Development of Real-time Aerodynamic Design System

Abstract

eloped. The new system makes possible real-time computation of the aerodynamic performance and shape sensitivity of passenger cars. The system is made up of technological elements including approximation modeling, categorizing, extraction of feature values, sensitivity analysis and automatic database update. It realizes real-time computation of aerodynamic performance through the combination of approximation modeling and categorizing technologies, computation of shape sensitivity by means of on-line sensitivity calculations, and continuous increase in accuracy by means of automatic learning.Computational results for Computational Fluid Dynamics (CFD) and the real-time aerodynamic design system were compared in order to evaluate the performance of the system, verifying its effectiveness and ability to reduce computation time.

References

  1. (1) Sederberg, T.W., Parry, S.R.: Free-Form Deformation of Solid Geometric Models, Proceedings of ACM SIGGRAPH in Computers & Graphics, 20 (4), p. 151-160 (1986)
  2. (2) Tang, B.: Orthogonal Array-Based Latin Hypercubes, Journal of the American Statistical Association, Vol. 88 (424), p. 1392-1397 (1993)
  3. (3) Forrester, A., Keane A., Sobester, A.: Engineering Design via Surrogate Modelling: A Practical Guide, Wiley, 228p. (2008)
  4. (4) Sasena, M.J.: Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, Doctoral paper of University of Michigan (2002)
  5. (5) Yamashita, O., Sato, M., Yoshioka, T., Tong, F., Kamitani, Y.: Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns, Neuroimage, Vol. 42, Issue 4, p. 1414-1429 (2008)
  6. (6) Kohonen, T.: Self-Organizing Maps, Springer, 501p. (2001)
  7. (7) MacQueen, J. B.: Some Methods for Classification and Analysis of Multivariate Observations, Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, MR0214227, p. 281–297 (1967)
  8. (8) Hopfield, J. J.: Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA, Vol. 79, p. 2554-2558 (1982)
  9. (9) Cortes, C., Vapnik, V.: Support-Vector Networks, Machine Learning, Vol. 20, Issue 3, p. 273-297 (1995)
  10. (10) Sakoe, H., Chiba, S.: Douteki keikaku hou wo riyoushita onsei no jikan seikika ni motozuku renzoku tango ninshiki, Nihon onkyo gakkaishi, Vol. 27, No. 9, p. 483-490 (1971)
  11. (11) Sakoe, H., Chiba, S.: A Dynamic Programming Algorithm Optimization for Spoken Word Recognition, IEEE Trans. on Acoust. Speech and Signal Proc., Vol. 26, Issue 1, p. 43-49 (1978)
  12. (12) Sakoe, H.: Two-level DP-matching- -A dynamic programming-based pattern matching algorithm for connected word recognition, IEEE Trans. on Acoust. Speech and Signal Proc., Vol. 27, No. 6, p. 588-595 (1979)
  13. (13) Ascough II, J. C., Green, T. R., Ma, L., Ahjua, L. R.: Key criteria and selection of sensitivity analysis methods applied to natural resource models, International Congress on Modeling and Simulation Proceedings (2005)
  14. (14) Othmer, C.: CFD Topology and Shape Optimization with Adjoint Methods, VDI-Berichte, 1967, p. 61 (2006)
  15. (15) Graening, L., Menzel, S., Hasenjaeger, M., Bihrer, T., Olhofer, M., Sendhoff, B.: Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries, Journal of Mathematical Modelling and Algorithms, Vol. 7, Issue 4, p. 329-350 (2008)

Author (organization or company)

Publication Information

Article of Honda R&D Technical Review Vol.24 No.2 (107 - 116)

PDF

PDF 1.1MB

Citation Information

Takahiro FUKUSHIGE, Tokitomo ARIYOSHI, Yusuke UDA, Yuki OKUMA, Tatsuya OKABE, Koichi YAMAMOTO, Lars GRAENING, Stefan MENZEL, Markus OLHOFER.
Development of Real-time Aerodynamic Design System
Honda R&D Technical Review Vol.24 No.2 pp.(107 - 116)2012.
Copy citation information
Copied