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Research Papers: Design Theory and Methodology

Modeling Methods and Conceptual Design Principles for Reconfigurable Systems

[+] Author and Article Information
Afreen Siddiqi

Department of Aeronautics & Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139siddiqi@mit.edu

Olivier L. de Weck

Engineering Systems Division, Department of Aeronautics & Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139deweck@mit.edu

J. Mech. Des 130(10), 101102 (Sep 10, 2008) (15 pages) doi:10.1115/1.2965598 History: Received April 26, 2007; Revised March 04, 2008; Published September 10, 2008

Reconfigurable systems can attain different configurations at different times thereby altering their functional abilities. Such systems are particularly suitable for specific classes of applications in which their ability to undergo changes easily can be exploited to fulfill new demands, allow for evolution, and improve survivability. This paper identifies the main factors that drive the need for reconfigurability and proposes methods for modeling reconfigurable systems. A survey of 33 different reconfigurable systems is also presented to provide broader insights and general design guidelines for reconfigurable systems.

Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Properties enabled by reconfigurability

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Figure 2

Stages in life cycle of a reconfigurable system

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Figure 3

Number of journal articles published with the keyword “reconfig” in the title or abstract (Ref. 1)

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Figure 4

Object-process diagrams of reconfigurable systems: (a) OPD of a reconfigurable system; (b) OPD of a morphing UAV

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Figure 5

NextGen MXF-1 morphing UAV (17)

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Figure 6

Markov model of a generic reconfigurable system

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Figure 7

Markov model of operational and reconfiguration states

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Figure 8

Markov model of reconfigurable rover

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Figure 9

Rover operational state probabilities

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Figure 10

Markov model of reconfigurable rover with a failure state

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Figure 11

Operational and reconfiguration state probabilities

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Figure 12

Effect of 10% change in self-transition probabilities of reconfiguration states

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Figure 13

Markov model of a reconfigurable unmanned aerial vehicle

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Figure 14

Evolution of UAVs state probabilities

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Figure 15

Effect of 20% increase and decrease in failure probability over time

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Figure 16

Generic reconfigurable system

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Figure 17

Online and off-line reconfigurations

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Figure 18

Reconfigurability drivers for systems in three application domains

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Figure 19

Reconfiguration time ratios

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Figure 20

Self-similar architecture in reconfigurable systems (31)

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Figure 21

Reconfigurable and traditional measurement systems

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Figure 22

Reconfigurable satellite transponder

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Figure 23

Reconfigurable system design process

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