Towards a Dynamic Data Driven System for Structural and Material Health Monitoring

This paper outlines the initial motivations and implementation scope supporting a dynamic data driven application system for material and structural health monitoring as well as critical event prediction. The dynamic data driven paradigm is exploited to promote application advances, application measurement systems and methods, mathematical and statistical algorithms and finally systems software infrastructure relevant to this effort. These advances are intended to enable behavior monitoring and prediction as well as critical event avoidance on multiple time scales.

[1]  John G. Michopoulos,et al.  Characterization of strain-induced damage in composites based on the dissipated energy density part I. Basic scheme and formulation , 1995 .

[2]  John G. Michopoulos,et al.  Health error prediction and sensor topology optimization on a smart pressure vessel , 1995, Smart Structures.

[3]  C. Farhat,et al.  The second generation FETI methods and their application to the parallel solution of large-scale linear and geometrically non-linear structural analysis problems , 2000 .

[4]  Charbel Farhat,et al.  Three-field-based nonlinear aeroelastic simulation technology - Status and application to the flutter analysis of an F-16 configuration , 2002 .

[5]  Mark N. Glauser,et al.  Towards Practical Flow Sensing and Control via POD and LSE Based Low-Dimensional Tools (Keynote Paper) , 2002 .

[6]  Mark N. Glauser,et al.  Towards practical flow sensing and control via POD and LSE based low-dimensional tools , 2004 .

[7]  Charbel Farhat,et al.  Time‐decomposed parallel time‐integrators: theory and feasibility studies for fluid, structure, and fluid–structure applications , 2003 .

[8]  Ryan F. Schmit,et al.  Improvements in low dimensional tools for flow-structure interaction problems: Using global POD , 2003 .

[9]  Gregory W. Brown,et al.  Application of a three-field nonlinear fluid–structure formulation to the prediction of the aeroelastic parameters of an F-16 fighter , 2003 .

[10]  Michel Lesoinne,et al.  Parameter Adaptation of Reduced Order Models for Three-Dimensional Flutter Analysis , 2004 .

[11]  Divyakant Agrawal,et al.  Medians and beyond: new aggregation techniques for sensor networks , 2004, SenSys '04.

[12]  Leonidas J. Guibas,et al.  Locating and bypassing routing holes in sensor networks , 2004, IEEE INFOCOM 2004.

[13]  Charbel Farhat,et al.  POD-based Aeroelastic Analysis of a Complete F-16 Configuration: ROM Adaptation and Demonstration , 2005 .

[14]  Charbel Farhat,et al.  On a data-driven environment for multiphysics applications , 2005, Future Gener. Comput. Syst..

[15]  Charbel Farhat,et al.  6. A Time-Parallel Implicit Methodology for the Near-Real-Time Solution of Systems of Linear Oscillators , 2007 .