Skip to main content

Conceptual design of sacrificial sub-systems: failure flow decision functions


This paper presents a method to conceptually model sacrificing non-critical sub-systems, or components, in a failure scenario to protect critical system functionality through a functional failure modeling technique. Understanding the potential benefits and drawbacks of choosing how a failure is directed in a system away from critical sub-systems and toward sub-systems that can be sacrificed to maintain core functionality can help system designers to design systems that are more likely to complete primary mission objectives despite failure events. Functional modeling techniques are often used during the early stage of conceptual design for complex systems to provide a better understanding of system architecture. A family of methods exists that focuses on the modeling of failure initiation and propagation within a functional model of a system. Modeling failure flow provides an opportunity to understand system failure propagation and inform system design iteration for improved survivability and robustness. Currently, the ability to model failure flow decision-making is missing from the family of function failure and flow methodologies. The failure flow decision function (FFDF) methodology presented in this paper enables system designers to model failure flow decision-making problems where functions and flows that are critical to system operation are protected through the sacrifice of less critical functions and flow exports. The sacrifice of less critical system functions and flows allows for mission critical functionality to be preserved, leading to a higher rate of mission objective completion. An example of FFDF application in a physical design is a non-critical peripheral piece of electrical hardware being sacrificed during an electrical surge condition to protect critical electronics necessary for the core functionality of the system. In this paper, a case study of the FFDF method is presented based on a Sojourner class Mars Exploration Rover (MER) platform.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  • Blanchard BS, Fabrycky JW (1990) Systems engineering and analysis 4th edn. Prentice Hall, Englewood Cliffs, New Jersey.

  • Bohm MR, Stone RB, Szykman S (2005) Enhancing virtual product representations for advanced design repository systems. J Comput Inf Sci Eng 5(4):360–372

    Article  Google Scholar 

  • Browning TR (2001) Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Trans Eng Manag 48(3):292–306.

  • David P, Idasiak V, Kratz F (2010) Reliability study of complex physical systems using SysML. Reliab Eng Syst Saf 95(4):431–450

    Article  Google Scholar 

  • Distefano S, Puliafito A (2007) Dynamic reliability block diagrams: overview of a methodology. ESREL 7:1059–68.

  • Ericson C (1999) Fault tree analysis–a history from the proceeding of the 17th International System Safety Conference. Orlando

  • Force, US Air (1981) “ICAM architecture Part II, Vol. IV., Function Modelling Manual (IDEF0).” AFWAL-TR-81-4023, Wright-Patterson Air Force Base, OH, USA

  • Garvey PR, Pinto CA (2009) Introduction to functional dependency network analysis. In: The MITRE Corporation and Old Dominion, Second International Symposium on Engineering Systems, MIT, Cambridge, Massachusetts, vol. 5.1.

  • Garvey PR, Ariel PC, Santos JR (2014) Modelling and measuring the operability of interdependent systems and systems of systems: advances in methods and applications. Int J Syst Syst Eng 5(1):1–24

    Article  Google Scholar 

  • Gosselin SR (2006) Probabilities of failure and uncertainty estimate information for passive components: a literature review. Division of Fuel, Engineering, and Radiological Research, Office of Nuclear Regulatory Research, US Nuclear Regulatory Commission

  • Guariniello C, DeLaurentis D (2017) Supporting design via the system operational dependency analysis methodology. Res Eng Design 28(1):53–69

    Article  Google Scholar 

  • Haimes YY, Horowitz BM, Lambert JH, Santos JR, Lian C, Crowther KG (2005) Inoperability input-output model for interdependent infrastructure sectors. I: theory and methodology. J Infrastruct Syst 11(2):67–79

    Article  Google Scholar 

  • Hirtz J, Stone RB, McAdams DA, Szykman S, Wood KL (2002) A functional basis for engineering design: reconciling and evolving previous efforts. Res Eng Design 13(2):65–82

    Article  Google Scholar 

  • Huang E, Ramamurthy R, McGinnis LF (2007) System and simulation modeling using SysML. In: Proceedings of the 39th Conference on Winter Simulation: 40 Years! The Best Is yet to Come, pp 796–803. IEEE Press.

  • Hutcheson RS, McAdams DA, Stone RB, Tumer IY (2006) A function-based methodology for analyzing critical events. In: ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers. pp 1193–1204.

  • Jensen D, Tumer IY, Kurtoglu T (2009) Flow state logic (FSL) for analysis of failure propagation in early design. In: ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, pp 1033–1043.

  • JPL Team X (2016) Accessed 1 April

  • Kalvin AD, Varol YL (1983) On the generation of all topological sortings. J Algorithms 4(2):150–162. doi:10.1016/0196-6774(83)90042-1

    MathSciNet  Article  MATH  Google Scholar 

  • Kumamoto H, Henley EJ (1996) Probabilistic risk assessment and management for engineers and scientists. Institute of Electrical and Electronics Engineers (IEEE Press).

  • Kurtoglu T, Tumer IY (2007) Ffip: a framework for early assessment of functional failures in complex systems. In: The International Conference on Engineering Design, ICED, vol. 7.

  • Kurtoglu T, Tumer IY (2008) A graph-based fault identification and propagation framework for functional design of complex systems. J Mech Design 130(5):051401

    Article  Google Scholar 

  • Kurtoglu T, Tumer IY, Jensen DC (2010) A functional failure reasoning methodology for evaluation of conceptual system architectures. Res Eng Des 21(4):209–234

    Article  Google Scholar 

  • Lightsey B (2001) Systems engineering fundamentals. DTIC document.

  • Long J (2002) Relationships between common graphical representations in systems engineering. Vitech White Paper, Vitech Corporation, Vienna, p 70

    Google Scholar 

  • Lucero B, Viswanathan VK, Linsey JS, Turner CJ (2014) Identifying critical functions for use across engineering design domains. J Mech Des 136(12):121101

    Article  Google Scholar 

  • Materese R (2002) A functional basis for engineering design: reconciling and evolving previous efforts. Text. NIST. Accessed Feb 1

  • Mimlitz Z, Short A, Van Bossuyt DL (2016) Towards risk-informed operation of autonomous vehicles to increase resilience in unknown and dangerous environments. In: ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

  • Mohr RR (2002) Failure modes and effects analysis. JE Jacobs Sverdrup. Accessed 3 Mar 2016

  • Navarro I, Fernando M (2012) An introduction to swarm robotics. Int Sch Res Not 2013(September):e608164. doi:10.5402/2013/608164

    Google Scholar 

  • O’Halloran BM, Papakonstantinou N, Van Bossuyt DL (2015) Modeling of function failure propagation across uncoupled systems. In: Reliability and Maintainability Symposium (RAMS), 2015 Annual, IEEE, pp 1–6.

  • Papakonstantinou N, Sierla S, Jensen DC, Tumer IR (2012) Simulation of interactions and emergent failure behavior during complex system design. J Comput Inf Sci Eng 12(3):031007

    Article  Google Scholar 

  • Rumbaugh J, Jacobson I, Booch G (2004) Unified modeling language reference manual, The Pearson Higher Education.

  • Sen C, Summers JD, Mocko GM (2013) Physics-based reasoning in conceptual design using a formal representation of function structure graphs. J Comput Inf Sci Eng 13(1):011008

    Article  Google Scholar 

  • Short AR, Van Bossuyt DL (2015a) Rerouting failure flows using logic blocks in functional models for improved system robustness: failure flow decision functions. In: International Conference on Engineering Design 2015

  • Short AR, Van Bossuyt DL (2015b) Risk attitude informed route planning in a simulated planetary rover. In: ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, V01BT02A048–V01BT02A048. American Society of Mechanical Engineers.

  • Short AR, Van Bossuyt DL (2016) Active mission success estimation through PHM-informed probabilistic modelling. Accessed 4 Mar

  • Short AR, Mimlitz Z, Van Bossuyt DL (2016) Autonomous system design and controls design for operations in high risk environments. In: ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

  • Sojourner Rover Home Page (2015) Accessed 15 Dec

  • Stone RB, Wood KL (2000) Development of a functional basis for design. J Mech Des 122(4):359–370

    Article  Google Scholar 

  • Stone RB, Tumer IY, Van Wie M (2005) The function-failure design method. J Mech Des 127(3):397–407

    Article  Google Scholar 

  • Truszkowski W, Hinchey M, Rash J, Rouff C (2004) NASA’s swarm missions: the challenge of building autonomous software. IT Prof 6(5):47–52

    Article  Google Scholar 

  • Van Eck D, McAdams DA, Vermaas PE (2007) Functional decomposition in engineering: a survey. In: ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp 227–236. American Society of Mechanical Engineers.

  • Wertz JR, Everett DF, Puschell JJ (2011a) Risk and reliability. In: Space mission engineering: the new SMAD. Microcosm Press

  • Wertz JR, Everett DF, Puschell JJ (2011b) Space mission engineering: the new SMAD. Microcosm Press

  • Yadav S, Verma KK, Mahanta S (2012) The maze problem solved by micro mouse. Int J Eng Adv Technol (IJEAT) ISSN 2249–8958

Download references


This research was partially supported by United States Nuclear Regulatory Commission Grant No. NRC-HQ-84-14-G-0047. Any opinions or findings of this work are the responsibility of the authors, and do not necessarily reflect the views of the sponsors or collaborators. The authors wish to acknowledge the work of the undergraduate research assistants in the Van Bossuyt lab and specifically wish to thank the following students for their contributions: Alexis Humann, David Hodge, Zachary Mimlitz, and Robin Coleman. The authors wish to thank LeVar Burton, Fred Rogers, Gene Roddenberry, Carl Sagan, and their individual middle school and high school science and technical arts teachers who inspired them to pursue careers in the sciences and engineering, and instilled in them a sense of purpose and compassion.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Douglas L. Van Bossuyt.


Appendix 1

Appendix 2

Flow type Probability of passing failure downstream Probability of passing failure upstream
Collectable energy 0.10 0.00
Electrical energy 0.40 0.02
Digital signal 0.50 0.02
Control signal 0.50 0.02
Positional information 0.47 0.00
Visual information 0.47 0.00
Rotational work 0.50 0.15
Translational work 0.50 0.15
Alignment work 0.25 0.15
Function Probability of accepting failure flow  
Accumulate energy 0.50  
Store energy 0.12  
Distribute electrical 0.24  
Control magnitude electrical 0.20  
Convert electrical to rotation 0.16  
Transmit rotation 0.16  
Convert rotation to translation 0.16  
Direct command 0.44  
Process signal 0.01  
Store data 0.01  
Record position 0.22  
Record visual 0.22  
Transmit data 0.44  

Appendix 3

Index Flow type   
f1 Collectable energy   
f2 Electrical energy   
f3 Digital signal   
f4 Position information   
f5 Visual information   
f6 Rotational work   
f7 Translation work   
f8 Steering work   
Index Function type Index Function type
1 Operating environment 26 Convert electric-to-rotation 7
2 Accumulate energy 1 27 Convert electric-to-rotation 8
3 Accumulate energy 2 28 Convert electric-to-rotation 9
4 Accumulate energy 3 29 Convert electric-to-rotation 10
5 Accumulate energy 4 30 Transmit rotation 1
6 Accumulate energy 5 31 Transmit rotation 2
7 Accumulate energy 6 32 Transmit rotation 3
8 Accumulate energy 7 33 Transmit rotation 4
9 Accumulate energy 8 34 Convert rotation-to-translation 1
10 Accumulate energy 9 35 Convert Rotation-to-Translation 2
11 Accumulate energy 10 36 Convert rotation-to-translation 3
12 Accumulate energy 11 37 Convert rotation-to-translation 4
13 Accumulate energy 12 38 Convert rotation-to-translation 5
14 Accumulate energy 13 39 Convert rotation-to-translation 6
15 Store energy 1 40 Direct command
16 Store energy 2 41 Process signal
17 Store energy 3 42 Process signal (digital)
18 Distribute electricity 43 Store data 1
19 Control magnitude electrical 44 Store data 2
20 Convert electric-to-rotation 1 45 Store data 3
21 Convert electric-to-rotation 2 46 Record position
22 Convert electric-to-rotation 3 47 Record visual 1
23 Convert electric-to-rotation 4 48 Record visual 2
24 Convert electric-to-rotation 5 49 Record visual 3
25 Convert electric-to-rotation 6 50 Transmit data (analogue)

Appendix 4

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Short, AR., Lai, A.D. & Van Bossuyt, D.L. Conceptual design of sacrificial sub-systems: failure flow decision functions. Res Eng Design 29, 23–38 (2018).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Functional modeling
  • Failure modeling
  • Failure flow decision-making
  • Design methodology