Abstract
The shape of the blade stacking line greatly influences the low-pressure axial-flow fan’s operational efficiency, but there is still no fast and effective way to determine the stacking line’s optimal shape. This paper presents an automatic optimization design procedure for the blade stacking line in the low-pressure axial-flow fan based on the FINE/Design3D™ platform. The procedure combines computational fluid dynamics (CFD), surrogate model method, and genetic algorithm (GA) to execute a secondary optimization on a composite skewed-swept rotor-only axial fan blade. The results show that the static efficiency and the static pressure rise of the optimized fan respectively increase by 3.76 % and 5.82 % without stall margin decrease. The blade shape variation in skew and sweep direction reduces the tip leakage flow loss, improves the blade loading distribution, and contributes to the efficiency increment. This research provides a useful reference for the blade stacking line’s automatic optimization for the axial-flow fan.
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Abbreviations
- CFD :
-
Computational fluid dynamics
- DoE :
-
Design of experiments
- GA :
-
Genetic algorithm
- LCVT :
-
Latinized centroidal voronoi tessellations
- LE :
-
Leading-edge
- RBF :
-
Radial basis functions
- TE :
-
Trailing-edge
- n :
-
Rotational speed
- p 1t :
-
Total pressure at the inlet
- p 2s :
-
Static pressure at the outlet
- p t :
-
Total pressure
- Δp ts :
-
Total to static pressure rise
- q v :
-
Volume flow rate
- v z :
-
Axial velocity
- D :
-
Blade tip diameter
- D H :
-
Hub diameter
- D S :
-
Shroud diameter
- P Shaft :
-
Shaft power
- Z :
-
Number of blades
- η ts :
-
Total to static efficiency
- φ :
-
Flow coefficient
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Acknowledgments
The authors would like to acknowledge Thomas H. Carolus for his generous provision of geometry and testing data about USI7. The computational resource was supported by the HPC center of ZJU (ZHOUSHAN CAMPUS).
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Chuang Kong is currently a Ph.D. candidate in the Institute of Process Equipment, College of Energy Engineering, Zhejiang University, China. He received his B.S. degree from Dalian University of Technology, China, in 2016. His research interests include computational fluid dynamics and design optimization of turbomachinery.
Tao Jin is currently a Professor at Zhejiang University, China. He received his B.S. degree from Zhejiang University, in 1985, and his M.S. and Ph.D. in the Department of Chemical Engineering from the same university in 1989 and 2000, respectively. His research interests include fluid-machinery, computational aided design, and computational aided engineering.
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Kong, C., Wang, M., Jin, T. et al. An optimization on the stacking line of low-pressure axial-flow fan using the surrogate-assistant optimization method. J Mech Sci Technol 35, 4997–5005 (2021). https://doi.org/10.1007/s12206-021-1018-x
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DOI: https://doi.org/10.1007/s12206-021-1018-x