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Multi-level Decomposed Systems Design: Converting a Requirement Specification into an Optimization Problem

Part of: Systems Engineering

Published online by Cambridge University Press:  26 July 2019

T. F. Beernaert  and
L. F. P. Etman

Abstract

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Complex technological artefacts are often decomposed into smaller components to keep their design manageable. The resulting challenge is to coordinate decisions that involve multiple components and to design components such that high-level targets are met. Analytical Target Cascading (ATC) is an analytical coordination method for the optimization of decomposed systems, which we aim to incorporate in systems engineering design process. To this extent, we relate the domain of engineering optimization to the domain of requirements engineering, and propose a method that constructs an ATC problem from functional specifications and requirements written in the newly developed Elephant Specification Language. The proposed method is demonstrated in the two-level design of an automotive powertrain. This contribution is a step towards design automation and is expected to increase the usability of decomposed optimization techniques.

Keywords

Systems Engineering (SE)Integrated product developmentDesign AutomationRequirementsOptimisation
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 3691 - 3700
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Eppinger, S. D., and Browning, T. R. (2012), “Design Structure Matrix Methods and Applications”, MIT Press, Cambridge, MA, USAGoogle Scholar
Forsberg, K., and Mooz, H. (1991), “The Relationship of System Engineering to the Project Cycle”, Joint Conference of NCOSE and the American Society for Engineering Management, pp. 57–65Google Scholar
Hull, E., Jackson, K., and Dick, J. (2005), “Requirements Engineering”, 2nd edn., Springer London Berlin HeidelbergGoogle Scholar
Kim, H.-M., Michelena, N. F., Papalambros, P. Y., and Jiang, T. (2003), “Target Cascading in Optimal System Design”, Journal of Mechanical Design, Vol. 125, No. 3, pp. 474–480Google Scholar
Michelena, N. F., Park, H., and Papalambros, P. Y. (2002), “Convergence properties of Analytical Target Cascading”, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, GeorgiaGoogle Scholar
Papalambros, P. Y., and Wilde, D. (2017), “Principles of Optimal Design: Modeling and Computation”, 3rd edn., Cambridge University Press, CambridgeGoogle Scholar
Tosserams, S., Etman, L. F. P., Papalambros, P. Y., and Rooda, J. E. (2006), “An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers”, Structural and Multidisciplinary Optimization, Vol. 31, No. 3, pp. 176–189Google Scholar
Tosserams, S., Etman, L. F. P., and Rooda, J. E. (2008), “Augmented Lagrangian coordination for distributed optimal design in MDO”, International Journal for Numerical Methods in Engineering, Vol. 73, pp. 1885–1910Google Scholar
Wilschut, T. (2018), “System specification and design structuring methods for a lock product platform”, Dissertation, Eindhoven University of TechnologyGoogle Scholar
Xu, M., Fadel, G., and Wiecek, M. M. (2017), “Improving the Performance of Augmented Lagrangian Coordination: Decomposition Variants and Dual Residuals”, Journal of Mechanical Design, Vol. 139, No. 3, pp. 031401Google Scholar
Young, R. R. (2004), “The Requirements Engineering Handbook”, Artech House, Boston, MA, USAGoogle Scholar