Abstract
A three-stage axial turbine was redesigned by jointly applying S2 flow surface direct problem calculation methods and multistage local optimization methods. A genetic algorithm and artificial neural network were jointly adopted during optimization. A three-dimensional viscosity Navier-Stokes equation solver was applied for flow computation. H-O-H-topology grid was adopted as computation grid, that is, an H-topology grid was adopted for inlet and outlet segment, whereas an O-topology grid was adopted for stator zone and rotor zone. Through the optimization design, the total efficiency increases 1.1%, thus indicating that the total performance is improved and the design objective is achieved.
| Original language | English |
|---|---|
| Pages (from-to) | 93-98 |
| Number of pages | 6 |
| Journal | Frontiers of Energy and Power Engineering in China |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2008 |
Keywords
- Artificial neural network
- Genetic algorithm
- Optimization design
- S2 flow surface direct problem calculation
- Turbine
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