Graduate Thesis Or Dissertation
 

Optimizing ballast design of wave energy converters using evolutionary algorithms

Öffentlich Deposited

Herunterladbarer Inhalt

PDF Herunterladen
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/ft848s66q

Descriptions

Attribute NameValues
Creator
Abstract
  • Wave Energy Converters (WECs) promise to be a viable alternative to current electrical generation methods. However, these WECs must become more efficient before wide-scale industrial use can become cost-effective. The efficiency of a WEC is primarily dependent upon its geometry and ballast configuration which are both difficult to evaluate, due to slow computation time and high computation cost of current models. In this thesis, we use evolutionary algorithms to optimize the ballast geometry of a wave energy generator using a two step process. First, we generate a function approximator (neural network) to predict wave energy converter power output with respect to key geometric design variables. This is a critical step as the computation time of using a full model (e.g., AQWA) to predict energy output prohibits the use of an evolutionary algorithm for design optimization. The function approximator reduced the computation time by over 99% while having an average error of only 3.5%. The evolutionary algorithm optimized the weight distribution of a WEC, resulting in an 84% improvement in power output over a ballast-free WEC.
License
Resource Type
Date Available
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Subject
Urheberrechts-Erklärung
Publisher
Peer Reviewed
Language
Replaces

Beziehungen

Parents:

This work has no parents.

In Collection:

Artikel

Miniaturansicht Titel Hochladedatum Sichtbarkeit Aktionen
file details ColbyMitchell2012.pdf 2017-08-03 Öffentlich Herunterladen