Sensitivity Analysis and Uncertainty Quantification for Systems Engineering with DaVinci

Most often, decisions made during early phases of systems design and acquisition determine the majority of the life-cycle costs for those systems. Physics-based, high fidelity models that can support rapid analysis (minutes to hours) and rapid design (hours to days) would improve the quality of early acquisition decisions. But, mathematical and engineering modeling must discard some elements of the real world, leading to uncertainty in the model. Quantifying this uncertainty, as well as an understanding of the driving input variables through sensitivity analysis is essential for credible, responsible engineering. The DaVinci software product is being developed in direct response to these needs. DaVinci is designed around a unified life-cycle engineering model encompassing multi-fidelity analysis for a wide range of applications. At its core, DaVinci provides next generation modeling capability for functional analysis, alternative design evaluation, trade-space exploration, decision making, and acquisition planning. The DaVinci infrastructure and architecture will enable a collaborative environment for all aspects of early acquisition processes and provide a much more effective mechanism for transferring detailed models and product descriptions between phases of acquisition throughout the life of the program. DaVinci couples a rich graphical user interface with pre-engineered system components and large scale computing to allow systems engineers and acquisition stakeholders the use of computationally based engineering to enable rapid system engineering development iterations for requirements traceability, sensitivity analysis and uncertainty propagation, physics-based systems representations, and the creation of multi-fidelity models suitable for early preliminary design.

[1]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[2]  Mark M. Meerschaert,et al.  Mathematical Modeling , 2014, Encyclopedia of Social Network Analysis and Mining.

[3]  J. C. Toomay Radar Principles for the Non-Specialist , 1989 .

[4]  Maxwell Blair,et al.  Air Vehicle Enviroment in C++: A Computational Design Environment for Conceptual Innovations , 2010, J. Aerosp. Comput. Inf. Commun..

[5]  Maxwell Blair,et al.  The Development of a Geometry Engine with Analytic Sensitivities , 2012 .

[6]  Mathematical Modeling: Teaching the Open-ended Application of Mathematics , 2001 .

[7]  Iwo Białynicki-Birula,et al.  Modeling Reality: How Computers Mirror Life , 2004 .

[8]  Douglass E. Post,et al.  A new DoD initiative: the Computational Research and Engineering Acquisition Tools and Environments (CREATE) program , 2008 .

[9]  David K. Barton Radar system analysis , 1976 .

[10]  Michael S. Eldred,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's reference manual. , 2010 .

[11]  Wright-Patterson Afb,et al.  SCOOT: Sensitivity Class with Overloaded Operator Types , 2010 .

[12]  Gregory L. Roth Decision making in systems engineering: The foundation , 2007, 2007 International Symposium on Collaborative Technologies and Systems.

[13]  Wright-Patterson Afb,et al.  The Application of the MISTC Framework to Structural Design Optimization , 2005 .

[14]  Maxwell Blair,et al.  AVEC: A Computational Design Framework for Conceptual Innovations , 2006 .

[15]  David J. Pannell,et al.  Sensitivity Analysis of Normative Economic Models: Theoretical Framework and Practical Strategies , 1997 .

[16]  Jess Marcum,et al.  A statistical theory of target detection by pulsed radar , 1948, IRE Trans. Inf. Theory.