Quadratic partial eigenvalue assignment in large-scale stochastic dynamic systems for resilient and economic design. (May 2016)

• Record Type: Journal Article
• Authors: Das, S.; Goswami, K.; Datta, B.N.
• Is Part Of: Mechanical systems and signal processing. Volume 72/73(2016)
• Page Start: 359
• Page End: 375
• Publication Date: 2016-05
• Subjects: Quadratic partial eigenvalue assignment -- Structural vibrations -- Optimization under uncertainty -- Probability of failure -- Disjointed failure domains -- Importance sampling; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.10.001
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Failure of structural systems under dynamic loading can be prevented via active vibration control which shifts the damped natural frequencies of the systems away from the dominant range of a loading spectrum. The damped natural frequencies and the dynamic load typically show significant variations in practice. A computationally efficient methodology based on quadratic partial eigenvalue assignment technique and optimization under uncertainty has been formulated in the present work that will rigorously account for these variations and result in economic and resilient design of structures. A novel scheme based on hierarchical clustering and importance sampling is also developed in this work for accurate and efficient estimation of probability of failure to guarantee the desired resilience level of the designed system. Finally the most robust set of feedback matrices is selected from the set of probabilistically characterized optimal closed-loop system to implement the new methodology for design of active controlled structures. Numerical examples are presented to illustrate the proposed methodology. Abstract : Highlights: Quadratic partial eigenvalue assignment based active vibration control is used. New optimization under uncertainty framework is formulated for resilient design. Hierarchical clustering technique is used to identify disjointed failure domain. Importance sampling is used to estimate low probability of failure to guarantee resilience. Guidelines for selection of feedback matrices are prescribed for implementation.