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Design Optimization

In the past we have developed an efficient approach for computationally expensive multi-objective design optimization problems. In this approach we bring together design of experiment, a response surface model, a genetic algorithm and computational fluid dynamics analysis tools to provide an integrated optimization system. We use an improved hypercube sampling to preselect an array of design points on which the computational fluid dynamics code will run. Then a computationally cheap surrogate model is constructed based on response surface approximation. A real-coded genetic algorithm is then applied on the surrogate model to perform multi-objective optimization. Representative solutions are chosen from the Pareto-optimal front to verify against the computational fluid dynamics code.

 

This proposed method have been used in the redesign of a single-stage turbo pump, a two-stage turbo pump, and the NASA rotor67 transonic compressor blade. For the rotor67 compressor blade design, we can increase the pressure ratio by 1.8% and reduce the weight by 5%. We achieve these with a much reduced computational cost.

 

 NASA Rotor67 Compressor          Computational Grid 

           Rotor67 compressor                                       Computational grid

 

           Comparison of streamlines over the baseline and improved design

            Comparison of streamlines before and after design

 

References

  • Lian, Y., and Liou, M-S., "Aero-Structural Optimization of a Transonic Compressor Rotor," Journal of Propulsion and Power, Vol. 22, No. 4, 2006, pp. 880-888.
  • Lian, Y., and Kim, N-H., "Reliability-based Design Optimization of a Transonic Compressor," AIAA Journal, Vol. 44, No. 2, 2006, pp. 368-375.
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