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Multiobjective Design Optimization of Merging Configuration for an Exhaust Manifold of a Car Engine

  • Masahiro Kanazaki
  • Masashi Morikaw
  • Shigeru Obayashi
  • Kazuhiro Nakahashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

Multiobjective design optimization system of exhaust manifold shapes for a car engine has been developed using Divided Range Multiobjective Genetic Algorithm (DRMOGA) to obtain more engine power as well as to achieve less environmental impact. The three-dimensional manifold shapes are evaluated by the unstructured, unsteady Euler code coupled with the empirical engine cycle simulation code. This automated design system using DRMOGA was confirmed to find Pareto solutions for the highly nonlinear problems.

Keywords

Pareto Front Multiobjective Optimization Pareto Solution Automate Design System Exhaust Manifold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    M. Kanazaki, S. Obayashi and K. Nakahashi, “The Design Optimization of Intake/Exhaust Performance of a Car Engine Using MOGA,” EUROGEN 2001, Athens, Sep. 19–21, 2001, postproceedings in print.Google Scholar
  2. 2.
    T. Hiroyasu, M. Miki and S. Watanabe, “The New Model of Parallel Genetic Algorithm in Multi-Objective Optimization Problems (Divided Range Multi-Objective Genetic Algorithm),” IEEE Proceedings of the Congress on Evolutionary Computation 2000, Vol. 1, pp.333–340, 2000.Google Scholar
  3. 3.
    D. Sharov, and K. Nakahashi, “Reordering of 3-D Hybrid Unstructured Grids for Lower-Upper Symmetric Gauss-Seidel Computations,” AIAA J., Vol. 36, No. 3, pp. 484–486, 1998.CrossRefGoogle Scholar
  4. 4.
    K. Ohnishi, H. Nobumoto, T. Ohsumi and M. Hitomi, “Development of Prediction Technology of Intake and Exhaust System Performance Using Computer Simulation,” MAZDA Technical Paper (in Japanese), No. 6, 1988.Google Scholar
  5. 5.
    L. J. Eshelman and J. D. Schaffer, “Real-coded genetic algorithms and interval schemata,” Foundations of Genetic Algorithms2, Morgan Kaufmann Publishers, Inc., San Mateo, pp. 187–202, 1993.Google Scholar
  6. 6.
    C. M. Fonseca and P. J. Fleming, “Genetic algorithms for multiobjective optimization: formulation, discussion and generalization,” 5th International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, San Francisco, pp. 416–423, 1993.Google Scholar
  7. 7.
    K. A. De Jong, “An Analysis of the Behavior of a Class of Genetic Adaptive System,” Doctoral Dissertation, University of Michigan, Ann Arbor, 1975.Google Scholar
  8. 8.
    Y. Ito and K. Nakahashi, “Direct Surface Triangulation Using Stereolithography (STL) Data,” AIAA Paper 2000-0924, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Masahiro Kanazaki
    • 1
  • Masashi Morikaw
    • 2
  • Shigeru Obayashi
    • 1
  • Kazuhiro Nakahashi
    • 2
  1. 1.Institute of Fluid ScienceTohoku UniversitySendaiJapan
  2. 2.Dept. of Aeronautics and Space EngineeringTohoku UniversitySendaiJapan

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