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2020 | OriginalPaper | Buchkapitel

7. Uncertainty-Based Multidisciplinary Design Optimization (UMDO)

verfasst von : Loïc Brevault, Mathieu Balesdent

Erschienen in: Aerospace System Analysis and Optimization in Uncertainty

Verlag: Springer International Publishing

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Abstract

This chapter is devoted to the description of the MDO formulations in the presence of uncertainty. In Chapter 1, deterministic MDO formulations have been introduced, highlighting the interest of such methodologies to solve complex and multidisciplinary design problems. Uncertainty-based multidisciplinary design optimization (UMDO) deals with the presence of uncertainty in MDO problems. The understanding of the importance of UMDO is spreading among academia and industry quickly. Nevertheless, in comparison with the deterministic MDO approaches, the UMDO methodologies are still in the early stages of development and numerous challenges have still to be solved. In the last decades, important improvements have been made in this field of research and are presented in this chapter. UMDO problems combine the challenges of deterministic MDO (organization of the design process, control of interdisciplinary couplings, etc.) and the difficulties involved by uncertainty propagation for multi-physics problems. Most of the existing UMDO formulations are built on the uncertainty propagation techniques dedicated to multidisciplinary problems presented in Chapter 6. The algorithms used to solve these UMDO problems are not discussed in this chapter, but all the presented optimization techniques in Chapter 5 may be used to solve UMDO problems.

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Vorheriges Kapitel Uncertainty Propagation for Multidisciplinary Problems

Nächstes Kapitel Multi-Fidelity for MDO Using Gaussian Processes

Agarwal, H., Renaud, J., Lee, J., and Watson, L. (2004a). A unilevel method for reliability based design optimization. In 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, Palm Springs, CA, USA.

Agarwal, H., Renaud, J. E., Preston, E. L., and Padmanabhan, D. (2004b). Uncertainty quantification using evidence theory in multidisciplinary design optimization. Reliability Engineering & System Safety, 85(1–3):281–294.

Ahn, J. and Kwon, J.-H. (2006). An efficient strategy for reliability-based multidisciplinary design optimization using bliss. Structural and Multidisciplinary Optimization, 31(5):363–372.

Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika, 60(2):255–265.MathSciNetMATH

Balesdent, M., Bérend, N., and Dépincé, P. (2013). New multidisciplinary design optimization approaches for launch vehicle design. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 227(10):1545–1555.

Ball, K. et al. (1997). An elementary introduction to modern convex geometry. Flavors of geometry, 31:1–58.MathSciNetMATH

Baudoui, V. (2012). Optimisation robuste multiobjectifs par modèles de substitution (in French). PhD thesis, Institut Supérieur de l’Aéronautique et de l’Espace, Toulouse.

Baudoui, V., Klotz, P., Hiriart-Urruty, J.-B., Jan, S., and Morel, F. (2012). LOcal Uncertainty Processing (LOUP) method for multidisciplinary robust design optimization. Structural and Multidisciplinary Optimization, 46(5):711–726.

Braun, R. D., Moore, A. A., and Kroo, I. M. (1997). Collaborative approach to launch vehicle design. Journal of spacecraft and rockets, 34(4):478–486.

Brevault, L. (2015). Contributions to multidisciplinary design optimization under uncertainty, application to launch vehicle design. PhD thesis, Ecole Nationale Supérieure des Mines de Saint-Etienne.

Brevault, L., Balesdent, M., Bérend, N., and Le Riche, R. (2015a). Decoupled multidisciplinary design optimization formulation for interdisciplinary coupling satisfaction under uncertainty. AIAA Journal, 54(1):186–205.

Brevault, L., Balesdent, M., Bérend, N., and Le Riche, R. (2015b). Multi-level hierarchical MDO formulation with functional coupling satisfaction under uncertainty, application to sounding rocket design. In 11th World Congress on Structural and Multidisciplinary Optimization, Sydney, Australia.

Chen, X., Hasselman, T., Neill, D., Chen, X., Hasselman, T., and Neill, D. (1997). Reliability based structural design optimization for practical applications. In 38th AIAA Structures, structural dynamics, and materials conference, Kissimmee, FL, USA.

Chiralaksanakul, A. and Mahadevan, S. (2007). Decoupled approach to multidisciplinary design optimization under uncertainty. Optimization and Engineering, 8(1):21–42.MathSciNetMATH

Cho, S.-g., Jang, J., Kim, S., Park, S., Lee, T. H., Lee, M., Choi, J.-S., Kim, H.-W., and Hong, S. (2016). Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data. Structural and Multidisciplinary Optimization, 54(6):1671–1688.

Dempster, A. P. (1967). Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika, 54(3–4):515–528.MathSciNet

Devolder, O., Glineur, F., and Nesterov, Y. (2010). Solving infinite-dimensional optimization problems by polynomial approximation. In Recent Advances in Optimization and its Applications in Engineering, pages 31–40. Springer.

Du, X. (2002). Efficient methods for engineering design under uncertainty. PhD thesis, Graduate College, University of Illinois at Chicago.

Du, X. and Chen, W. (2001). A hierarchical approach to collaborative multidisciplinary robust design. In 4th Congress of Structural and Multidisciplinary Optimization, Dalin, China.

Du, X. and Chen, W. (2002). Efficient uncertainty analysis methods for multidisciplinary robust design. AIAA Journal, 40(3):545–552.

Du, X., Guo, J., and Beeram, H. (2008). Sequential optimization and reliability assessment for multidisciplinary systems design. Structural and Multidisciplinary Optimization, 35(2):117–130.MathSciNetMATH

Dubourg, V., Sudret, B., and Deheeger, F. (2013). Metamodel-based importance sampling for structural reliability analysis. Probabilistic Engineering Mechanics, 33:47–57.

Dubreuil, S., Bartoli, N., Gogu, C., and Lefebvre, T. (2016). Propagation of modeling uncertainty by polynomial chaos expansion in multidisciplinary analysis. Journal of Mechanical Design, 138(11):111411.

Duchon, J. (1977). Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In Constructive theory of functions of several variables, pages 85–100. Springer.

Eldred, M. (2009). Recent advances in non-intrusive polynomial chaos and stochastic collocation methods for uncertainty analysis and design. In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Palm Springs, CA, USA.

Ghosh, S., Lee, C., and Mavris, D. N. (2014). Covariance matching collaborative optimization for uncertainty-based multidisciplinary aircraft design. In 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Atlanta, GA, USA.

Giassi, A., Bennis, F., and Maisonneuve, J.-J. (2004). Multidisciplinary design optimisation and robust design approaches applied to concurrent design. Structural and Multidisciplinary Optimization, 28(5):356–371.

Gu, X. S., Renaud, J. E., and Penninger, C. L. (2006). Implicit uncertainty propagation for robust collaborative optimization. Journal of Mechanical Design, 128(4):1001–1013.

Guoqiang, C., Jianping, T., and Yourui, T. (2018). A reliability-based multidisciplinary design optimization method with evidence theory and probability theory. International Journal of Reliability, Quality and Safety Engineering, 25(01):1850003.

Haldar, A. and Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design, volume 1. Wiley New York.

Hansen, N. and Ostermeier, A. (1996). Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In 1996 IEEE International Conference on Evolutionary Computation (CEC), Nagoya, Japan.

Hirmajer, T., Balsa-Canto, E., and Banga, J. R. (2010). Mixed-integer non-linear optimal control in systems biology and biotechnology: numerical methods and a software toolbox. IFAC Proceedings Volumes, 43(5):314–319.

Hu, W., Azarm, S., and Almansoori, A. (2013). New approximation assisted multi-objective collaborative robust optimization (new AA-McRO) under interval uncertainty. Structural and Multidisciplinary Optimization, 47(1):19–35.MathSciNetMATH

Hu, X., Parks, G. T., Chen, X., and Seshadri, P. (2016). Discovering a one-dimensional active subspace to quantify multidisciplinary uncertainty in satellite system design. Advances in Space Research, 57(5):1268–1279.

Huang, H.-Z., Yu, H., Zhang, X., Zeng, S., and Wang, Z. (2010). Collaborative optimization with inverse reliability for multidisciplinary systems uncertainty analysis. Engineering Optimization, 42(8):763–773.MathSciNet

Jaeger, L., Gogu, C., Segonds, S., and Bes, C. (2013). Aircraft multidisciplinary design optimization under both model and design variables uncertainty. Journal of Aircraft, 50(2):528–538.

Koch, P., Wujek, B., and Golovidov, O. (2000). A multi-stage, parallel implementation of probabilistic design optimization in an MDO framework. In 8th AIAA Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA, USA.

Koch, P. N., Wujek, B., Golovidov, O., and Simpson, T. W. (2002). Facilitating probabilistic multidisciplinary design optimization using Kriging approximation models. In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, GA, USA.

Kokkolaras, M., Mourelatos, Z. P., and Papalambros, P. Y. (2004). Design optimization of hierarchically decomposed multilevel systems under uncertainty. In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Salt Lake City, UT, USA.

Leotardi, C., Serani, A., Iemma, U., Campana, E. F., and Diez, M. (2016). A variable-accuracy metamodel-based architecture for global MDO under uncertainty. Structural and Multidisciplinary Optimization, 54(3):573–593.MathSciNet

Li, L., Jing, S., and Liu, J. (2010). A hierarchical hybrid strategy for reliability analysis of multidisciplinary design optimization. In 14th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), Shanghai, China.

Li, L., Liu, J. H., and Liu, S. (2014). An efficient strategy for multidisciplinary reliability design and optimization based on CSSO and PMA in SORA framework. Structural and Multidisciplinary Optimization, 49(2):239–252.MathSciNet

Li, Y., Jiang, P., Gao, L., and Shao, X. (2013). Sequential optimisation and reliability assessment for multidisciplinary design optimisation under hybrid uncertainty of randomness and fuzziness. Journal of Engineering Design, 24(5):363–382.

Lin, P. T. and Gea, H. C. (2013). Reliability-based multidisciplinary design optimization using probabilistic gradient-based transformation method. Journal of Mechanical Design, 135(2):021001.

Liu, H., Chen, W., Kokkolaras, M., Papalambros, P. Y., and Kim, H. M. (2006). Probabilistic analytical target cascading: a moment matching formulation for multilevel optimization under uncertainty. Journal of Mechanical Design, 128(4):991–1000.

McAllister, C. D. and Simpson, T. W. (2003). Multidisciplinary robust design optimization of an internal combustion engine. Journal of Mechanical Design, 125(1):124–130.

Meng, D., Li, Y.-F., Huang, H.-Z., Wang, Z., and Liu, Y. (2015). Reliability-based multidisciplinary design optimization using subset simulation analysis and its application in the hydraulic transmission mechanism design. Journal of Mechanical Design, 137(5):051402.

Moore, R. E., Kearfott, R. B., and Cloud, M. J. (2009). Introduction to interval analysis, volume 110. SIAM.MATH

Nataf, A. (1962). Détermination des distributions dont les marges sont données (in French). Comptes Rendus de l’ Académie des Sciences, 225:42–43.MATH

Negoita, C., Zadeh, L., and Zimmermann, H. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(3–28):61–72.MathSciNet

Noton, A. R. M. (2013). Introduction to variational methods in control engineering. Elsevier.MATH

Oakley, D. R., Sues, R. H., and Rhodes, G. S. (1998). Performance optimization of multidisciplinary mechanical systems subject to uncertainties. Probabilistic Engineering Mechanics, 13(1):15–26.

Padmanabhan, D. and Batill, S. (2002). Reliability based optimization using approximations with applications to multi-disciplinary system design. In 40th AIAA Aerospace Sciences Meeting & Exhibit, Reno, NV, USA.

Rackwitz, R. (2001). Reliability analysis—a review and some perspectives. Structural safety, 23(4):365–395.

Rhodes, G. and Sues, R. (1994). Portable parallel stochastic optimization for the design of aeropropulsion components. In 35th Structures, Structural Dynamics, and Materials Conference, Hilton Head, SC, USA, page 1419.

Rosenblatt, M. (1952). Remarks on a multivariate transformation. The annals of mathematical statistics, 23(3):470–472.MathSciNetMATH

Sokolowski, J. and Zolesio, J.-P. (1992). Introduction to shape optimization. In Introduction to Shape Optimization, pages 5–12. Springer.

Sues, R. and Cesare, M. (2000). An innovative framework for reliability-based MDO. In 41st Structures, Structural Dynamics, and Materials Conference and Exhibit, Atlanta, GA, USA.

Wang, L., Xiong, C., Hu, J., Wang, X., and Qiu, Z. (2018). Sequential multidisciplinary design optimization and reliability analysis under interval uncertainty. Aerospace Science and Technology, 80:508–519.

Xia, T., Li, M., and Zhou, J. (2016). A sequential robust optimization approach for multidisciplinary design optimization with uncertainty. Journal of Mechanical Design, 138(11):111406.

Xiong, F., Sun, G., Xiong, Y., and Yang, S. (2014). A moment-matching robust collaborative optimization method. Journal of Mechanical Science and Technology, 28(4):1365–1372.

Xiong, F., Yin, X., Chen, W., and Yang, S. (2010). Enhanced probabilistic analytical target cascading with application to multi-scale design. Engineering Optimization, 42(6):581–592.

Xu, H., Li, W., Xing, L., and Zhu, S.-P. (2017). Multidisciplinary design optimization under correlated uncertainties. Concurrent Engineering, 25(3):262–275.

Yang, F., Yue, Z., Li, L., and Guan, D. (2018). Hybrid reliability-based multidisciplinary design optimization with random and interval variables. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(1):52–64.

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

Yao, W., Chen, X., Ouyang, Q., and Van Tooren, M. (2012). A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization. Structural and Multidisciplinary Optimization, 45(4):559–574.

Yao, W., Chen, X., Ouyang, Q., and Van Tooren, M. (2013). A reliability-based multidisciplinary design optimization procedure based on combined probability and evidence theory. Structural and Multidisciplinary Optimization, 48(2):339–354.MathSciNet

Zaman, K. and Mahadevan, S. (2017). Reliability-based design optimization of multidisciplinary system under aleatory and epistemic uncertainty. Structural and Multidisciplinary Optimization, 55(2):681–699.MathSciNet

Zhang, J. and Zhang, B. (2013a). A collaborative approach for multidisciplinary systems reliability design and optimization. In Advanced Materials Research, volume 694, pages 911–914. Trans Tech Publ.

Zhang, J. and Zhang, B. (2013b). A collaborative strategy for reliability-based multidisciplinary design optimization. In 17th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), Whister, Canada.

Zhang, X. and Huang, H.-Z. (2010). Sequential optimization and reliability assessment for multidisciplinary design optimization under aleatory and epistemic uncertainties. Structural and Multidisciplinary Optimization, 40(1–6):165.

Zhao, M. and Cui, W. (2011). On the development of bi-level integrated system collaborative optimization. Structural and Multidisciplinary Optimization, 43(1):73–84.

Zhou, K., Doyle, J. C., Glover, K., et al. (1996). Robust and optimal control, volume 40. Prentice hall New Jersey.

Titel: Uncertainty-Based Multidisciplinary Design Optimization (UMDO)
verfasst von: Loïc Brevault
Mathieu Balesdent
Verlag: Springer International Publishing
Buch: Aerospace System Analysis and Optimization in Uncertainty
Print ISBN: 978-3-030-39125-6

Electronic ISBN: 978-3-030-39126-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39126-3_7

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