Systems Engineering Advancement Research Initiative

Keywords engineering research research initiative systems engineering systems engineering advancement research
Standards groups

Engineering Systems

As the operational environment of engineering systems
has become increasingly characterized by disturbances
which asymmetrically degrade performance, so too has
the need for system engineering methodologies that
assure system success in the presence of hostile
environments. Fulfillment of this need is further
complicated by the emergence of networked systems
that, while often geographically distributed, are
interdependent and often exhibit nonlinear failure
modes. Examples of impulse events triggering
catastrophic losses include the tragic events and
economic fallout of September 11th, 2001, the Northeast
Blackout of 2003, and the devastation of Hurricane
Katrina on New Orleans. More recently, China’s
successful test of an anti-satellite (Asat) weapon against
an aging Chinese Feng Yun 1C weather satellite on
January 11, 2007, has incited calls for enhancing the
survivability of US national security space architecture.

Ongoing research at SEAri aims to develop and test an
architecting methodology for the development of highly
survivable systems. Traditionally specified as a
requirement in military systems, survivability is an
increasingly important attribute of all systems which
must be robust to environments characterized by
frequent disturbances. While disturbances may originate
from a wide range of man-made and natural hostile
environments, a universal challenge confronting system
architects is the specification, development,
procurement, operation, and maintenance of systems
with critical survivability requirements.

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Research Bulletin
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The SEAri research portfolio is designed to advance the
theories, methods, and effective practice of systems
engineering applied to complex socio-technical systems
through a collaborative research approach across four
focus areas. The first area is socio-technical decision
making, which seeks to improve the ability to make
effective decisions under conditions of high complexity
and uncertainty.

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