A Data-Based Policy Model for Intelligent Turbine Cascade Design

Li, Z.; Liu, Y.; Yang, J. G.; Wang, H. Q.

doi:10.47176/jafm.15.04.33515

A Data-Based Policy Model for Intelligent Turbine Cascade Design

Document Type : Regular Article

Authors

School of Energy and Power Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian 116024,China

10.47176/jafm.15.04.33515

Abstract

The study of intelligent design methods is becoming a hot topic for the design of turbine cascades. This paper proposes a data-based policy model to achieve intelligent design. To gain a high-quality policy model, empirical equations and "space extending + elitism" are adopted to dynamically optimize database. This guarantees the quality of the model. Compared to traditional optimization design approaches, the proposed method relies on less human experience to design a turbine cascade. Ten different turbine cascades are used to verify this method. Results show that, aerodynamic performance of the cascades redesigned is either the same as or better than that of the traditional cascades. The computing time is reduced by more than one order of magnitude compared to a "CFD + optimization algorithm" or "surrogate model + optimization algorithm" method. With the advantages in computing time and intelligence, the proposed novel method shows the possibility of replacing traditional design methods.

Keywords

References

Anguita, D., C. Cravero, C. Filz and F. Rivieccio (2003). An Innovative Technique for the Aerodynamic Design of Turbine Profiles Using Artificial Intelligence. In 33rd AIAA Fluid Dynamics Conference and Exhibit. Orlando, Florida.##

Aungier, R. H. (2006a). Turbine Aerodynamics: Axial-Flow and Radial-Flow Turbine Design and Analysis. New York, Chap. 6, 133-166.##

Aungier, R. H. (2006b). Turbine Aerodynamics: Axial-Flow and Radial-Flow Turbine Design and Analysis. New York, Chap. 4, 61-92.##

Behr, T. (2007). Control of Rotor Tip Leakage and Secondary Flow by Casing Air Injection in Unshrouded Axial Turbines. Ph.D. Thesis, ETH, Zurich.##

Bryce, J. D., M. R. Litchfield and N. P. Leversuch (1985). The Design, Performance and Analysis of a High Work Capacity Transonic Turbine. Journal of Engineering for Gas Turbines and Power 107(4), 931-937.##

Chatel, A., T. Verstraete and G. Coussement (2019). Multipoint Optimization of an Axial Turbine Cascade Using a Hybrid Algorithm. ASME Paper. No. GT2019-91471.##

Clark, C. J. (2019). A Step Towards an Intelligent Aerodynamic Design Process. ASME Paper. No. GT2019-91637.##

Colclough, C. D. (1966a). Design of Turbine Blades Suitable for Supersonic Relative Inlet Velocities and the Investigation of Their Performance in Cascades: Part I—Theory and Design. ARCHIVE Journal of Mechanical Engineering Science 1959-1982 (1-23) 8(1), 110–128. ##

Colclough, C. D. (1966b). Design of Turbine Blades Suitable for Supersonic Relative Inlet Velocities and the Investigation of Their Performance in Cascades: Part II—Experiments, Results and Discussion. ARCHIVE Journal of Mechanical Engineering Science 1959-1982 (1-23) 8(2), 185–197. ##

Deb, K., A. Pratap, S. Agarwal and T. Meyarivan (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE transactions on evolutionary computation 6(2), 182–197. ##

Deb, K. and H. Jain (2013). An Evolutionary Many-objective Optimization Algorithm Using Reference-point-based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE transactions on evolutionary computation 18(4), 577-601.##

Drela, M. and M. B. Giles (1987). Viscous-inviscid Analysis of Transonic and Low Reynolds Number Airfoils. AIAA Journal 25(10), 1347–1355. ##

Giel, P. W. (2008). NASA/GE Highly-loaded Turbine Research Program. AIAA Turbine Engine Testing Working Group Meeting, Cleveland, Ohio.##

Hodson, H. P. and R. G. Dominy (1987). The Off-Design Performance of a Low-Pressure Turbine Cascade. Journal of Turbomachinery 109(2), 201-209.##

Jain, H. and K. Deb (2013). An Evolutionary Many-objective Optimization Algorithm Using Reference-point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach. IEEE Transactions on Evolutionary Computation 18(4), 602-622.##

Kacker, S. C. and U. Okapuu (1982). A Mean Line Prediction Method for Axial Flow Turbine Efficiency. Journal of Engineering for Power 104(1), 111–119. ##

Kiock, R., F. Lehthaus, N. C. Baines and C. H. Sieverding (1986). The Transonic Flow Through a Plane Turbine Cascade as Measured in Four European Wind Tunnels. Journal of Engineering for Gas Turbines and Power 108(2), 277–284. ##

Klambauer, G., T. Unterthiner, A. Mayr and S. Hochreiter (2017). Self-Normalizing Neural Networks. In Advances in Neural Information Processing Systems (NIPS). arXiv: 1706.02515##

Kosowski, K., K. Tucki and A. Kosowski (2009). Turbine stage design aided by artificial intelligence methods. Expert Systems with Applications 36(9), 11536-11542.##

Kosowski, K., K. Tucki and A. Kosowski (2010). Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades. Journal of Turbomachinery 132(1), 014501. ##

Lee, S., S. Lee., K. Kim., D. Lee., Y. Kang and D. Rhee (2014). Optimization Framework Using Surrogate Model for Aerodynamically Improved 3D Turbine Blade Design. ASME Paper. No. GT2014-26571.##

Li, Z. H. and X. Q. Zheng (2017). Review of Design Optimization Methods for Turbomachinery Aerodynamics. Progress in Aerospace Sciences 93, 1-23.##

Meng, F. S., J. Gao, Q. Zheng, W. L. Fu and X. Z. Liu (2019). Experimental Study on Large Meridional Expansion Annular Sector Cascades with Variable Working Conditions. Journal of Propulsion Technology 40(5), 986-995.##

Moroz, L., Y. Govoruschenko, L. Romanenko and P. Pagur (2004). Methods and Tools for Multidisciplinary Optimization of Axial Turbine Stages with Relatively Long Blades. ASME Paper. No. GT2004-53379.##

Pierret, S. and R. A. Van den Braembussche (1999). Turbomachinery Blade Design Using a Navier–Stokes Solver and Artificial Neural Network. Journal of Turbomachinery 121(2), 326–332. ##

Pritchard, L. J. (1985). An Eleven Parameter Axial Turbine Airfoil Geometry Model. ASME Paper. No. 85-GT-219.##

Shelton, M. L., B. A. Gregory, S. H. Lamson, H. L. Moses, R. L. Doughty and T. Kiss (1993). Optimization of a Transonic Turbine Airfoil Using Artificial Intelligence CFD and Cascade Testing. ASME Paper. No. 93-GT-161.##

Sonoda, T., T. Arima, M. Olhofer, B. Sendhoff, F. Kost and P. A. Giess (2006). A Study of Advanced High-loaded Transonic Turbine Airfoils. Journal of Turbomachinery. 128(4), 650-657.##

Thulin, R. D., D. C. Howe and I. D. Singer (1982). Energy Efficient Engine High-pressure Turbine Detailed Design Report. NASA Report. NASA-CR-165608.##

Tsujita, H. and M. Kaneko (2019). Profile Loss of Ultra-Highly Loaded Turbine Cascade at Transonic Flow Condition. ASME Paper. No. GT-2019-91264.##

Waesker. M., B. Buelten., N. Kienzle and C. Doetsch (2020). Optimization of Supersonic Axial Turbine Blades Based on Surrogate Models. ASME Paper. No. GT2020-14465##

Youngren, H. H. (1991). Analysis and Design of Transonic Cascades with Splitter Vanes. Ph.D. thesis, Massachusettes Institute of Technology, Cambridge, US.##