Model reduction of large-scale dynamical (mechanical) systems A. Antoulas, D. Sorensen, K. Gallivan,...
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Transcript of Model reduction of large-scale dynamical (mechanical) systems A. Antoulas, D. Sorensen, K. Gallivan,...
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Model reduction of large-scaledynamical (mechanical) systems
A. Antoulas, D. Sorensen, K. Gallivan, P. Van Dooren, A. Grama, C. Hoffmann, A. Sameh
Purdue University, Rice University, Florida State University, Université catholique de Louvain
NSF ITR: Model Reduction of Dynamical Systems for Real Time Control
June 7, 2004ICCS’2004, Krakow, Poland
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Research goals or wishful thinking ?
Modeling of mechanical structures
Identification/calibration (cheap sensors)
Simulation/validation (prognosis)
Model reduction
Control (earthquakes, car industry, large flexible structures)
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Passive / Semi-Active Fluid Dampers
Passive fluid dampers contain bearings and oil absorbing seismic energy. Semi-active dampers work with variable orifice damping.
(Picture courtesy Steven Williams)
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Active Mass Damper
Active Mass Damping via control of displacement, velocity or acceleration of a mass (here by a turn-screw actuator).
Eigenvalue analysis showed dominant transversal mode (0.97 Hz) and torsional mode (1.13Hz). A two-mass active damper damps these modes.
(Picture courtesy Bologna Fiere)
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The Future: Fine-Grained Semi-Active Control.
Dampers are based on Magneto-Rheological fluids with viscosity that changes in milliseconds, when exposed to a magnetic field.
New sensing and networking technology allows to do fine-grained real-time control of structures subjected to winds, earthquakes or hazards.
(Pictures courtesy Lord Corp.)
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This technology starts to be applied…
Dongting Lake Bridge has now MR dampers to control wind-induced vibration
(Pictures courtesy of Prof. Y. L. Xu, Hong Kong Poly.)
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Second order system models
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Reduced order model
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Start by simplifying the model …
Simplify by
keeping only concrete
substructure
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and then reduce the state dimension …
i.e. reduce the number of equations describing the “state” of the system
26400 2nd order eqs
20 2nd order eqs
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State space model reduction
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Gramians yield good approximation
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Interpolate with rational Krylov spaces
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Apply this to 2nd order
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Single clamped beam example
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Interpolation of large scale systems
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Error depends on neglected eigenvalues
Hankel singular values dropquickly by a factor >1000 :
Frequency response errorshows same error order
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Structural simulation: case study
Simulate the effects of crashing fluid into reinforced concrete
Model the columns to reproduce the behavior of spirally reinforced columns including the difference in material response of the concrete within and outside the spiral reinforcement.
Fluid modeled by filling of elements in a (moving) grid
IBM Regatta Power4 platform with 8 processors
Model size: 1.2M elements
Run time: 20 hours
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Column Model
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Control = interconnected systems
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Control = interconnected systems
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Interconnected systems ~ 2nd order systems
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Conclusions
Work progress on several fronts
Acquisition of high-rise structural models (Purdue)
Developing novel model reduction techniques and application on the above acquired full models (RICE, FSU, UCL)
Development of sparse matrix parallel algorithms needed for model reduction and simulation (Purdue)
Control via interconnected systems (RICE, FSU, UCL)
Time-varying MOR for calibration/adaptation (RICE, FSU, UCL)