The development and operation of any space system face a variety of uncertainties. We have developed a taxonomy that structures such uncertainties. In addition, we have identified the dependencies between them, enabling an evaluation of how uncertainties may be affected when another uncertainty materializes. DOI: http://dx.doi.org/10.2514/6.2012-5195

1. RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
Space systems face multiple types of uncertainties from the design phase
through production, testing, launch, operation and retirement of the
space system that challenge the mission success in multiple dimensions
and aspects. Therefore proper identification, classification, categorization
and management of uncertainties are necessary in understanding the
environment that space systems are embedded and also essential in
identifying the adaptable designs, architectures, or solutions. Given the
ever increasing dynamic environment of current space systems, sources of
uncertainties are considerably diverse and therefore make proper
identification and management a crucial part of design and operation of
adaptable and Flexible Space Systems. This paper aims on a thorough and
holistic taxonomy and categorization of space systems uncertainties for
the purpose of keeping track of uncertainties and facilitate their
prioritization, management, scenario building and appropriate modeling
during the entire life cycle for the purpose of designing Adaptable and
Flexible Space Systems. Several major types of uncertainties were
organized into five major groups including policy, service performance,
organization, technology and market; which are derived from the
stakeholders and mapping the space system context. The taxonomy has
been defined ensuring completeness and coherency. Then various
classification types based on uncertainty dimension, being exogenous or
endogenous, level of complexity and other classification types are
presented. This research also addresses the peculiarities of the space
systems according to their type of mission and customer.
LIMITATIONS OF EXISTING COLLECTIONS
ABSTRACT
TAXONOMY OF UNCERTAINTIES
Dimension classification addresses how the
uncertainty occurs in temporal and physical
dimensions.
DIMENSIONS
UNCERTAINTIES INTER-DEPENDENCIES
UNDERSTANDING MARKET UNCERTAINTIES
ACKNOWLEDGMENTS
Alejandro Salado
Stevens Institute of Techn.
asaladod@stevens.edu
Ph: +49 176 321 31458
Lack of completeness
Uncertainties are identified without following a structured process.
Instead brainstorming or field knowledge are used. Therefore it is not
possible to ensure that the selected uncertainties are the right ones
(validation of the selected uncertainties) or their completeness.
Lack of weighting
Different uncertainties have different effects on the system and therefore
the criticality of their impacts shall be reflected on the trade-offs.
Lack of distinction based on mission type
Impact of uncertainties may be perceived differently depending on the
type of mission.
Lack of distinction based con customer type
Impact of uncertainties may be perceived differently depending on the
type of mission.
Limited to space segment
Segments others that the space segment are not considered in the
categorizations. However, they may have a major contribution to the end
value for money of space systems, particularly during operations.
Mix of different abstraction levels
For example considering high level uncertainties like cost or market and
low level ones like semi-major axis or inclination. Makes it difficult to
measure and use uncertainties at the right level of abstraction. Threatens
the completeness of the uncertainties taken into account.
Lack of organizational uncertainties
Project related organizational aspects can affect the successful completion
of a space system, e.g. leave of key personnel, adequate project
organization, unsuitable supplier selection, etc. are not addressed.
Alejandro Salado, Roshanak Nilchiani, and Mahmoud Efatmaneshnik
Stevens Institute of Technology
Taxonomy and Categorization of Uncertainties in Space Systems with an
Application to the Measurement of the Value of Adaptability
Lack of knowledge: facts that are not known,
or are known only imprecisely.
Lack of definition: unspecified elements.
Statistically characterized phenomena:
elements that cannot be known precisely, but
that can be statistically bounded.
Known unknowns: those that are identified,
but that cannot be reduced beforehand.
Unknown unknowns: emergent behaviors of a
system, i.e. there is no awareness of their
existence until they actually occur.
ROOTS AND SOURCES* OBJECTIVE Vs SUBJECTIVE
Objective uncertainties are those subjected to
rules that remain relatively constant over time,
often follow a statistical probabilistic
distribution of an uncertain physical
phenomenon, and therefore can be studied and
estimated with a high degree of confidence.
Subjective uncertainties are those for which
the rules may change dramatically and
therefore high confidence levels cannot be
reached when estimating them through in-
depth studies.
NATURE: SIMPLE VS COMPLEX
Based on the boundaries of the system under
study.
ENDOGENOUS Vs EXOGENOUS
CONTACT
The present research has been developed
under the DARPA/NASA Ames Contract
Number: NNA11AB35C on the
Fractionated Space Systems F6 project
awarded to the Stevens Institute of
Technology.
Authors would like to thank Dr. Owen
Brown for his ideas and feedback on the
elements presented herein.
* McManus, H., and Hastings, D., "A framework for understanding uncertainty and
its mitigation and exploitation in complex systems," Engineering Management
Review, IEEE , vol.34, no.3, pp.81, Third Quarter 2006.
Simple: the Adaptable response can
potentially resolve the uncertainty
Complex: the adaptable response
creates a new uncertainty profile or
type
Why uncertainty correlation matters?
 Realistic scenarios, realistic options, time to exercise and option
 Trigger possibility, chain reaction effect
Rare catastrophic events in complex systems are poorly probable, yet
highly possible. The collective effect of insignificant uncertainties
may have grave consequences.
Uncertainty
Taxonomy
Policy
Technology
Capability
Market
Service
performance
Is it allowed to build and use the system?
Export, Frequency allocation, Mission-specific
regulations, and disposal
Is it feasible to build and use the system?
Obsolescence, Technology readiness, and
system readiness
Can we build and operate the system?
Supply chain, Cost, Technical capability, Key
people, V&V, Design, Requirements, and
Customer involvement
Does the system operate within the initial
specified performance level?
Reliability, Availability, Debris, Radiation,
Weather hazard, Lifetime, and Performance
Is the system successful?
Market size, Discount rate, Competitor, Market
caputre, and Schedule
Missions
Communications – Navigation –
Earth Observation – Science –
Human Spaceflight
Customers
Commercial
Government
Military
Process to aim at completeness
Top-down: in addition to evaluating the uncertainties
inherent to the system, stakeholders for space systems are
consulted.
Bottom-up: review of uncertainty collections available in
existing literature
Application to the design of adaptable and flexible systems
System level structure and completeness
Behavioral impact description and inter-dependencies
Multi-dimensional importance and objective-based classification
Market
uncert.
Customer type
Commercial Government Military
Market size Size of the market
addressable by the
system.
Scientific community that
could use the system.
Population that could
benefit from the system.
Total amount of military
conflicts.
Discount
rate
Opportunity cost of
capital.
Opportunity cost of
scientific or social revenue.
Opportunity cost of
upgraded military
capacity.
Competitor New competitors
entering the market
while the system is
being developed or
when operational.
Other projects or market
segment getting public /
government interest that
make budget / funding
fluctuate (e.g. budget
moving from Earth
observation to Human
Spaceflight).
Other governmental
agencies. Governments
may transfer funding
between the different
agencies making the
budget/funding
fluctuate.
Market
capture
Actual part of the
market using the
system.
Actual part of the scientific
community using what the
system delivers.
Actual usage by
populations/agencies/etc. of
what the system delivers.
Amount of conflicts
(percentage) where the
system can be used.
Schedule Time to market. Time to bring the system
into operation.
Time to bring the system
into operation.