Title:
A Methodology for Capability-Based Technology Evaluation for Systems-of-Systems

dc.contributor.advisor Mavris, Dimitri N.
dc.contributor.author Biltgen, Patrick Thomas en_US
dc.contributor.committeeMember Bishop, Carlee
dc.contributor.committeeMember Brown, David
dc.contributor.committeeMember Loewy, Robert G.
dc.contributor.committeeMember Schrage, Daniel P.
dc.contributor.department Aerospace Engineering en_US
dc.contributor.department Engineering
dc.date.accessioned 2007-05-25T17:24:15Z
dc.date.available 2007-05-25T17:24:15Z
dc.date.issued 2007-03-26 en_US
dc.description.abstract Post-Cold War military conflicts have highlighted the need for a flexible, agile joint force responsive to emerging crises around the globe. The 2005 Joint Capabilities Integration and Development System (JCIDS) acquisition policy document mandates a shift away from stove-piped threat-based acquisition to a capability-based model focused on the multiple ways and means of achieving an effect. This shift requires a greater emphasis on scenarios, tactics, and operational concepts during the conceptual phase of design and structured processes for technology evaluation to support this transition are lacking. In this work, a methodology for quantitative technology evaluation for systems-of-systems is defined. Physics-based models of an aircraft system are exercised within a hierarchical, object-oriented constructive simulation to quantify technology potential in the context of a relevant scenario. A major technical challenge to this approach is the lack of resources to support real-time human-in-the-loop tactical decision making and technology analysis. An approach that uses intelligent agents to create a "Meta-General" capable of forecasting strategic and tactical decisions based on technology inputs is used. To demonstrate the synergy between new technologies and tactics, surrogate models are utilized to provide intelligence to individual agents within the framework and develop a set of tactics that appropriately exploit new technologies. To address the long run-times associated with constructive military simulations, neural network surrogate models are implemented around the forecasting environment to enable rapid trade studies. Probabilistic techniques are used to quantify uncertainty and richly populate the design space with technology-infused alternatives. Since a large amount of data is produced in the analysis of systems-of-systems, dynamic, interactive visualization techniques are used to enable "what-if" games on assumptions, systems, technologies, tactics, and evolving threats. The methodology developed in this dissertation is applied to a notional Long Range Strike air vehicle and system architecture in the context of quantitative technology evaluation for the United States Air Force. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/14520
dc.publisher Georgia Institute of Technology en_US
dc.subject Capability-based acquisition en_US
dc.subject Technology evaluation en_US
dc.subject Neural networks en_US
dc.subject Surrogate models en_US
dc.subject Long range strike en_US
dc.subject Systems-of-systems en_US
dc.subject Constructive simulation en_US
dc.subject Intelligent agents en_US
dc.subject JCIDS en_US
dc.subject FLAMES en_US
dc.subject Multivariate analysis en_US
dc.subject Agent-based modeling en_US
dc.title A Methodology for Capability-Based Technology Evaluation for Systems-of-Systems en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Mavris, Dimitri N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
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