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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.