Skip to main content

Multidisciplinary System Modeling and Optimization

  • Chapter
  • First Online:
Aerospace System Analysis and Optimization in Uncertainty

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 156))

  • 851 Accesses

Abstract

With the increasing complexity of systems such as aerospace vehicles, it has become more and more necessary to adopt a global and integrated approach from the early steps and all along the design process. Tightly coupling aerodynamics, propulsion, structure, guidance and navigation, trajectory, etc. but also taking into account environmental and operational constraints as well as manufacturability, reliability, maintainability is a huge challenge.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 79.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Agte, J., de Weck, O., Sobieszczanski-Sobieski, J., Arendsen, P., Morris, A., and Spieck, M. (2010). MDO: assessment and direction for advancement—an opinion of one international group. Structural and Multidisciplinary Optimization, 40(1):17–33.

    Google Scholar 

  • Alexandrov, N. and Lewis, R. (2000). Algorithmic perspectives on problem formulations in MDO. In 8th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach, CA, USA.

    Google Scholar 

  • Alexandrov, N. M. (1997). Multilevel methods for MDO. Multidisciplinary Design Optimization: State of the Art, SIAM, pages 79–89.

    Google Scholar 

  • Allison, J., Kokkolaras, M., Zawislak, M., and Papalambros, P. Y. (2005). On the use of analytical target cascading and collaborative optimization for complex system design. In 6th World Congress on Structural and Multidisciplinary Optimization, Rio de Janeiro, Brazil.

    Google Scholar 

  • Balesdent, M. (2011). Multidisciplinary design optimization of launch vehicles. PhD thesis, Ecole Centrale de Nantes.

    Google Scholar 

  • Balesdent, M., Bérend, N., Dépincé, P., and Chriette, A. (2012). A survey of multidisciplinary design optimization methods in launch vehicle design. Structural and Multidisciplinary Optimization, 45(5):619–642.

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  • Blair, J., Ryan, R., and Schutzenhofer, L. (2001). Launch vehicle design process: characterization, technical integration, and lessons learned. NASA/TP-2001-210992, NASA, Langley Research Center.

    Google Scholar 

  • Braun, R. D. (1996). Collaborative optimization: an architecture for large-scale distributed design. PhD thesis, Stanford University.

    Google Scholar 

  • Breitkopf, P. and Coelho, R. F. (2013). Multidisciplinary design optimization in computational Mechanics. John Wiley & Sons.

    Google Scholar 

  • Choudhary, R., Malkawi, A., and Papalambros, P. (2005). Analytic target cascading in simulation-based building design. Automation in construction, 14(4):551–568.

    Google Scholar 

  • Coelho, R. F., Breitkopf, P., Knopf-Lenoir, C., and Villon, P. (2009). Bi-level model reduction for coupled problems. Structural and Multidisciplinary Optimization, 39(4):401–418.

    MathSciNet  MATH  Google Scholar 

  • Conn, A. R., Gould, N. I., and Toint, P. L. (2000). Trust region methods, volume 1. SIAM.

    MATH  Google Scholar 

  • Cramer, E. J., Dennis, Jr, J., Frank, P. D., Lewis, R. M., and Shubin, G. R. (1994). Problem formulation for multidisciplinary optimization. SIAM Journal on Optimization, 4(4):754–776.

    MathSciNet  MATH  Google Scholar 

  • DeMiguel, A.-V. and Murray, W. (2000). An analysis of collaborative optimization methods. In 8th AIAA/USAF/NASA/ISSMO symposium on multidisciplinary analysis and optimization, Long Beach, CA, USA.

    Google Scholar 

  • DeMiguel, V. and Murray, W. (2006). A local convergence analysis of bilevel decomposition algorithms. Optimization and Engineering, 7(2):99–133.

    MathSciNet  MATH  Google Scholar 

  • El Majd, B. A., Desideri, J.-A., and Habbal, A. (2010). Optimisation de forme fluide-structure par un jeu de Nash (in French). Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, (13):3–15.

    MathSciNet  Google Scholar 

  • Felippa, C. A., Park, K., and Farhat, C. (2001). Partitioned analysis of coupled mechanical systems. Computer methods in applied mechanics and engineering, 190(24–25):3247–3270.

    MATH  Google Scholar 

  • Golovidov, O., Kodiyalam, S., Marineau, P., Wang, L., and Rohl, P. (1998). Flexible implementation of approximation concepts in an MDO framework. In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, page 4959.

    Google Scholar 

  • Gray, J. S., Hwang, J. T., Martins, J. R. R. A., Moore, K. T., and Naylor, B. A. (2019). OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization. Structural and Multidisciplinary Optimization, 59(4):1075–1104.

    MathSciNet  Google Scholar 

  • Haftka, R. T. and Watson, L. T. (2005). Multidisciplinary design optimization with quasiseparable subsystems. Optimization and Engineering, 6(1):9–20.

    MathSciNet  MATH  Google Scholar 

  • Han, J. and Papalambros, P. (2010). A Note on the Convergence of Analytical Target Cascading With Infinite Norms. Journal of Mechanical Design, 132(3):034502–034502–6.

    Google Scholar 

  • Henderson, R., Martins, J. R. R. A., and Perez, R. (2012). Aircraft conceptual design for optimal environmental performance. Aeronautical Journal, 116(1175):1.

    Google Scholar 

  • Hiriyannaiah, S. and Mocko, G. M. (2008). Information management capabilities of MDO frameworks. In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pages 635–645. American Society of Mechanical Engineers.

    Google Scholar 

  • Huang, C.-H. and Bloebaum, C. (2004). Incorporation of preferences in multi-objective concurrent subspace optimization for multidisciplinary design. In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, USA.

    Google Scholar 

  • Huang, H., An, H., Wu, W., Zhang, L., Wu, B., and Li, W. (2014). Multidisciplinary design modeling and optimization for satellite with maneuver capability. Structural and Multidisciplinary Optimization, 50(5):883–898.

    Google Scholar 

  • Hwang, J. T., Lee, D. Y., Cutler, J. W., and Martins, J. R. R. A. (2013). Large-scale MDO of a small satellite using a novel framework for the solution of coupled systems and their derivatives. In 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Boston, MA, USA.

    Google Scholar 

  • Keane, A. and Nair, P. (2005). Computational approaches for aerospace design: the pursuit of excellence. Wiley & Sons.

    Google Scholar 

  • Kennedy, G. and Martins, J. (2014). A parallel aerostructural optimization framework for aircraft design studies. Structural and Multidisciplinary Optimization, 50(6):1079–1101.

    Google Scholar 

  • Kenway, G., Kennedy, G., and Martins, J. R. R. A. (2014). Scalable parallel approach for high-fidelity steady-state aeroelastic analysis and adjoint derivative computations. AIAA Journal, 52(5):935–951.

    Google Scholar 

  • Kim, H. (2001). Target Cascading in Optimal System Design. PhD thesis, University of Michigan, USA.

    Google Scholar 

  • Kim, H., Chen, W., and Wiecek, M. (2006). Lagrangian Coordination for Enhancing the Convergence of Analytical Target Cascading. AIAA Journal, 44(10):2197–2207.

    Google Scholar 

  • Lambe, A. B. and Martins, J. R. R. A. (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.

    MATH  Google Scholar 

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

    Google Scholar 

  • 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.

    Google Scholar 

  • Michelena, N., Kim, H., and Papalambros, P. (1999). A system partitioning and optimization approach to target cascading. In 12th International Conference on Engineering Design. Munich, Germany.

    Google Scholar 

  • Michelena, N., Park, H., and Papalambros, P. (2003). Convergence properties of analytical target cascading. AIAA Journal, 41(5):897–905.

    Google Scholar 

  • Nguyen, N.-V., Choi, S.-M., Kim, W.-S., Lee, J.-W., Kim, S., Neufeld, D., and Byun, Y.-H. (2013). Multidisciplinary unmanned combat air vehicle system design using multi-fidelity model. Aerospace Science and Technology, 26(1):200–210.

    Google Scholar 

  • Ortega, J. M. (1973). Stability of difference equations and convergence of iterative processes. SIAM Journal on Numerical Analysis, 10(2):268–282.

    MathSciNet  MATH  Google Scholar 

  • Padula, S. and Gillian, R. (2006). Multidisciplinary environments: a history of engineering framework development. In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, page 7083.

    Google Scholar 

  • Pareto, V. (1971). Manual of Political Economy. A.M Kelley. New-York, NY, USA.

    Google Scholar 

  • Perez, V., Renaud, J., and Watson, L. (2002). Reduced sampling for construction of quadratic response surface approximations using adaptive experimental design. In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Denver, CO, USA.

    Google Scholar 

  • Peri, D. and Campana, E. F. (2003). Multidisciplinary design optimization of a naval surface combatant. Journal of Ship Research, 47(1):1–12.

    Google Scholar 

  • Renaud, J. and Gabriele, G. (1993). Improved Coordination in Nonhierarchic System Optimization. AIAA Journal, 31(12):2367–2373.

    MATH  Google Scholar 

  • Renaud, J. and Gabriele, G. (1994). Approximation in Nonhierarchic System Optimization. AIAA Journal, 32(1):198–205.

    MATH  Google Scholar 

  • Rodriguez, J. F., Perez, V. M., Padmanabhan, D., and Renaud, J. E. (2001). Sequential approximate optimization using variable fidelity response surface approximations. Structural and Multidisciplinary Optimization, 22(1):24–34.

    Google Scholar 

  • Rodriguez, J. F., Renaud, J. E., and Watson, L. T. (1998). Trust region augmented Lagrangian methods for sequential response surface approximation and optimization. Journal of Mechanical Design, 120(1):58–66.

    Google Scholar 

  • Salas, A. and Townsend, J. (1998). Framework requirements for MDO application development. In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, page 4740.

    Google Scholar 

  • Salkuyeh, D. K. (2007). Generalized Jacobi and Gauss-Seidel methods for solving linear system of equations. Numerical mathematics—English series -, 16(2):164.

    MathSciNet  Google Scholar 

  • Sankararaman, S. and Mahadevan, S. (2012). Likelihood-based approach to multidisciplinary analysis under uncertainty. Journal of Mechanical Design, 134(3):031008.

    Google Scholar 

  • Sellar, R. and Batill, S. (1996). Concurrent subspace optimization using gradient-enhanced neural network approximations. In 6th Symposium on Multidisciplinary Analysis and Optimization, Bellevue, WA, USA.

    Google Scholar 

  • Sellar, R., Batill, S., and Renaud, J. (1996). Response surface based, concurrent subspace optimization for multidisciplinary system design. In 34th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA.

    Google Scholar 

  • Sobieszczanski-Sobieski, J. (1988). Optimization by decomposition: a step from hierarchic to non-hierarchic systems. NASA Technical Report, CP-3031.

    Google Scholar 

  • Sobieszczanski-Sobieski, J. (1990). Sensitivity of Complex Internally Coupled Systems. AIAA Journal, 28(1):153–160.

    Google Scholar 

  • Sobieszczanski-Sobieski, J., Agte, J., and Sandusky, R. (1998). Bi-level integrated system synthesis (BLISS). NASA Technical Report TM-1998-208715.

    Google Scholar 

  • Sobieszczanski-Sobieski, J., Agte, J. S., and Sandusky, R. R. (2000). Bi-level integrated system synthesis. AIAA Journal, 38(1):164–172.

    Google Scholar 

  • Sobieszczanski-Sobieski, J. and Haftka, R. (1997). Multidisciplinary aerospace design optimization: survey of recent developments. Structural and Multidisciplinary Optimization, 14(1):1–23.

    Google Scholar 

  • Tedford, N. P. and Martins, J. R. R. A. (2006). On the common structure of MDO problems: a comparison of architectures. In 11th AIAA/ISSMO multidisciplinary analysis and optimization conference, Portsmouth, VA.

    Google Scholar 

  • Tedford, N. P. and Martins, J. R. R. A. (2010). Benchmarking multidisciplinary design optimization algorithms. Optimization and Engineering, 11(1):159–183.

    MathSciNet  MATH  Google Scholar 

  • Tosserams, S., Etman, L. P., and Rooda, J. (2009). A classification of methods for distributed system optimization based on formulation structure. Structural and Multidisciplinary Optimization, 39(5):503.

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  • Wujek, B., Renaud, J., and Batill, S. (1997). A Concurrent Engineering Approach for Multidisciplinary Design in a Distributed Computing Environment. Multidisciplinary Design Optimization: State-of-the-Art, N. Alexandrov and M.Y. Hussaini (Ed.), SIAM Series: Proceedings in Applied Mathematics 80, pp. 189–208.

    Google Scholar 

  • Wujek, B., Renaud, J., Batill, S., and Brockman, J. (1996). Concurrent Subspace Optimization Using Design Variable Sharing in a Distributed Computing Environment. Concurrent Engineering, 4(4):361–377.

    Google Scholar 

  • Yi, S.-I., Shin, J.-K., and Park, G. (2008). Comparison of MDO methods with mathematical examples. Structural and Multidisciplinary Optimization, 35(5):391–402.

    Google Scholar 

  • Zadeh, P. M. and Toropov, V. (2002). Multi-fidelity multidisciplinary design optimization based on collaborative optimization framework. In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, GA, USA.

    Google Scholar 

  • Zang, T. A., Hemsch, M. J., Hilburger, M. W., Kenny, S. P., Luckring, J. M., Maghami, P., Padula, S. L., and Stroud, W. J. (2002). Needs and opportunities for uncertainty-based multidisciplinary design methods for aerospace vehicles. NASA/TM-2002-211462, NASA Langley Research Center.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Loïc Brevault .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Brevault, L., Balesdent, M. (2020). Multidisciplinary System Modeling and Optimization. In: Aerospace System Analysis and Optimization in Uncertainty. Springer Optimization and Its Applications, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-39126-3_1

Download citation

Publish with us

Policies and ethics