Reduced-Order Model Design for Nonlinear Smart System Models
نویسندگان
چکیده
The use of finite element or finite difference techniques to discretize nonlinear smart material system models can yield full-order numerical models that accurately characterize the system dynamics but do so at significant computational cost. This can preclude the use of these full-order models for uncertainty analysis, sensitivity analysis, system design, or real-time control implementation. In this paper, we discuss the contruction of reduced-order system models using proper orthogonal decompositions (POD) with updates. Through the use of snapshots constructed from the full-order models, fundamental physics is retained while significantly improving efficiency for high-speed implementation.
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