Identification of reduced order average linear models from nonlinear dynamic simulations

Standard riser global dynamic analysis software packages utilize line element models that cannot capture the complex behavior of flexible risers. This paper presents a computationally efficient nonlinear dynamic analysis methodology capable of incorporating detailed finite element models and scalable to global dynamic simulations of entire flexible riser systems. Subject methodology captures the global geometric nonlinear effects and its coupling to stick-slip friction — a clear requirement for accurate armour stress predictions. In addition, the method enables the formulation of stress transformation matrices which allow the direct recovery of armour stresses from the global simulations. A demonstration problem involving the nonlinear dynamic simulation of a 500m flexible riser system is presented.

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Reduced-Order, Trajectory Piecewise-Linear Models for Nonlinear Computational Fluid Dynamics

34th AIAA Fluid Dynamics Conference and Exhibit ◽

10.2514/6.2004-2329 ◽

2004 ◽

Cited By ~ 12

Author(s):

David Gratton ◽

Karen Willcox

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Linear Models ◽

Piecewise Linear ◽

Reduced Order ◽

Piecewise Linear Models

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Feedback Control Methods for a Single Machine Infinite Bus System

10.31224/osf.io/hn2pj ◽

2020 ◽

Author(s):

Pratik Vernekar ◽

Zhongkui Wang ◽

Andrea Serrani ◽

Kevin Passino

Keyword(s):

Single Machine ◽

Linear Models ◽

Linear Quadratic Regulator ◽

Pole Placement ◽

High Fidelity ◽

Nonlinear Feedback ◽

Linear Quadratic ◽

Reduced Order ◽

System P ◽

Single Machine Infinite Bus

In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

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A Nonlinear Dynamic Reduced-Order Model for a Large Gas Turbine Outer Casing Low Cycle Fatigue Prediction

10.1115/gtindia2021-76056 ◽

2021 ◽

Author(s):

Aditya Dubey ◽

Rishi Relan ◽

Uwe Lohse ◽

Jaroslaw Szwedowicz

Keyword(s):

Finite Element Analysis ◽

Finite Element ◽

Gas Turbine ◽

Nonlinear Dynamic ◽

Low Cycle Fatigue ◽

Reduced Order Model ◽

Cycle Fatigue ◽

Order Model ◽

Element Analysis ◽

Reduced Order

Abstract The secondary stresses that result from nonlinear and transient thermal gradients during the start-up and shut down of the large gas turbine engines drive low-cycle fatigue at specific locations of the outer casing. Typical service inspection of the outer casing is primarily based on finite element analysis estimates, considering various safety factors. However, as finite element analysis includes the worst possible combination of loading scenarios and operating conditions any engine may encounter in actual operation, this results in a conservative estimation of the service interval. Therefore, a generic preventive maintenance plan for the whole fleet often underutilises the casing capability and added cost. Hence, this paper proposes a data-driven nonlinear dynamic reduced-order model developed using the temperature data from low-cycle fatigue critical casing locations, ramp rates, and the percentage load of operation to predict the stresses. As a result, a reduced-order model can assess the damage for low-cycle fatigue critical locations in real-time using the operational data and propose an appropriate service intervention plan for each casing in a fleet.

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Piecewise Linear Models for Nonlinear Dynamic Systems

Frequenz ◽

10.1515/freq.1988.42.2-3.71 ◽

1988 ◽

Vol 42 (2-3) ◽

Cited By ~ 2

Author(s):

Jochen Jess

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽

Linear Models ◽

Piecewise Linear ◽

Nonlinear Dynamic Systems ◽

Piecewise Linear Models

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Nonlinear Dynamic Analysis of Cantilever Beam Using POD Based Reduced Order Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.786.398 ◽

2015 ◽

Vol 786 ◽

pp. 398-403 ◽

Cited By ~ 1

Author(s):

Kulkarni Atul Shankar ◽

Manoj Pandey

Keyword(s):

Dynamic Analysis ◽

Large Deformation ◽

Cantilever Beam ◽

Nonlinear Dynamic ◽

Degrees Of Freedom ◽

Numerical Technique ◽

Nonlinear Dynamic Analysis ◽

Reduced Order Model ◽

Order Model ◽

Reduced Order

In this paper, a reduced order model is obtained for nonlinear dynamic analysis of a cantilever beam. Nonlinearity in the system is basically due to large deformation. A reduced order model is an efficient method to formulate low order dynamical model which can be obtained from data obtained from numerical technique such as finite element method (FEM). Nonlinear dynamical models are complex with large number of degrees of freedom and hence, are computationally intensive. With formulation of reduced order models (i.e. Macromodels) number of degrees of freedom are reduced to fewer degrees of freedom by using projection based method like Galerkin’s projection, so as to make system computationally faster and cost effective. These macromodels are obtained by extracting global basis functions from fully meshed model runs. Macromodels are generated using technique called proper orthogonal decomposition (POD) which gives good linear fit for the nonlinear systems. Using POD based macromodel, response of system can be computed using fewer modes instead of considering all modes of system. Macromodel is generated to obtain the response of cantilever beam with large deformation and hence, simulation time is reduced by factor of 90 approximately with error of order of 10-4. Further, method of POD based reduced order model is aplied to beam with different loading conditions to check the robustness of the macromodel. POD based macromodel response gives good agreement with FEA model response for a cantilever beam.

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