methods as optimization schemes. The baseline model is the rotor system of generic Georgia Tech Helicopter (GTH), which is a three-bladed, soft-in-plane, bearingless rotor system. The resulting methodology is a two-level optimization, global and local. Previous studies showed that when stiffnesses are used as design variables in optimization, these values act as if they are independent and produce design requirements that cannot be achieved by local-level optimization. To force design variables at the global level to stay within the feasible design space of the local level, a surrogate model was adapted into the methodology. For the surrogate model, different ``design of experiments" (DOE) methods were tested to find the most computationally efficient DOE method. The response surface method (RSM) and Kriging were tested for the optimization problem. The results show that using the surrogate model speeds up the optimization process and the Kriging model shows superior performance over RSM models. As a result, the global-level optimizer produces requirements that the local optimizer can achieve. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/16268
dc.publisher Georgia Institute of Technology en_US
dc.subject Frequency placement en_US
dc.subject Genetic algorithms en_US
dc.subject Multi-level optimization en_US
dc.subject Surrogate models en_US
dc.subject Composite blades en_US
dc.subject Hybrid optimization en_US
dc.title A Hybrid Optimization Scheme for Helicopters with Composite Rotor Blades en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication f6a932db-1cde-43b5-bcab-bf573da55ed6
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