International Scientific Journal

Authors of this Paper

External Links


Blade is one of the core components of turbine machinery. The reliability of blade is directly related to the normal operation of plant unit. However, with the increase of blade length and flow rate, non-linear effects such as finite deformation must be considered in strength computation to guarantee enough accuracy. Parallel computation is adopted to improve the efficiency of classical nonlinear finite element method and shorten the blade design period. So it is of extraordinary importance for engineering practice. In this paper, the dynamic partial differential equations and the finite element method forms for turbine blades under centrifugal load and flow load are given firstly. Then, according to the characteristics of turbine blade model, the classical method is optimized based on central processing unit + graphics processing unit heterogeneous parallel computation. Finally, the numerical experiment validations are performed. The computation speed of the algorithm proposed in this paper is compared with the speed of ANSYS. For the rectangle plate model with mesh number of 10 k to 4000 k, a maximum speed-up of 4.31 can be obtained. For the real blade-rim model with mesh number of 500 k, the speed-up of 4.54 times can be obtained.
PAPER REVISED: 2015-12-20
PAPER ACCEPTED: 2015-12-25
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2016, VOLUME 20, ISSUE Supplement 3, PAGES [S823 - S831]
  1. Zhisong, F., et al., Architecting the Finite Element Method Pipeline for the GPU, Journal of Computational and Applied Mathematics, 257 (2014), Feb., pp. 195-211
  2. Karatarakis, A., et al., GPU-Acceleration of Stiffness Matrix Calculation and Efficient Initialization of EFG Meshless Methods, Computer Methods in Applied Mechanics and Engineering, 199 (2010), May, pp. 3305-3314
  3. Ren, L., et al., Sparse LU Factorization for Parallel Circuit Simulation on GPU, IEEE, Proceedings, Design Automation Conference, San Francisco, Cal., USA, 2012
  4. Liu, L., et al., A Highly Efficient GPU-CPU Hybrid Parallel Implementation of Sparse LU Factorization, Chinese Journal of Electronics, 20 (2012), 1, pp. 7-12
  5. Capiez-Lernout, C., et al., Geometric Non-Linear Dynamic Analysis of Uncertain Structures with Cyclic Symmetry - Application to a Mistuned Industrial Bladed Disk, Proceedings, International Conference on Uncertainty in Structural Dynamics, Leuven, Belgium, 2014
  6. Chew, K., et al., Structural Optimization and Parametric Study of Offshore wind Turbine Jacket Substructure, Proceedings, 23rd International Offshore and Polar Engineering Conference, Anchorage, Alas., USA, 2013, ISOPE-I-13-010
  7. Smith, I. M., et al., Programming the Finite Element Method, 3rd ed., Beijing Electronic Industry, Beijing, 2005
  8. Xie, Y. H., The Study for Fatigue Failure Life Estimation and Design Analysis System of Steam Turbine Blade, Xi'an Jiaotong University, Xi'an, China, 1997
  9. Rene, B., et al., Non-Linear Finite Element Analysis of Solids and Structures, 2nd ed., John Wiley & Sons, New York, USA, 2012
  10. Ted, B., et al., Non-Linear Finite Elements for Continua and Structures, John Wiley & Sons Ltd., Chichester, UK, 2000
  11. Thomas, J. R., The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, Dover, Mineola, N.Y., USA, 2000
  12. Thomas, R., et al., Parallel Programming For Multicore and Cluster Systems, Springer, New York, USA, 2010
  13. Shen, L., Shusen Z., Study on Substructure Method for Torsional Vibration of a Branch Shafting System, Journal of Vibration and Shock, 26 (2007), 10, pp. 148-151

© 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence