the university of texas at el paso college of engineering


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  • Presented to Fourth General Assembly Cartagena Network of Engineering CNE September 22nd, 2010, Metz, France Dr. Ricardo Pineda Chair Industrial, Manufacturing & Systems Engineering Department Director Research Institute for Manufacturing & Engineering Systems http://rimes.utep.eduhttp://rimes.utep.edu and Engineering Systems Research Institute for Manufacturing Understanding Engineered Complex Systems of Systems
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  • Agenda Who Are we? Why Complex System of Systems (CxSoS)? Systems Systems of Systems CxSoS Research Challenges Conclusions
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  • UTEP Roots 1 st degrees offered Engineer of Mines (1914) TSSMM Engineer of Mines (1914) TSSMM B.S. in Mining Engineering (1919) TCMM B.S. in Mining Engineering (1919) TCMM The only Doctoral/Research Intensive University with a Mexican-American majority student population College of Engineering
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  • 5 UTEP Doctoral Enrollment Growth
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  • College Programs MS PROGRAMS Civil Engineering Computer Science Computer Engineering Electrical Engineering Environmental Engineering Systems Engineering Information Technology (M.I.T.) Manufacturing Engineering Mechanical Engineering Metallurgical & Materials Engineering Industrial Engineering PhD PROGRAMS Civil Engineering Computer Engineering Computer Science Environmental Science & Engineering Materials Science & Engineering Manufacturing Engineering (under TCB Approval) UNDERGRADUATE PROGRAMS/DEPARTMENTS
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  • College at a Glance: 2008-2009 Enrollment: 2323 BS, 368 MS, 124 PhD Graduates: 258 BS, 114 MS, 16 PhD 80+ Faculty $17 M Research Expenditures ($35 Research Revenues) $16.5 M total endowments
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  • Major Contributor to Diverse Workforce UTEP College of Engineering is the: #1 producer of Hispanic American BS #1 producer of Hispanic American BS #4 producer of Hispanic American MS #4 producer of Hispanic American MS #2 producer of Hispanic American PhD #2 producer of Hispanic American PhD #1 Graduate School for Hispanics (Hispanic Business Magazine)
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  • Current Research Centers Center for Transportation Infrastructure Systems (CTIS) W.M. Keck Center for 3D Innovation Research Institute for Manufacturing and Engineering Systems (RIMES) Cyber ShARE Center for Excellence CSER Center for Structural Integrity of Aerospace Systems
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  • RIMES Objectives To foster total systems-level thinking across Colleges & Industry. To address applications oriented SoSE research areas. To advance multi-disciplinary educational programs (UG/G). To research/publish advances in SE emerging technologies and practices. To stimulate the adoption of standards and best SE practices within industries.
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  • Areas of Research MPT for Integrated Development SoS Formal Requirements Methodologies MBSE & Modeling Languages (UML, SysML) Application of SOA to SoS (Next Gen NCS) Trade-off studies Lean/Enterprise SE SoS Reliability (Prognostics, Resilience) SoS Risk Analysis
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  • Applied Research Health Care Delivery Systems: UMC/TT Energy Systems: US Army, Raytheon, EPE Sustainability: USDA(2) Knowledge: GDC KMS
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  • Challenges of the 21 st Century Engineering Challenges According to NAE: Renewable Energy Renewable Energy Water Conservation Water Conservation Environment Protection Environment Protection Global Warming Global Warming Sustainability Sustainability Improve medicine and healthcare delivery Improve medicine and healthcare delivery Reducing vulnerability to human and natural threats Reducing vulnerability to human and natural threats Expand and enhance human capability and happiness Expand and enhance human capability and happiness Grand Challenges and Engineering Systems, Charles M. Vest, CESU, MIT, 2009
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  • What do the challenges have in common? Open Systems Socio-Political implications Trans-disciplinary Science & Engineering efforts Self Organization Knowledge Emergence Required ECOSYSTEMS THEY ARE ALL ECOSYSTEMS
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  • Systems Engineering Attributes Linear decomposition Hierarchical Structure Design (logical, behavior, physical) Architectural Synthesis Predetermined outputs and behavior Emergence not allowed Environment compliance Attribute Behavior Logical Physical Single Design Source: Systems Analysis, Design, and Development, Charles S. Wasson, Wiley-Interscience 2006
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  • SE Complementary approach
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  • SoS Engineering n Stakeholders n Attributes n Capabilities n Behaviors n Governances n N Interface Agreement SOS Interface Agreement SOS Inter- Agent Outer- Agent Inner- Agent
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  • SOS Attributes Operational Independence of the Elements Managerial Independence of the Elements Evolutionary Development Incremental Knowledge Some Emergent Behavior Geographic Distribution Source: Sheard Sarah, Third Millennium Systems, LLC
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  • From Attributes to Functions Attribute 1 Att Capability n Capability 1 Function n Function 1 Measure n Measure 1
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  • SOS Approach
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  • ENGINEERED SOS Legal Social Environment Ethical Semi-Closed Privacy
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  • CxSOS Defined? Systems Science: Systems Science: It is an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the non- linear interactions of microscopic elements Systems Engineering View: Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous formation of distinctive temporal, spatial or functional structures [Springer Complexity] Social Science: Social Science: The crucial point of Complex Systems approach is that from a macroscopic point of view the development of political, social, or cultural order is not only the sum of single intentions, but the collective result of non- linear interactions [REF]
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  • ENGINEERED CxSOS Emergence of Knowledge Self Organization Privacy Legal Social Environment Ethical Autonomy OPEN
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  • CxSOS Attributes Attributes (NCSI, 2009)*** Environmental Awareness Environmental Awareness Mutual Sustainment Mutual Sustainment Autonomy Autonomy Self organizing Self organizing Emergent macro-level behavior Emergent macro-level behavior Trust Trust Attributes (Dagli/Ergin, 2009)* LTP impossible Unexpected changes Patterns & ST predictability Evolutionary *Source: Systems of Systems Architecting, C.H. Dagli, N. Kilicay-Ergin, in Systems of Systems Engineering, Wiley Series in Systems Engineering. 2009 Attributes/(Sheard, 2009)** Non-linear behavior (initial condition) Non-linear behavior (initial condition) Non-predictable Non-predictable May evolve from order to chaos May evolve from order to chaos Self-organized Self-organized Whole may be greater than sum of the parts Whole may be greater than sum of the parts Emergent Adaptive behavior Emergent Adaptive behavior Ecosystem more fit as it becomes more connected Ecosystem more fit as it becomes more connected **Source: Principles of Complex Systems for Systems Engineering, S. Sheard, A. Mostashari, Systems Engineering Vol. 12, No 4, 2009 ***Source: Net-Centric Services Framework, V2.1, Net-Centric Operations Industry Consortium, 2009
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  • Process Interface Adaptive Knowledge & Autonomy?? Attribute Criteria Knowledge Domain Interface Hardware Layer Software Layer Process Service Application Governance
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  • Importance of End-to-End Systems Thinking Challenges more and more trans-disciplinary Enterprise-centric to end-user centric To design for optimizing desired objectives To predict design attributes of the Systems that are not inherent in the blocks (The whole is greater than the sum of its parts) To design for dynamic Stakeholder Requirements changes (flexibility, modularity, re-usability, etc.)
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  • Educational Challenges Charles M. Vest has observed Charles M. Vest has observed the engineering world is disappearing. Making universities and engineering schools exciting, creative, adventurous, rigorous, demanding, and empowering environments is more important than specifying curricular details. From Casual Newtonian decomposition Analysis to Non- linear analysis Trans-disciplinary and experiential learning: team environment (increased organization/ people connectivity)
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  • Research CHALLENGES What is the interconnection structure to optimize the objective of CxSOS? How does organizational connectivity affect knowledge emergence and what are the processes to maximize them? Are there any Adaptation laws to evolve functions of the overall CxSOS? Is Knowledge Emergence dictated or limited by Interface agreement? Can new methods avoid unpredictable emergent behavior? V&V Methodo


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