Optimization and Engineering

, Volume 5, Issue 2, pp 235–262

Optimal Aeroacoustic Shape Design Using the Surrogate Management Framework

  • Alison L. Marsden
  • Meng Wang
  • John E. DennisJr.
  • Parviz Moin
Article

DOI: 10.1023/B:OPTE.0000033376.89159.65

Cite this article as:
Marsden, A.L., Wang, M., Dennis, J.E. et al. Optimization and Engineering (2004) 5: 235. doi:10.1023/B:OPTE.0000033376.89159.65

Abstract

Shape optimization is applied to time-dependent trailing-edge flow in order to minimize aerodynamic noise. Optimization is performed using the surrogate management framework (SMF), a non-gradient based pattern search method chosen for its efficiency and rigorous convergence properties. Using SMF, design space exploration is performed not with the expensive actual function but with an inexpensive surrogate function. The use of a polling step in the SMF guarantees that the algorithm generates a convergent subsequence of mesh points in the parameter space. Each term of this subsequence is a weak local minimizer of the cost function on the mesh in a sense to be made precise later. We will discuss necessary optimality conditions for the design problem that are satisfied by the limit of this subsequence. Results are presented for an unsteady laminar flow past an acoustically compact airfoil. Constraints on lift and drag are handled within SMF by applying the filter pattern search method of Audet and Dennis, within which a penalty function is used to form and optimize a surrogate function. Optimal shapes that minimize noise have been identified for the trailing-edge problem in constrained and unconstrained cases. Results show a significant reduction (as much as 80%) in acoustic power with reasonable computational cost using several shape parameters. Physical mechanisms for noise reduction are discussed.

surrogate optimization aeroacoustics pattern search methods optimal shape design 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Alison L. Marsden
    • 1
  • Meng Wang
    • 2
  • John E. DennisJr.
    • 3
  • Parviz Moin
    • 1
  1. 1.Mechanical Engineering DepartmentStanford UniversityUSA
  2. 2.Center for Turbulence ResearchStanford UniversityUSA
  3. 3.Department of Computational and Applied MathematicsRice UniversityUSA

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