To decrease the computational cost of genetic algorithm optimizations,
surrogate models are used during optimization. Online update of surrogate
models and repeated exchange of surrogate models with exact model during
genetic optimization converts static optimization problems to dynamic ones.
However, genetic algorithms fail to converge to the global optimum in
dynamic optimization problems. To address these problems, a multiploid
genetic algorithm optimization method is proposed. Multi-fidelity surrogate
models are assigned to corresponding levels of fitness values to sustain the
static optimization problem. Low fidelity fitness values are used to decrease
the computational cost. The exact/highest-fidelity model fitness value is used for converging to the global optimum. The algorithm is applied to
single and multi-objective turbine blade aerodynamic optimization
problems. The design objectives are selected as maximizing the adiabatic
efficiency and torque so as to reduce the weight, size and the cost of the gas
turbine engine. A 3-D steady Reynolds-Averaged Navier-Stokes solver is
coupled with an automated unstructured grid generation tool. The solver is
validated by using two well known test cases. Blade geometry is modelled
by 37 design variables. Fine and coarse grid solutions are respected as high
and low fidelity surrogate models, respectively. One of the test cases is
selected as the baseline and is modified in the design process. The effects of
input parameters on the performance of the multiploid genetic algorithm are
studied. It is demonstrated that the proposed algorithm accelerates the
optimization cycle while providing convergence to the global optimum for
single and multi-objective problems.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609196/index.pdf |
| Date | 01 December 2007 |
| Creators | Oksuz, Ozhan |
| Contributors | Akmandor, Ibrahim Sinan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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