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A relative adequacy framework for multimodel management in multidisciplinary design optimization

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Abstract

In previous work, we presented a novel relative adequacy framework to manage the employment of a set of available computational models in (single-disciplinary) design optimization problems. In this paper, we extend our method to solve multidisciplinary design optimization problems with particular emphasis on strongly coupled fluid-structure interactions. We illustrate that these interactions can have a significant impact on multimodel management: models that may be selected in a single-disciplinary analysis context can be inadequate in a multidisciplinary analysis one. We implement our method for two multidisciplinary design optimization architectures: the monolithic multidisciplinary feasible formulation and a penalty-based distributed interdisciplinary feasible formulation. We illustrate the proposed multimodel management methodology by means of two example problems: a flexible beam fluid-structure interaction problem and a transonic fan flow problem. The obtained results demonstrate that our framework is accurate and efficient while exhibiting significant computational cost benefits, especially when disciplinary coupling is tight.

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References

  • Bayoumy A, Kokkolaras M (2019) A relative adequacy framework for multi-model management in design optimization. Journal of Mechanical Design, pages 1–22, ISSN 1050-0472, vol 142

  • Allison J, Kokkolaras M, Papalambros P (2005) On the impact of coupling strength on complex system optimization for single-level formulations. volume: 31st Design Automation Conference. Parts A and B of ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 09:265–275

    Google Scholar 

  • Baptista R, Marzouk Y, Willcox K, Peherstorfer B (2018) Optimal approximations of coupling in multidisciplinary models. AIAA Journal 56(6):2412–2428

    Article  Google Scholar 

  • Simpson T, Martins J (2011) Multidisciplinary design optimization for complex engineered systems: report from a National Science Foundation Workshop. Journal of Mechanical Design 133(10):10. 101002–101002–10, ISSN 1050-0472

    Article  Google Scholar 

  • Allaire D, Willcox K, Toupet O (2010) A bayesian-based approach to multifidelity multidisciplinary design optimization. In: 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. AIAA

  • Geiselhart K, Ozoroski L, Fenbert J, Shields E, Li W (2011) Integration of multifidelity multidisciplinary computer codes for design and analysis of supersonic aircraft. In: 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. AIA

  • Kuya Y, Takeda K, Zhang X, Forrester A (2011) Multifidelity surrogate modeling of experimental and computational aerodynamic data sets. AIAA journal 49(2):289–298

    Article  Google Scholar 

  • Yao W, Chen X, Luo W, van Tooren M, Guo J (2011) Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Progress in Aerospace Sciences 47(6):450–479. ISSN 0376-0421

    Article  Google Scholar 

  • March A (2012) Multifidelity methods for multidisciplinary system design. PhD thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts

  • Ghoman S, Kapania R, Chen P, Sarhaddi D, Lee D (2012) Multifidelity, multistrategy, and multidisciplinary design optimization environment. J Aircr 49(5):1255–1270

    Article  Google Scholar 

  • Christensen D (2012) Multifidelity methods for multidisciplinary design under uncertainty. Master’s thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts

  • Martins J, Lambe A (2013) Multidisciplinary design optimization: a survey of architectures. AIAA J 51(9):2049–2075

    Article  Google Scholar 

  • Joly M, Verstraete T, Paniagua G (2014) Integrated multifidelity, multidisciplinary evolutionary design optimization of counterrotating compressors. Integrated Computer-Aided Engineering 21(3):249–261. ISSN 1875-8835

    Article  Google Scholar 

  • Toal D, Keane A, Benito D, Dixon J, Yang J, Price M, Robinson T, Remouchamps A, Kill N (2014) Multifidelity multidisciplinary whole-engine thermomechanical design optimization. J Propuls Power 30(6):1654–1666

    Article  Google Scholar 

  • Viana FAC, Simpson TW, Balabanov V, Toropov V (2014) Metamodeling in multidisciplinary design optimization: how far have we really come? AIAA J 52(4):670–690

    Article  Google Scholar 

  • Variyar A, Economon T, Alonso J (2016) Multifidelity conceptual design and optimization of strut-braced wing aircraft using physics based methods. In: 54th AIAA Aerospace Sciences Meeting, p 2000

  • Fernández-Godino G, Park C, Nam K, Haftka R (2019) Issues in deciding whether to use multifidelity surrogates. AIAA Journal 57(5):2039–2054

    Article  Google Scholar 

  • Avriel M, Williams A (1970) Complementary geometric programming. SIAM J Appl Math 19(1):125–141

    Article  MathSciNet  MATH  Google Scholar 

  • Morris A (1972) Approximation and complementary geometric programming. SIAM J Appl Math 23(4):527–531

    Article  MathSciNet  MATH  Google Scholar 

  • Braibant V, Fleury C (1985) An approximation-concepts approach to shape optimal design. Computer Methods in Applied Mechanics and Engineering 53(2):119–148. ISSN 0045-7825

    Article  MathSciNet  MATH  Google Scholar 

  • Hajela P (1986) Geometric programming strategies in large-scale structural synthesis. AIAA J 24(7):1173–1178

    Article  MATH  Google Scholar 

  • Schmit L, Farshi B (1974) Some approximation concepts for structural synthesis. AIAA Journal 12(5):692–699

    Article  Google Scholar 

  • Schmit Jr L, Miura H (1976) Approximation concepts for efficient structural synthesis technical report NASA CR-2552

  • Barthelemy J, Haftka R (1993) Approximation concepts for optimum structural design. Structural optimization 5(3):129–144. ISSN 1615-1488

    Article  Google Scholar 

  • Burgee S, Giunta A, Balabanov V, Grossman B, Mason W, Narducci R, Haftka R, Watson L (1996) A coarse-grained parallel variable-complexity multidisciplinary optimization paradigm. The International Journal of Supercomputer Applications and High Performance Computing 10(4):269–299

    Article  Google Scholar 

  • Burgee S, Watson L, Giunta A, Grossman B, Haftka R, Mason W (May 1994) Parallel multipoint variable-complexity approximations for multidisciplinary optimization. In: Proceedings of IEEE Scalable High Performance Computing Conference, pp 734– 740

  • Alexandrov N, Dennis J, Lewis R, Torczon V (1998) A trust-region framework for managing the use of approximation Models in optimization. Structural optimization 15(1):16–23. ISSN 1615-1488

    Article  Google Scholar 

  • Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optim 7(1):1–25

    Article  MathSciNet  MATH  Google Scholar 

  • Zang T, Green L (1998) Multidisciplinary design optimization techniques - implications and opportunities for fluid dynamics research. Technical report, Institute for Computer Applications in Science and Engineering NASA

  • Kennedy M, O’Hagan A (2001) Bayesian calibration of computer models. Journal of The Royal Statistical Society. Series B, Statistical Methodology 63(3):425–464. ISSN 1369- 7412

    Article  MathSciNet  MATH  Google Scholar 

  • Deyst J (2002) The application of estimation theory to managing risk in product developments.. In: Proceedings. The 21st Digital Avionics Systems Conference, volume 1 pages 4A3–4A3

  • Miller S, Yukish M, Simpson T (2017) Design as a sequential decision process. Struct Multidiscip Optim 57:305–324

    Article  MathSciNet  Google Scholar 

  • Unal M, Miller S, Chhabra J, Warn G, Yukish M, Simpson M (2017) A sequential decision process for the system-level design of structural frames. Struct Multidiscip Optim 56:991–1011

    Article  Google Scholar 

  • Panchenko V, Moustapha H, Mah S, Patel K, Dowhan M (2003) Preliminary multi-disciplinary optimization in turbomachinery design. Technical report, Pratt and Whitney Canada Corp Longueuil (Quebec)

  • Griffin M (2010) How do we fix systems engineering?. In: 61st International Astronautical Congress, volume 27, Prague, Czech Republic

  • Rodriguez J, Renaud J, Watson L (1998) Convergence of trust region augmented lagrangian methods using variable fidelity approximation data. Structural optimization 15(3-4):141–156

    Article  Google Scholar 

  • Zadeh PM, Toropov V (2002) Multi-fidelity multidisciplinary design optimization based on collaborative optimization framework. In: 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, p 5504

  • Wang X, Liu Y, Sun W, Song X, Zhang J (2018) Multidisciplinary and multifidelity design optimization of electric vehicle battery thermal management system. Journal of Mechanical Design 140(9):8. 094501

    Article  Google Scholar 

  • Audet C, Dennis J (2006) Mesh adaptive direct search algorithms for constrained optimization. SIAM J Optim 17(1):188–217

    Article  MathSciNet  MATH  Google Scholar 

  • Audet C, Kokkolaras M, Le Digabel S, Talgorn B (2018) Order-based error for managing ensembles of surrogates in mesh adaptive direct search. Journal of Global Optimization 70(3):645–675. ISSN 1573-2916

    Article  MathSciNet  MATH  Google Scholar 

  • Hübner B., Walhorn E, Dinkler D (2004) A monolithic approach to fluid–structure interaction using space–time finite elements. Computer Methods in Applied Mechanics and Engineering 193 (23-26):2087–2104

    Article  MATH  Google Scholar 

  • Sanchez R, Palacios R, Economon T, Kline H, Alonso J, Palacios F (2016) Towards a fluid-structure interaction solver for problems with large deformations within the open-source SU2 suite In 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp 0205

  • Academic Research Mechanical ANSYS (2018) Release 18.1, help system, coupled field analysis guide, ANSYS inc.

  • Tosserams S, Kokkolaras M, Etman L, Rooda J (2010) A nonhierarchical formulation of analytical target cascading. Journal of Mechanical Design 132(5):051002

    Article  Google Scholar 

  • Lambe A, Martins J (2012) Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes. Structural and Multidisciplinary Optimization 46(2):273–284

    Article  MATH  Google Scholar 

  • Cramer E, Dennis J, Frank P, Lewis R, Shubin G (1994) Problem formulation for multidisciplinary optimization. SIAM J Optim 4(4):754–776

    Article  MathSciNet  MATH  Google Scholar 

  • Balling RJ, Sobieszczanski-Sobieski J (1996) Optimization of coupled systems - a critical overview of approaches. AIAA journal 34(1):6–17

    Article  MATH  Google Scholar 

  • Bloebaum C (1995) Coupling strength-based system reduction for complex engineering design. Structural Optimization 10(2):113–121

    Article  Google Scholar 

  • Tosserams S, Etman L, Rooda J (2008) Augmented Lagrangian coordination for distributed optimal design in MDO. Int J Numer Methods Eng 73(13):1885–1910

    Article  MathSciNet  MATH  Google Scholar 

  • Talgorn B, Kokkolaras M (2017) Compact implementation of non-hierarchical analytical target cascading for coordinating distributed multidisciplinary design optimization problems. Structural and Multidisciplinary Optimization 56(6):1–6

    Article  MathSciNet  Google Scholar 

  • Talgorn B (2016) NoHiMDO, A non hierarchical solver for MDO problems, URL https://github.com/bastientalgorn/NoHiMDO

  • Trepanier J, Lupien A, Tribes C, et al. (2017) A 3d parameterization for transonic fan blade multidisciplinary design. Aeronautics and Aerospace Open Access Journal 1(1):31–39

    Article  Google Scholar 

  • A. Strazisar, Wood J, Hathaway M, Suder K (1989) Laser anemometer measurements in a transonic axial-flow fan rotor. Technical report. Nasa Lewis Research Center, Cleveland, OH, United States

    Google Scholar 

  • Samareh J (2001) Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization. AIAA Journal 39(5):877–884

    Article  Google Scholar 

  • Naz S (2014) Multidisciplinary design optimization (MDO) of transonic fan blade. PhD thesis École Polytechnique de montréal

  • Khelghatibana M (2014) An approach for aerodynamic optimization of transonic fan blades. PhD thesis École Polytechnique de montréal

  • Bayoumy A (2020) RAF-TIMDO. URL https://github.com/Ahmed-Bayoumy/RAF-TIMDO/tree/alphahttps://github.com/Ahmed-Bayoumy/RAF-TIMDO/tree/alpha

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Funding

This work has been supported partially by FRQNT grant 2015-PR-182098 and NSERC CRDPJ 513992-17; the authors are grateful for this partial support, which does not constitute an endorsement of the opinions expressed in this paper. The first author is grateful to the Faculty of Engineering at McGill University for its partial support through a McGill Engineering Doctoral Award.

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Correspondence to Michael Kokkolaras.

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Responsible Editor: Nathalie Bartoli

Replication of results

The geometric CAD models, analysis input files, and scripts required to reproduce the numerical examples are available under open-source licenses and are maintained in a version control repository on GitHub (Bayoumy 2020)

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Bayoumy, A.H., Kokkolaras, M. A relative adequacy framework for multimodel management in multidisciplinary design optimization. Struct Multidisc Optim 62, 1701–1720 (2020). https://doi.org/10.1007/s00158-020-02591-7

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  • DOI: https://doi.org/10.1007/s00158-020-02591-7

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