Skip to main content

Active mission success estimation through functional modeling

Abstract

Through the application of statistical models, the active mission success estimation (AMSE) introduced in this paper can be performed during a rapidly developing unanticipated failure scenario to support decision making. AMSE allows for system operators to make informed management and control decisions by performing analyses on a nested system of functional models that requires low time and computational cost. Existing methods for analyses of mission success such as probabilistic risk assessment or worst case analysis have been applied in the analysis and planning of space missions since the mid-twentieth century. While these methods are effective in analyzing anticipated failure scenarios, they are built on computational models, logical structures, and statistical models that often are difficult and time-intensive to modify, and are computationally inefficient leading to very long calculation times and making their ability to respond to unanticipated or rapidly developing scenarios limited. To demonstrate AMSE, we present a case study of a generalized crewed Martian surface station mission. A crew of four astronauts must perform activities to achieve scientific objectives while surviving for 1070 Martian sols before returning to Earth. A second crew arrives at the same site to add to the settlement midway through the mission. AMSE uses functional models to represent all of the major environments, infrastructure, equipment, consumables, and critical systems of interest (astronauts in the case study presented) in a nested super system framework that is capable of providing rapidly reconfigurable and calculable analysis. This allows for AMSE to be used to make informed mission control decisions when facing rapidly developing or unanticipated scenarios. Additionally, AMSE provides a framework for the inclusion of humans into functional analysis through a systems approach. Application of AMSE is expected to produce informed decision making benefits in a variety of situations where humans and machines work together toward mission goals in uncertain and unpredictable conditions.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Notes

  1. This assumes no self-sacrifice or other extreme solutions.

Abbreviations

AI:

Artificial intelligence

AMSE:

Active mission success estimation

CDF:

Cumulative distribution function

DRV:

Daily recommended value

EMU:

Extravehicular mobility units

EVA:

Extravehicular activity

FBED:

Functional basis for engineering design

FFD:

Referred to as functional flow diagrams

FFIP:

Failure flow identification and propagation

ISRU:

In situ resource utilization

IVA:

Intra-vehicular activities

PDM:

Prognostic-enabled decision making

PHM:

Prognostics and health management

PRA:

Probabilistic risk assessment

SEV:

Surface exploration vehicle

WCA:

Worst case analysis

References

  • Appendix 2. Estimated Calorie Needs per Day, by Age, Sex, and Physical Activity Level-2015–2020 Dietary Guidelines-Health.Gov (2016). http://health.gov/dietaryguidelines/2015/guidelines/appendix-2/. Accessed Apr 5 2016

  • Balaban E, Narasimhan S, Daigle M, Roychoudhury I, Sweet A, Bond C, Gorospe G (2013) Development of a mobile robot test platform and methods for validation of prognostics-enabled decision making algorithms. Int J Prognostics Health Manag 4:1–19

  • Barnes JR (1999) Initiation and Spread of Martian Dust Storms. In: The fifth international conference on Mars, 1:6011. http://adsabs.harvard.edu/abs/1999ficm.conf.6011B

  • Beish J, Former ALPO Senior Mars Recorder (2016) A trend analysis for predicting dust storms on Mars. http://www.alpo-astronomy.org/jbeish/MetTrendDust.htm. Accessed Apr 5 2016

  • Blanchard BS, Fabrycky WJ (1990) Systems Engineering and analysis, vol 4. Prentice Hall Englewood Cliffs, New Jersey

  • Board Mars Climate Orbiter Mishap Investigation (1999) Mars Climate Orbiter Mishap Investigation Board: Phase I Report. Jet Propulsion Laboratory

  • 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 

  • O'Halloran B, 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

  • Bryant CR, Stone RB, McAdams DA, Kurtoglu T, Campbell MI et al (2005) Concept generation from the functional basis of design. In: ICED 05: 15th International conference on engineering design: engineering design and the global economy, 1702. Engineers Australia

  • Cantor B, Malin M, Edgett KS (2002) Multiyear Mars Orbiter Camera (MOC) observations of repeated Martian weather phenomena during the Northern Summer Season. J Geophys Res Planets (1991–2012) 107(E3):3–11

    Google Scholar 

  • Daigle M, Roychoudhury I, Narasimhan S, Saha S, Saha B, Goebel K (2011) Investigating the effect of damage progression model choice on prognostics performance. In: Proceedings of the annual conference of the Prognostics and Health Management Society

  • Do S, Owens A, Ho K, Schreiner S, de Weck O (2016) An independent assessment of the technical feasibility of the Mars One Mission Plan—updated analysis. Acta Astronaut 120(March):192–228. https://doi.org/10.1016/j.actaastro.2015.11.025

  • Environment of Manned Systems (2016). http://history.nasa.gov/conghand/mannedev.htm. Accessed Apr 5 2016

  • Food D, Administration et al (2014) Guidance for industry: a food labeling guide (14. Appendix F: calculate the percent daily value for the appropriate nutrients

  • Friedenthal S, Moore A, Steiner R (2014) A practical guide to SysML: the systems modeling language. Morgan Kaufmann

  • Frost SA, Goebel K, Obrecht L (2013) Integrating structural health management with contingency control for wind turbines. IJPHM Special Issue on Wind Turbine PHM (Color), p 11

  • Gao L, Liu S, Dougal RA (2002) Dynamic lithium-ion battery model for system simulation. IEEE Trans Compon Packag Technol 25(3):495

    Article  Google Scholar 

  • Gleick PH (1996) Basic water requirements for human activities: meeting basic needs. Water Int 21(2):83–92

    Article  Google Scholar 

  • Goddard RH (1920) A method of reaching extreme altitudes. Nature 105(August):809–811. https://doi.org/10.1038/105809a0

    Article  Google Scholar 

  • Goebel K, Saha B, Saxena A, Celaya JR, Christophersen JP (2008) Prognostics in battery health management. IEEE Instrum Meas Mag 11(4):33

    Article  Google Scholar 

  • Greene K, Oremus W (2014) An all-female mission to Mars Slate. http://www.slate.com/articles/health_and_science/space_20/2014/10/manned_mission_to_mars_female_astronauts_are_cheaper_to_launch_into_outer.html?wpsrc=sh_all_dt_tw_top. Accessed Oct 19 2014

  • Herr N, Nicod J-M, Varnier C (2014) Prognostic decision making to extend a platform useful life under service constraint. In: Prognostics and Health Management (PHM), 2014 IEEE conference on IEEE, pp 1–11

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

    Article  Google Scholar 

  • HI-SEAS Mission 3 | Solar System Exploration Research Virtual Institute (2016). http://sservi.nasa.gov/articles/hi-seas-mission-3/. Accessed Apr 4 2016

  • 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 DC, Tumer IY, Kurtoglu T (2008) Modeling the propagation of failures in software driven hardware systems to enable risk-informed design. In: ASME 2008 international mechanical engineering congress and exposition. American Society of Mechanical Engineers, pp 283–293. http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1645006

  • Jensen DC, 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

  • Jones H (2000) Matching crew diet and crop food production in BIO-Plex. No. 2000-01-2397. SAE Technical Paper

  • Keller K, Swearingen K, Sheahan J, Bailey M, Dunsdon J, Wojtek Przytula K, Jordan B (2006) Aircraft electrical power systems prognostics and health management. In: 2006 IEEE conference on aerospace. IEEE, p 12

  • Kumamoto H, Henley EJ (1996) Probabilistic risk assessment and management for engineers and scientists, 2nd edn. IEEE Press, Chicago

  • 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, Campbell MI, Bryant CR, Stone RB, McAdams DA et al (2005) Deriving a component basis for computational functional synthesis. In: ICED 05: 15th international conference on engineering design: engineering design and the global economy, 1687. Engineers Australia

  • 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 

  • 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 

  • MacCallum T, Poynter J, Bearden D (2004) Lessons learned from biosphere 2: when viewed as a ground simulation/analog for long duration human space exploration and settlement. In: International conference on environmental systems

  • Mars Fact Sheet (2016). http://nssdc.gsfc.nasa.gov/planetary/factsheet/marsfact.html. Accessed Apr 5 2016

  • Mimlitz Z, Short AR, 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

  • Modarres M, Kaminskiy M, Krivtsov V (2011) Reliability engineering and risk analysis: a practical guide. CRC Press

  • Mohaghegh Z, Kazemi R, Mosleh A (2009) Incorporating organizational factors into probabilistic risk assessment (PRA) of complex socio-technical systems: a hybrid technique formalization. Reliab Eng Syst Saf 94(5):1000–1018

    Article  Google Scholar 

  • NASA::S&MS::In Situ Resource Utilization (ISRU) Element (2016). http://isru.msfc.nasa.gov/. Accessed Apr 5 2016

  • Nassif SR, Strojwas AJ, Director SW (1986) A methodology for worst-case analysis of integrated circuits. Comput Aided Des Integr Circuits Syst IEEE Trans 5(1):104–113

    Article  Google Scholar 

  • Nathalie HERR, Nicod J-M, Varnier C (2016) Prognostics-based scheduling to extend a distributed platform production horizon under service constraint: model, complexity and resolution. https://hal.archives-ouvertes.fr/hal-01005443/. Accessed Apr 16 2016

  • Nutrition, Center for Food Safety and Applied (2016) Labeling and nutrition—guidance for industry: a food labeling guide (14. Appendix F: Calculate the Percent Daily Value for the Appropriate Nutrients). WebContent. http://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/LabelingNutrition/ucm064928.htm. Accessed Apr 5 2016

  • 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

  • Pecht M (2008) Prognostics and health management of electronics. Wiley

  • Pinto CA, Garvey PR (2012) Advanced risk analysis in engineering enterprise systems. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  • Plante J, Lee B (2005) Environmental conditions for space flight hardware: a survey. http://ntrs.nasa.gov/search.jsp?R=20060013394. Accessed 4 Apr 2016

  • Ramp IJ, Van Bossuyt DL (2014) Toward an automated model-based geometric method of representing function failure propagation across uncoupled systems. In: ASME 2014 International mechanical engineering congress and exposition, V011T14A007–V011T14A007. American Society of Mechanical Engineers

  • Ruf C, Renno NO, Kok JF, Bandelier E, Sander MJ, Gross S, Skjerve L, Cantor B (2009) Emission of non-thermal microwave radiation by a Martian dust storm. Geophys Res Lett. https://doi.org/10.1029/2009GL038715/full

    Google Scholar 

  • Saha B, Goebel K (2007) Battery Data Set, NASA Ames Prognostics Data Repository. NASA Ames, Moffett Field, CA, USA [Online]. http://ti.arc.nasa.gov/project/prognostic-data-repository. Accessed 15 Apr 2016

  • Saha B, Goebel K (2009) Modeling li–ion battery capacity depletion in a particle filtering framework. In: Proceedings of the annual conference of the prognostics and health management society, pp 2909–2924

  • Saha B, Goebel K, Poll S. Christophersen J (2009) Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Trans Instrum Meas 58(2):291–296

    Article  Google Scholar 

  • Saha B, Koshimoto E, Quach CC, Hogge EF, Strom TH, Hill BL, Vazquez SL, Goebel K (2011) Battery health management system for electric UAVs. In: 2011 IEEE conference on aerospace. IEEE, pp 1–9

  • Saha B, Quach CC, Goebel K (2012) Optimizing battery life for electric UAVs using a bayesian framework. In: 2012 IEEE conference on aerospace. IEEE, pp 1–7 

  • Sheppard JW, Kaufman MA, Wilmering TJ (2014) IEEE standards for prognostics and health management

  • Short AR, Van Bossuyt DL (2015) 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 et al (2015) Rerouting failure flows using logic blocks in functional models for improved system robustness: failure flow decision functions. In: DS 80-6 proceedings of the 20th international conference on engineering design (ICED 15) Vol 6: design methods and tools-part 2, Milan, Italy, pp 27-30

  • Short AR, Mimlitz, Van Bossuyt (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

  • Short AR, Lai AD, Van Bossuyt DL (2017) Conceptual design of sacrificial sub-systems: failure flow decision functions. Res Eng Des:1–16

  • 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 

  • Sweet A, Gorospe G, Daigle M, Celaya JR, Balaban E, Roychoudhury I, Narasimhan S (2014) Demonstration of prognostics-enabled decision making algorithms on a hardware mobile robot test platform

  • Upadhyay SK (2010) Common failure distributions. Wiley Encyclopedia of operations research and management science

  • Van Bossuyt DL, Hoyle C (2012) Risk attitudes in risk-based design: considering risk attitude using utility theory in risk-based design. Artif Intell Eng Des Anal Manuf. https://doi.org/10.1017/S0890060412000261

    Google Scholar 

  • Van Bossuyt DL, Dong A (2013) On measuring engineering risk attitudes. J Mech Des. https://doi.org/10.1115/1.4025118

    Google Scholar 

  • Van Bossuyt DL, Tumer IY, Wall SD (2013) A case for trading risk in complex conceptual design trade studies. Res Eng Des 24(3):259–275

    Article  Google Scholar 

  • Weir A (2011) The Martian. Crown Publishing Group, USA

    Google Scholar 

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

    Google Scholar 

  • Widodo A, Shim M-C, Caesarendra W, Yang B-S (2011) Intelligent prognostics for battery health monitoring based on sample entropy. Expert Syst Appl 38(9):11763–11769

    Article  Google Scholar 

  • Xing Y, Williard N, Tsui K-L, Pecht M (2011) A comparative review of prognostics-based reliability methods for lithium batteries. In: Prognostics and system health management conference (PHM-Shenzhen). IEEE, pp 1–6

  • Ye Y (1997) Worst-case analysis. Interior point algorithms: theory analysis, pp 147–177

  • Zhang G, Isom J (2011) Gearbox vibration source separation by integration of time synchronous averaged signals. In: Annual conference of the prognostics and health management society

Download references

Acknowledgements

This research was partially supported by United States Nuclear Regulatory Commission Grant number 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 Robotics, Automation, and Design group at Colorado School of Mines.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bryony DuPont.

Appendices

Appendix 1

Task Duration (sec) Systems used System health factors Resources used Quantity
Sleeping 30,960 Habitat module Time inhabited 30,960 (s) Calories burned ~ 8.5 (kcal/kg) × Astronaut weight (kg)
Physical intensity 0.5/10
Eat food 7200 Habitat module Time inhabited 7200 (s) Calories burned ~ 2.8 (kcal/kg) × Astronaut Weight (kg)
Physical intensity 1.0/10 Food eaten 3025 (kcal) Gained
Exercise 7200 Habitat module Time inhabited 7200 (s) Calories burned ~ 17.4 (kcal/kg) × Astronaut weight (kg)
Physical intensity 9.5/10
Maintain farm 3600 Farm module Time inhabited 3600 (s) Calories burned ~ 4.4 (kcal/kg) × Astronaut Weight (kg)
Physical intensity 4.5/10 Water used ~ 20 (L/m2 of Crops Being Grown)
Food produced ~ 8.4 (kg/day) At full production
EVA 28,800 Air lock Uses 2 Calories burned ~ 25 (kcal/kg) × Astronaut weight (kg)
EMU Time inhabited 28,800 (s)
Physical intensity 3.0/10
SEV Time inhabited 3600 (s)
Physical intensity 1.7/10
IVA 10,800 Habitat module Time inhabited 10,800 (s) Calories burned ~ 5 (kcal/kg) × Astronaut weight (kg)
Physical intensity 1.3/10

Appendix 2

Sol Hour Radiation Temperature Starvation Exhaustion Injury
0 8.6 1.53E−06 5.09E−80 7.48E−12 9.87E−10 1.00E−09
0 9.6 1.53E−06 6.02E−10 1.25E−08 1.37E−07 8.00E−06
0 13.6 1.53E−06 1.32E−37 1.56E−08 2.33E−07 7.20E−06
0 15.6 1.53E−06 3.63E−19 1.64E−08 1.74E−06 1.00E−08
0 19.6 1.53E−06 1.32E−37 2.04E−08 4.50E−06 7.20E−06
0 24.6 1.53E−06 2.19E−28 1.24E−08 2.67E−05 1.00E−08
1 33.2 1.53E−06 5.09E−80 8.49E−12 9.87E−10 1.00E−09
1 36.2 1.53E−06 6.02E−10 7.96E−09 1.37E−07 1.00E−05
1 37.2 1.53E−06 6.02E−10 8.51E−09 2.33E−07 1.00E−05
1 38.2 1.53E−06 6.02E−10 9.10E−09 3.91E−07 1.00E−05
1 39.2 1.53E−06 6.02E−10 9.73E−09 6.49E−07 1.00E−05
1 40.2 1.53E−06 6.02E−10 1.04E−08 1.07E−06 1.00E−05
1 41.2 1.53E−06 6.02E−10 1.11E−08 1.74E−06 1.00E−05
1 42.2 1.53E−06 6.02E−10 1.19E−08 2.81E−06 1.00E−05
1 43.2 1.53E−06 6.02E−10 1.27E−08 4.50E−06 1.00E−05
1 47.2 1.53E−06 1.32E−37 1.41E−08 7.12E−06 1.00E−08
1 49.2 1.53E−06 3.63E−19 1.82E−08 4.07E−05 2.00E−05
2 57.8 1.53E−06 5.09E−80 1.38E−11 9.87E−10 1.00E−09
2 60.8 1.53E−06 6.02E−10 1.17E−08 1.37E−07 1.00E−05
2 61.8 1.53E−06 6.02E−10 1.25E−08 2.33E−07 1.00E−05
2 62.8 1.53E−06 6.02E−10 1.33E−08 3.91E−07 1.00E−05
2 63.8 1.53E−06 6.02E−10 1.42E−08 6.49E−07 1.00E−05
2 64.8 1.53E−06 6.02E−10 1.52E−08 1.07E−06 1.00E−05
2 65.8 1.53E−06 6.02E−10 1.62E−08 1.74E−06 1.00E−05
2 66.8 1.53E−06 6.02E−10 1.73E−08 2.81E−06 1.00E−05
2 67.8 1.53E−06 6.02E−10 1.85E−08 4.50E−06 1.00E−05
2 71.8 1.53E−06 1.32E−37 2.05E−08 7.12E−06 1.00E−08
2 73.8 1.53E−06 3.63E−19 2.64E−08 4.07E−05 2.00E−05

Appendix 3

Mission Plan

  • Sol-770

    • Crew Alpha Equipment Arrives on Planet

  • Sol 0

    • Crew Alpha Arrives on the Surface

    • Perform EVAs and IVAs to verify critical Martian Surface Habitat functionality

    • Unpack transit vehicle

    • Set up habitat module

  • Sol 1–5

    • Perform EVAs to validate external less critical functions

    • Begin Setup for experimentation and

    • Start farm

  • Sol 6-130

    • Tend to farm

      • Sol 55: Soybeans mature

      • Sol 67: Wheat mature

      • Sol 75: Potatoes mature

      • Sol 125: Sweet potatoes mature

      • Sol 130: Peanuts mature

      • Sol 130: Self-sufficient food source achieved

    • Perform EVAs on regular schedule

    • Perform IVAs on regular schedule

    • Perform Exercise on regular schedule

  • Sol 131–615

    • Perform EVAs on regular schedule

    • Perform IVAs on regular schedule

    • Perform Exercise on regular schedule

  • Sol 616–620

    • Begin verification of Beta Martian Surface Habitat during EVAs

    • Perform IVAs on regular schedule

    • Perform Exercise on regular schedule

  • Sol 621–769

    • Sol 621

      • Begin Farm Beta

    • Tend to Farm Beta

      • Sol 671: Soybeans mature

      • Sol 688: Wheat mature

      • Sol 696: Potatoes mature

      • Sol 746: Sweet potatoes mature

      • Sol 751: Peanuts mature

      • Sol 751: Self-sufficient food source achieved

    • Perform EVAs on regular schedule

    • Perform IVAs on regular schedule

    • Perform Exercise on regular schedule

  • Sol 770

    • Crew Beta Arrives on surface

      • Perform EVAs and IVAs to verify critical habitat functionality

      • Unpack transit vehicle

      • Set up habitat module

  • Sol 771–775

    • Crew Alpha

      • Perform EVAs on regular schedule

      • Perform IVAs on regular schedule

      • Perform Exercise on regular schedule

    • Crew Beta

      • Perform EVAs to validate external less critical functions

      • Begin Setup for experimentation and

  • Sols 776–1050

    • Crew Alpha

      • Perform EVAs on regular schedule

      • Perform IVAs on regular schedule

      • Perform Exercise on regular schedule

    • Crew Beta

      • Perform EVAs on regular schedule

      • Perform IVAs on regular schedule

      • Perform Exercise on regular schedule

  • Sols 1051–1069

    • Crew Alpha

      • Begin Prep for departure

      • Wrap up experiments

      • Perform EVAs to hand off tasks to Beta

      • Prepare habitat Alpha for vacancy

      • Will be used by Crew Gamma

    • Crew Beta

      • Perform EVAs on regular schedule

      • Perform IVAs on regular schedule

      • Perform Exercise on regular schedule

  • Sol 1070

    • Crew Alpha Departs from Martian Surface

    • End AMSE analysis

  • Sol 1540

    • Crew Gamma arrives and moves into habitat Alpha

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Short, AR., Hodge, R.D.D., Van Bossuyt, D.L. et al. Active mission success estimation through functional modeling. Res Eng Design 29, 565–588 (2018). https://doi.org/10.1007/s00163-018-0285-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00163-018-0285-8

Keywords

  • Risk
  • Functional modeling
  • Decision making
  • Mission success