Implicit constraint handling for shape optimisation with POD-morphing
DOI:
https://doi.org/10.13052/17797179.2012.719316Keywords:
model reduction, optimisation, diffuse approximation, PODAbstract
In the former paper, we have introduced an original morphing approach based on Proper Orthogonal Decomposition (POD) of shapes, designed to replace parametrized CAD models in structural optimization. Here, we expand the method to interpolate exclusively between admissible instances of structural shapes, thus permitting a global understanding of the design domain and also reducing the size of the optimisation problem. The result is a bilevel reparametrization approach for structural geometries based on Diffuse Approximation in a properly chosen locally linearized space, and the geometric parameters are replaced with the smallest set of variables needed to represent a manifold of admissible shapes for a chosen precision. We demonstrate the approach in a typical shape optimisation problem.
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References
Allaire, G., DeGournay, F., Jouve, F., & Toader, A.M. (2005). Structural optimization using topological
and shape sensitivity via a level set method. Control & Cybernetics, 34, 59–80.
Berkooz, G., Holmes, P., & Lumley, J.L. (1993). The proper orthogonal decomposition in the analysis
of turbulent flows. Annual Review of Fluid Mechanics, 25(1), 539–575.
Bregler, C., & Omohundro, S.M. (1995). Nonlinear image interpolation using manifold learning. In G.
Tesauro, D.S. Touretzky, and T.K. Leen (Eds.), Advances in Neural Information Processing Systems
(pp. 973–980). Cambridge, MA: MIT Press.
Breitkopf, P. (1998). An algorithm for construction of iso-valued surfaces for finite elements. Engineering
with Computers, 14(2), 146–149.
Breitkopf, P., Naceur, H., Rassineux, A., & Villon, P. (2005). Moving least squares response surface
approximation: Formulation and metal forming applications. Computers and Structures, 83(17–18),
–1428.
Bui-Thanh, T., Willcox, K., Ghattas, O., & van Bloemen, W.B. (2007). Goal-oriented, model-constrained
optimization for reduction of large-scale systems. Journal of Computational Physics, 224(2),
–896.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 8(6), 679–698.
Carlberg, K., & Farhat, C. (2010). A low-cost, goal-oriented compact proper orthogonal decomposition
basis for model reduction of static systems. International Journal for Numerical Methods in Engineering,
(3), 381–402.
Carlberg K., & Farhat C. (2008). A compact proper orthogonal decomposition basis for optimizationoriented
reduced-order models. 12th AIAA/ISSMO multidisciplinary analysis and optimization
conference, Victoria, Canada, 10–12, 2008.
Chappuis, C., Rassineux, A., Breitkopf, P., & Villon, P. (2004). Improving surface remeshing by feature
recognition. Engineering with Computers, 20(3), 202–209.
Chatterjee, A. (2005). An introduction to the proper orthogonal decomposition. Current Science, Special
Section: Computational Science, 78(7), 808–817.
Coelho, R.F., Breitkopf, P., & Knopf-Lenoir, C. (2009). Bi-level model reduction for coupled problems.
Structural and Multidisciplinary Optimization, 39(4), 401–418.
Cordier, L., El Majd, B.A., & Favier, J. (2010). Calibration of POD reduced order models using Tikhonov
regularization. International Journal for Numerical Methods in Fluids, 63(2), 269–296.
Couplet, M., Basdevant, C., & Sagaut, P. (2005). Calibrated reduced-order POD-Galerkin system for
fluid flow modeling. Journal of Computational Physics, 207(1), 192–220.
Dulong, J.-L., Druesne, F., & Villon, P. (2007). A model reduction approach for real-time part deformation
with nonlinear mechanical behavior. International Journal on Interactive Design and Manufacturing,
(4), 229–238.
Kaufman, A., Cohen, D., & Yagel, R. (1993). Volume graphics. IEEE, Computer, 26(7), 51–64.
Larsson, F., Diez, P., & Huerta, A. (2010). A flux-free a posteriori error estimator for the incompressible
Stokes problem using a mixed FE formulation. AIAA Journal, 199(37–40), 2383–2402.
Launder, B.E., & Spalding, D.B. (1974). The numerical computation of turbulent flows. Computer Methods
in Applied Mechanics and Engineering, 3(2), 269–289.
Nayroles, B., Touzot, G., & Villon, P. (1992). Generalizing the finite element method: Diffuse approximation
and diffuse elements. Computational Mechanics, 10(5), 307–318.
Raghavan, B., & Breitkopf, P. (2012). Asynchronous evolutionary shape optimization based on highquality
surrogates: application to an air-conditioning duct. Engineering with Computers.
Raghavan, B., Breitkopf, P., & Villon, P. (2011). POD-morphing: an a posteriori reparametrization
approach for shape optimization. European Journal of Computational Mechanics, 19(5–7), 673–699.
Ravindran, S.S. (2000). A reduced-order approach for optimal control of fluids using proper orthogonal
decomposition. International Journal for Numerical Methods in Fluids, 34(5), 425–448.
Sofia, A.Y.N., Meguid, S.A., & Tan, K.T. (2010). Shape morphing of aircraft wing: Status and challenges.
Materials & Design, 31(3), 1284–1292.
Willcox, K., & Peraire, J. (2002). Balanced model reduction via the proper orthogonal decomposition.
AIAA Journal, 40(11), 2323–2330.