Abstract
As a core component of a fan, the blade has a decisive impact on the aerodynamic performance of a low-pressure axial flow fan. Due to the limitations of the classical fan design theory on considering the complex 3D internal flow, the spanwise distribution of blade stacking line and section profiles are usually hard to reach the best state. This paper builds a surrogate-assistant multi-objective optimization flow combined with CFD method to explore the optimum blade shape under two typical working conditions. A total of 16 parameters were selected for demonstrating the blade stacking line and section profiles, according to Morris one-at-a-time sensitivity analysis. The objective and constraint functions were the fan’s total-to-static efficiency and static pressure rise, respectively. During the optimization, the surrogate models of all response functions were built using kriging models, on which the multi-objective genetic algorithm takes the exploration. The optimization results indicate that the maximum improvement of the efficiency is 1.26 % for low mass flow working condition and 5.47 % for high mass flow working condition. The optimized models tend to make the low-pressure zone distributed along the blade leading edge in the meridian view and to reduce the tip leakage vortex intensity. This paper provides a good practical demonstration of multi-objective fine optimization on the blade shape of a low-pressure axial flow fan.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Abbreviations
- CFD :
-
Computational fluid dynamics
- DOE :
-
Design of experiment
- LE :
-
Leading edge
- LOO :
-
Leave-one-out
- MOGA :
-
Multi-objective genetic algorithm
- PS :
-
Pressure side
- RBF :
-
Radial basis functions
- RMS :
-
Root mean square
- SS :
-
Suction side
- TE :
-
Trailing edge
- TLV :
-
Tip leakage vortex
- c i :
-
Chord length of blade section profile
- D :
-
Blade tip diameter
- p t :
-
Total pressure
- p 1t :
-
Total pressure at the inlet
- p 2s :
-
Static pressure at the outlet
- q v :
-
Volume flow rate
- P :
-
Shaft power
- R :
-
The R-axis of cylindrical coordinates
- x :
-
Design parameter vector
- z :
-
Number of Blades
- Z :
-
The z-axis of cylindrical coordinates
- φ:
-
Flow coefficient
- γ i :
-
Stagger angle of blade section profile
- η ts :
-
Total-to-static efficiency
- θ :
-
The θ-axis of cylindrical coordinates
- Δp ts :
-
Total-to-static pressure rise
References
K. Bamberger and T. Carolus, Design guidelines for low pressure axial fans based on CFD-trained meta-models, 11th European Conference Turbomachinery Fluid Dynamics and Thermodynamics, Spain (2015) 1–12.
S. Castegnaro, Aerodynamic design of low-speed axial-flow fans: a historical overview, Designs, 2(3) (2018) 20.
S. Zhou et al., Optimal design of multi-blade centrifugal fan based on partial coherence analysis, Proceedings of, he Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 236(2) (2022) 894–907.
X. Li, Z. Liu and Y. Zhao, Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 236(7) (2022) 3398–3417.
D. Sakaguchi et al., Global optimization of recirculation flow type casing treatment in centrifugal compressors of turbochargers, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 232(24) (2018) 4461–4471.
J.-H. Kim, J.-H. Choi and K.-Y. Kim, Surrogate modeling for optimization of a centrifugal compressor impeller, International J. of Fluid Machinery and Systems, 3(1) (2010) 29–38.
S. Kim et al., Design optimization for mixed-flow pump impeller by improved suction performance and efficiency with variables of specific speeds, Journal of Mechanical Science and Technology, 34(6) (2020) 2377–2389.
S. Kim et al., Design optimization of mixed-flow pump impellers with various shaft diameters at the same specific speed, Journal of Mechanical Science and Technology, 32(3) (2018) 1171–1180.
B.-J. Lin, C.-I. Hung and E. J. Tang, An optimal design of axial-flow fan blades by the machining method and an artificial neural network, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 216(3) (2002) 367–376.
S. J. Seo, S. M. Choi and K. Y. Kim, Design optimization of a low-speed fan blade with sweep and lean, Proceedings of the Institution of Mechanical Engineers, Part A: J. of Power and Energy, 222(1) (2008) 87–92.
K. Bamberger and T. Carolus, Development, application, and validation of a quick optimization method for the class of axial fans, J. of Turbomachinery, 139(11) (2017) 111001.
P. Song and J. Sun, Blade shape optimization for transonic axial flow fan, Journal of Mechanical Science and Technology, 29(3) (2015) 931–938.
J. Vad, G. Halász and T. Benedek, Efficiency gain of low-speed axial flow rotors due to forward sweep, Proceedings of the Institution of Mechanical Engineers, Part A: J. of Power and Energy, 229(1) (2015) 16–23.
A. Nazmi Ilikan and E. Ayder, Influence of the sweep stacking on the performance of an axial fan, J. Turbomach, 137(6) (2015) 61004.
F. Zenger, A. Lorenz and S. Becker, Experimental investigation of the flow-and sound-field of low-pressure axial fans with different blade stacking strategies, 17th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery (ISROMAC2017), Maui, United States (2017).
L. Zhang, Y.-Z. Jin and Y.-Z. Jin, An investigation on the effects of irregular airfoils on the aerodynamic performance of small axial flow fans, J. Mech. Sci. Technol, 27(6) (2013) 1677–1685.
R. A. Adjei et al., Multidisciplinary design optimization for performance improvement of an axial flow fan using free-form deformation, J. of Turbomachinery, 143 (1) (2020).
P. Song, J. Sun and K. Wang, Blade shape optimization of transonic axial flow fan in terms of sectional profiles and stacking line, Turbo Expo: Power for Lank, Sea, and Air, Düsseldorf, Germany (2014).
W. Yue, Y. Jin and Z. Wen, Multiobjective optimization design for skew and sweep parameters of two-stage blades of axial fan, ISRN Mechanical Engineering, 2013 (2013).
C. Kong 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(11) (2021) 4997–5005.
T. Ding et al., Optimization design of agricultural fans based on skewed-swept blade technology, Applied Engineering in Agriculture, 35(2) (2019) 249–258.
T. H. Carolus and R. Starzmann, An aerodynamic design methodology for low pressure axial fans with integrated airfoil polar prediction, Proceedings of the ASME Turbo Expo 2011, Vancouver, Canada (2011).
T. Carolus, T. Zhu and M. Sturm, A low pressure axial fan for benchmarking prediction methods for aerodynamic performance and sound, Noise Control Engineering J., 63(6) (2015) 537–545.
T. Zhu and T. H. Carolus, Experimental and numerical investigation of the tip clearance noise of an axial fan, Proceedings of ASME Turbo Expo 2013, San Antonio, Texas, USA (2013).
T. Zhu and T. H. Carolus, Experimental and unsteady numerical investigation of the tip clearance noise of an axial fan, ASME 2013 Turbine Blade Tip Symposium, ASME, Hamburg, Germany (2013) V001T04A001.
E. N. Jacobs, K. E. Ward and R. M. Pinkerton, The Characteristics of 78 Related Airfoil Sections from Tests in the Variable-Density Wind Tunnel, Langley Memorial Aeronautical Laboratory (1933).
J. Vad, Forward blade sweep applied to low-speed axial fan rotors of controlled vortex design: an overview, J. Eng. Gas. Turbines Power-Trans. ASME, 135(1) (2013) 012601.
M. Masi and A. Lazzaretto, A simplified theory to justify forward sweep in low hub-to-tip ratio axial fan, ASME Turbo Expo 2015: Turbine Technical Conference and Exposition, Montreal, Quebec, Canada (2015).
M. D. Morris, Factorial sampling plans for preliminary computational experiments, Technometrics, 33(2) (1991) 161–174.
B. M. Adams et al., Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 614 User’s Manual, Sandia National Lab. (SNL-NM) (2021).
A. Bhosekar and M. Ierapetritou, Advances in surrogate based modeling, feasibility analysis, and optimization: A review, Computers and Chemical Engineering, 108 (2018) 250–267.
S. Bagheri, W. Konen and T. Bäck, Comparing Kriging and radial basis function surrogates, Proc. 27. Workshop Computational Intelligence (2017) 243–259.
R. Jin, W. Chen and T. W. Simpson, Comparative studies of metamodelling techniques under multiple modelling criteria, Struct Multidisc Optim, 23(1) (2001) 1–13.
A. Giunta and L. Watson, A comparison of approximation modeling techniques-polynomial versus interpolating models, 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization (1998) 4758.
M. Meckesheimer et al., Computationally inexpensive metamodel assessment strategies, AIAA J., 40(10) (2002) 2053–2060.
E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Swiss Federal Institute of Technology, Switzerland (2001).
K. Deb, An efficient constraint handling method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, 186(2–4) (2000) 311–338.
Acknowledgments
The authors are grateful to Professor Thomas Carolus for providing the geometry model and experimental test data for USI7.
Author information
Authors and Affiliations
Corresponding author
Additional information
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. 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. from Zhejiang University in 1985, and M.S. and Ph.D. in Chemical Engineering from the same university in 1989 and 2000, respectively. His research interests include fluid machinery, computational aided design, and computational aided engineering.
Rights and permissions
About this article
Cite this article
Kong, C., Wang, M., Jin, T. et al. The blade shape optimization of a low-pressure axial fan using the surrogate-based multi-objective optimization method. J Mech Sci Technol 37, 179–189 (2023). https://doi.org/10.1007/s12206-022-1219-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-022-1219-y