An evaluation of constraint aggregation strategies for wing box mass minimization
- 444 Downloads
Constraint aggregation makes it feasible to solve large-scale stress-constrained mass minimization problems efficiently using gradient-based optimization where the gradients are computed using adjoint methods. However, it is not always clear which constraint aggregation method is more effective, and which values to use for the aggregation parameters. In this work, the accuracy and efficiency of several aggregation methods are compared for an aircraft wing design problem. The effect of the type of aggregation function, the number of constraints, and the value of the aggregation parameter are studied. Recommendations are provided for selecting a constraint aggregation scheme that balances computational effort with the accuracy of the computed optimal design. Using the recommended aggregation method and associated parameters, a mass of within 0.5 % of the true optimal design was obtained.
KeywordsStructural optimization Constraint aggregation Stress constraints Kreisselmeier– Steinhauser function Induced aggregation
The authors would like to thank Gaetan K. W. Kenway for his assistance in setting up the CRM wing geometry used in this paper. Computations were performed on the GPC supercomputer at the SciNet HPC Consortium. SciNet is funded by: the Canada Foundation for Innovation under the auspices of Compute Canada; the Government of Ontario; and the University of Toronto.
- Duysinx P, Sigmund O (1998) New developments in handling stress constraints in optimal material distribution. In: Proceedings of the 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, vol 1Google Scholar
- Kennedy G J, Martins J R R A (2014a) A parallel finite-element framework for large-scale gradient-based design optimization of high-performance structures. Finite Elements in Analysis and Design 87:56–73. doi: 10.1016/j.finel.2014.04.011
- Kennedy GJ, Martins JRRA (2014b) A parallel aerostructural optimization framework for aircraft design studies. Struct Multidiscip Optim 50(6):1079–1101. doi: 10.1007/s00158-014-1108-9
- Kenway G K W, Kennedy G J, Martins J R R A (2010) A CAD-Free Approach to High-Fidelity Aerostructural Optimization. In: 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Fort Worth,TX. doi: 10.2514/6.2010-9231
- Kenway G K W, Kennedy G J, Martins J R R A (2014a) Aerostructural optimization of the Common Research Model configuration. In: 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. doi: 10.2514/6.2014-3274, Atlanta
- Kenway G K W, Kennedy G J, Martins J R R A (2014b) Scalable parallel approach for high-fidelity steady-state aeroelastic analysis and adjoint derivative computations. AIAA J 52(5):935–951. doi: 10.2514/1.J052255
- Kreisselmeier G, Steinhauser R (1979) Systematic Control Design by Optimizing a Vector Performance Indicator. In: Symposium on Computer-Aided Design of Control Systems, IFAC, Zurich, Switzerland, pp 113-117Google Scholar
- Vassberg J C, DeHaan M A, Rivers S M, Wahls R A (2008) Development of a Common Research Model for applied CFD validation studies. AIAA:2008–6919Google Scholar
- van der Weide E, Kalitzin G, Schluter J, Alonso J (2006) Unsteady Turbomachinery Computations Using Massively Parallel Platforms. In: 44th AIAA Aerospace Sciences Meeting and Exhibit, Aerospace Sciences Meetings, American Institute of Aeronautics and Astronautics. doi: 10.2514/6.2006-421
- Wrenn G A (1989) An Indirect Method for Numerical Optimization Using the Kreisselmeier–Steinhauser Function. Tech. rep. NASA Langley Research Center, Hampton, VAGoogle Scholar