Abstract
This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.
| Original language | English |
|---|---|
| Title of host publication | AIP Conference Proceedings |
| Pages | 696-703 |
| Number of pages | 8 |
| Volume | 1225 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 10th Asian International Conference on Fluid Machinery, AICFM - Kuala Lumpur, Malaysia Duration: Oct 21 2010 → Oct 23 2010 |
Other
| Other | 10th Asian International Conference on Fluid Machinery, AICFM |
|---|---|
| Country | Malaysia |
| City | Kuala Lumpur |
| Period | 10/21/10 → 10/23/10 |
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Keywords
- Axial fan
- Evolutionary algorithm
- Pareto-optimal solutions
- Surrogate model
- Torque
- Total efficiency
ASJC Scopus subject areas
- Physics and Astronomy(all)
Cite this
Design optimization of an axial fan blade through multi-objective evolutionary algorithm. / Kim, Jin Hyuk; Choi, Jae Ho; Husain, Afzal; Kim, Kwang Yong.
AIP Conference Proceedings. Vol. 1225 2010. p. 696-703.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Design optimization of an axial fan blade through multi-objective evolutionary algorithm
AU - Kim, Jin Hyuk
AU - Choi, Jae Ho
AU - Husain, Afzal
AU - Kim, Kwang Yong
PY - 2010
Y1 - 2010
N2 - This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.
AB - This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.
KW - Axial fan
KW - Evolutionary algorithm
KW - Pareto-optimal solutions
KW - Surrogate model
KW - Torque
KW - Total efficiency
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UR - http://www.scopus.com/inward/citedby.url?scp=78549236839&partnerID=8YFLogxK
U2 - 10.1063/1.3464918
DO - 10.1063/1.3464918
M3 - Conference contribution
SN - 9780735407695
VL - 1225
SP - 696
EP - 703
BT - AIP Conference Proceedings
ER -