A Hybrid Multi-Objective Evolutionary Algorithm for Wind-Turbine Blade Optimization

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atmire.migration.oldid878
dc.contributor.advisorRival, David Emory
dc.contributor.advisorWood, David Howe
dc.contributor.advisorDixon, Kristian Robert
dc.contributor.authorSessarego, Matias
dc.date.accessioned2013-04-30T17:49:38Z
dc.date.available2013-06-10T07:00:46Z
dc.date.issued2013-04-30
dc.date.submitted2013en
dc.description.abstractA concurrent-hybrid non-dominated sorting genetic algorithm II (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the National Renewable Energy Laboratory's (NREL) 5MW wind-turbine blade. To estimate the aerodynamic and structural performance, blade element momentum (BEM) and beam models were developed and validated. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved at lower computational cost than for a conventional MOEA. To compare the rate of convergence between the hybrid and non-hybrid NSGA-II on the NREL 5MW blade optimization, a computationally intensive case requiring 110,000 objective-function evaluations was performed using the non-hybrid NSGA-II. From this particular case, a 1.8% increase in the annual energy production and 4.7% decrease in the flapwise root-bending moment with the same mass as the NREL 5MW blade was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem. A study on the gradient quality shows that the numerical instability of BEM and beam models hinders suitable gradient calculations.en_US
dc.identifier.citationSessarego, M. (2013). A Hybrid Multi-Objective Evolutionary Algorithm for Wind-Turbine Blade Optimization (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24761en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/24761
dc.identifier.urihttp://hdl.handle.net/11023/650
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEngineering--Mechanical
dc.subject.classificationwind-turbine blade designen_US
dc.subject.classificationMulti-Objective Optimizationen_US
dc.subject.classificationevolutionary algorithmsen_US
dc.titleA Hybrid Multi-Objective Evolutionary Algorithm for Wind-Turbine Blade Optimization
dc.typemaster thesis
thesis.degree.disciplineMechanical and Manufacturing Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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