Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-22T20:58:06.128Z Has data issue: false hasContentIssue false
  • English
  • Français

AN ASSUMPTION NETWORK-BASED APPROACH TO SUPPORT MARGIN ALLOCATION AND MANAGEMENT

Part of: Systems Engineering and Design

Published online by Cambridge University Press:  11 June 2020

S. El Fassi ,
M. D. Guenov  and
A. Riaz
S. El Fassi*
Affiliation:
Cranfield University, United Kingdom
M. D. Guenov
Affiliation:
Cranfield University, United Kingdom
A. Riaz
Affiliation:
Cranfield University, United Kingdom
*
*s.el-fassi@cranfield.ac.uk

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Presented is an approach to support margin allocation and management via a graph-theoretical network of assumptions. In contrast to the document-centric approach, the network captures assumptions dependencies, and enables an algorithmic process supporting margin allocation and management. Ultimately, this methodology is intended to assist decision-makers in managing assumptions and examining their impact on an architecture. Explicitly linking margins to assumptions allows to support mitigating their risk of invalidity. The approach is demonstrated with a conceptual aircraft design example.

Keywords

conceptual designdecision makinguncertaintyassumption managementmargin
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 2275 - 2284
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), 2020. Published by Cambridge University Press

References

Berner, C.L. (2017), Contributions to Improved Risk Assessments: To Better Reflect the Strength of Background Knowledge, [PhD Thesis], University of Stavanger.Google Scholar
Bile, Y. et al. (2018), “Towards Automating the Sizing Process in Conceptual (Airframe) Systems Architecting”, 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA, Reston, Virginia. https://doi.org/10.2514/6.2018-1067CrossRefGoogle Scholar
Cooke, R.M. et al. (2015), “Sculpting: A Fast, Interactive Method for Probabilistic Design Space Exploration and Margin Allocation”, 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA, Reston, Virginia. https://doi.org/10.2514/6.2015-3440CrossRefGoogle Scholar
Crawley, E., Cameron, B. and Selva, D. (2016), System Architecture: Strategy and Product Development for Complex Systems, Pearson Education Limited, Essex.Google Scholar
Eckert, C., Isaksson, O. and Earl, C. (2019), “Design margins: a hidden issue in industry”, Design Science, Vol. 5 No. 9, pp. 1–24. https://doi.org/10.1017/dsj.2019.7CrossRefGoogle Scholar
Flage, R. and Aven, T. (2009), “Expressing and communicating uncertainty in relation to quantitative risk analysis”, Reliability: Theory & Applications, Vol. 4 No. 2 (13), pp. 9–18.Google Scholar
Guenov, M.D. et al. (2020), “Computational Framework for Interactive Architecting of Complex Systems”, Systems Engineering. Forthcoming.CrossRefGoogle Scholar
INCOSE. (2015), INCOSE Systems Engineering Handbook, In: Walden, D.D., Roedler, G.J., Forsberg, K.J., Hamelin, R.D. and Shortell, T.M. (Eds.), John Wiley & Sons, Inc., Hoboken, New Jersey.Google Scholar
ISO. (2015), ISO/IEC/IEEE 15288: Systems and Software Engineering - System Life Cycle Processes, International Organization for Standardization, Geneva.Google Scholar
Kiureghian, A. Der and Ditlevsen, O. (2009), “Aleatory or epistemic? Does it matter?”, Structural Safety, Vol. 31 No. 2, pp. 105–112. https://doi.org/10.1016/j.strusafe.2008.06.020CrossRefGoogle Scholar
Kleiner, S. and Kramer, C. (2013), “Model Based Design with Systems Engineering Based on RFLP Using V6”, In: Abramovici, M. and Stark, R. (Eds.), Smart Product Engineering, Springer, Berlin, Heidelberg, pp. 93–102. https://doi.org/10.1007/978-3-642-30817-8_10CrossRefGoogle Scholar
Lewis, G.A., Mahatham, T. and Wrage, L. (2004), CMU/SEI-2004-TN-021: Assumptions Management in Software Development, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
McManus, H. and Hastings, D. (2005), “A Framework for Understanding Uncertainty and its Mitigation and Exploitation in Complex Systems”, INCOSE International Symposium, Vol. 15 No. 1, pp. 484–503. https://doi.org/10.1002/j.2334-5837.2005.tb00685.xCrossRefGoogle Scholar
NASA. (2016), NASA SP-2016-6105 Rev2: NASA Systems Engineering Handbook, National Aeronautics and Space Administration.Google Scholar
Ramsey, A. (1988), Formal Methods in Artificial Intelligence, Cambridge University Press, Cambridge.Google Scholar
Raymer, D. (2018), Aircraft Design: A Conceptual Approach, Sixth Edition, AIAA, Washington, DC. https://doi.org/10.2514/4.104909CrossRefGoogle Scholar
Sadlauer, A., Hehenberger, P. and Zeman, K. (2017), “The influence of documenting assumed values of product properties on the number of iterations in the design process - first observations”, International Journal of Information Technology and Management, Vol. 16 No. 1, pp. 73–90. https://doi.org/10.1504/IJITM.2017.080951CrossRefGoogle Scholar
Thunnissen, D.P. (2004), “Method for Determining Margins in Conceptual Design”, Journal of Spacecraft and Rockets, Vol. 41 No. 1, pp. 85–92.CrossRefGoogle Scholar
Tirumala, A.S. (2006), An Assumptions Management Framework for Systems Software, [PhD Thesis], University of Illinois at Urbana-Champaign.Google Scholar
Umeda, Y. and Tomiyama, T. (1997), “Functional reasoning in design”, IEEE Expert, Vol. 12 No. 2, pp. 42–48. https://doi.org/10.1109/64.585103CrossRefGoogle Scholar
VDI. (2004), VDI 2206: Design Methodology for Mechatronic Systems, Verein Deutscher Ingenieure, Düsseldorf.Google Scholar
Yang, C., Liang, P. and Avgeriou, P. (2018), “Evaluation of a process for architectural assumption management in software development”, Science of Computer Programming, Vol. 168, pp. 38–70. https://doi.org/10.1016/j.scico.2018.08.002CrossRefGoogle Scholar
Yang, C. et al. (2017), “An industrial case study on an architectural assumption documentation framework”, Journal of Systems and Software, Vol. 134, pp. 190–210. https://doi.org/10.1016/j.jss.2017.09.007CrossRefGoogle Scholar
Zaidi, T., Jimenez, H. and Mavris, D.N. (2014), “Quantifying Random Variable Dependence Structure Through Copulas Theory for Probabilistic Assessment”, 14th AIAA Aviation Technology, Integration, and Operations Conference, AIAA, Reston, Virginia. https://doi.org/10.2514/6.2014-2171CrossRefGoogle Scholar
Zang, T.A. et al. (2015), “A Strategy for Probabilistic Margin Allocation in Aircraft Conceptual Design”, 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA, Reston, Virginia. https://doi.org/10.2514/6.2015-3443CrossRefGoogle Scholar