scholarly journals Reduced Order Models for Nonlinear Dynamic Analysis With Application to a Fan Blade

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Mikel Balmaseda ◽  
G. Jacquet-Richardet ◽  
A. Placzek ◽  
D.-M. Tran
Keyword(s):  
Normal Modes ◽  
Nonlinear Dynamic Analysis ◽  
Evaluation Procedure ◽  
Reduced Order Models ◽  
Reduced Basis ◽  
Reduced Order ◽  
Stiffness Evaluation ◽  
Fan Blade ◽  
Proper Orthogonal ◽  
Good Agreement

Abstract In this work, reduced order models (ROM) that are independent from the full order finite element models (FOM) considering geometrical nonlinearities are developed and applied to the dynamic study of a fan. The structure is considered to present nonlinear vibrations around the prestressed equilibrium induced by rotation enhancing the classical linearized approach. The reduced nonlinear forces are represented by a polynomial expansion obtained by the stiffness evaluation procedure (STEP) and then corrected by means of a proper orthogonal decomposition (POD) that filters the full order nonlinear forces (StepC ROM). The linear normal modes (LNM) and Craig-Bampton (C-B) type reduced basis are considered here. The latter are parameterized with respect to the rotating velocity. The periodic solutions obtained with the StepC ROM are in good agreement with the solutions of the FOM and are more accurate than the linearized ROM solutions and the STEP ROM. The proposed StepC ROM provides the best compromise between accuracy and time consumption of the ROM.

scholarly journals Reduced Order Models for Nonlinear Dynamic Analysis With Application to a Fan Blade

2019 ◽  
Author(s):  
Mikel Balmaseda ◽  
G. Jacquet-Richardet ◽  
A. Placzek ◽  
D.-M. Tran
Keyword(s):  
Normal Modes ◽  
Nonlinear Dynamic Analysis ◽  
Evaluation Procedure ◽  
Reduced Order Models ◽  
Reduced Basis ◽  
Reduced Order ◽  
Stiffness Evaluation ◽  
Fan Blade ◽  
Proper Orthogonal ◽  
Good Agreement

Abstract In the present work reduced order models (ROM) that are independent from the full order finite element models (FOM) considering geometrical non linearities are developed and applied to the dynamic study of a fan. The structure is considered to present nonlinear vibrations around the pre-stressed equilibrium induced by rotation enhancing the classical linearised approach. The reduced nonlinear forces are represented by a polynomial expansion obtained by the Stiffness Evaluation Procedure (STEP) and then corrected by means of a Proper Orthogonal Decomposition (POD) that filters the full order nonlinear forces (StepC ROM). The Linear Normal Modes (LNM) and Craig-Bampton (C-B) type reduced basis are considered here. The latter are parametrised with respect to the rotating velocity. The periodic solutions obtained with the StepC ROM are in good agreement with the solutions of the FOM and are more accurate than the linearised ROM solutions and the STEP ROM. The proposed StepC ROM provides the best compromise between accuracy and time consumption of the ROM.

scholarly journals Geometrically nonlinear autonomous reduced order model for rotating structures

2018 ◽  
Author(s):  
Mikel Balmaseda ◽  
Georges Jacquet-Richardet ◽  
Antoine Placzek ◽  
Duc-Minh Tran
Keyword(s):  
Normal Modes ◽  
Harmonic Balance Method ◽  
Reduced Order Model ◽  
Static Equilibrium ◽  
Evaluation Procedure ◽  
Element Formulation ◽  
Geometrically Nonlinear ◽  
Reduced Basis ◽  
Order Model ◽  
Reduced Order

In the present work, as an extension to [2], an autonomous geometrically nonlinear reduced order model for the study of dynamic solutions of complex rotating structures is developed. In opposition to the classical finite element formulation for geometrically nonlinear rotating structures that considers small linear vibrations around the static equilibrium, nonlinear vibrations around the pre-stressed equilibrium are now considered. For that purpose, the linear normal modes are used as a reduced basis for the construction of the reduced order model. The stiffness evaluation procedure method (STEP) [4] is applied to compute the nonlinear forces induced by the displacements around the static equilibrium. This approach enhances the classical linearised small perturbations hypothesis to the cases of large displacements around the static pre-stressed equilibrium. Furthermore, a comparison between the steady solution given by HHT-α [1] and the Harmonic Balance Method (HBM) [3] is carried out. The proposed reduced order models are evaluated for a rotating beam case study.

Reduced order models for nonlinear dynamic analysis of structures with intermittent contacts

2017 ◽  
Vol 24 (12) ◽  
pp. 2591-2604 ◽  
Author(s):  
Stefano Zucca ◽  
Bogdan I Epureanu
Keyword(s):  
Boundary Conditions ◽  
Contact Area ◽  
Complex Geometry ◽  
Numerical Test ◽  
Normal Modes ◽  
Nonlinear Dynamic Analysis ◽  
Forced Response ◽  
Reduced Order Models ◽  
Frequency Range ◽  
Reduced Order

The forced response of structures with complex geometry and intermittent contacts is nonlinear due to contact breathing phenomena that occur during vibration. Therefore, calculation times to predict such responses can be extremely long especially because highly refined finite element models are necessary to properly model geometrically complicated structures. To alleviate this issue, reduced order models can be very beneficial as they can dramatically speed up the analysis process by reducing the size of the system and thus the calculation times. In this paper, a reduced order modeling method for the forced response of structures with intermittent contacts is developed. The proposed approach assumes that the dynamics of the nonlinear system in the frequency range of interest is spatially correlated. The spatial correlation can be dominated by normal modes of the open (or sliding) linear system. Nonetheless, the boundary conditions of a vibrating structure with intermittent frictionless contacts vary in time (i.e. at any time t the contacts are partly open and closed), and the actual extent of the contact area changes over time. Here, this observation is complemented by the assumption that, given the frequency range of the harmonic excitation force, the system dynamics is dominated by one of the modes of either the open or the sliding system, and thus the time evolution of the contact area can be estimated by knowing (a) the normal penetration at the contact node pairs at rest due to pre-stress, and (b) the vector of normal relative displacements at the contact nodes of that dominant mode. As a result, a set of normal modes – referred to as piecewise linear modes – is computed, by imposing special boundary conditions at the nodes lying on the contact surfaces, and a reduction basis is selected and used to reduce the size of the model of the system. Two numerical test cases, specifically a cracked plate and two coaxial cylinders are used for validation. Results show that the proposed method allows to accurately compute the nonlinear forced response of structures with intermittent contacts both in case of zero gap and in case of initial pre-stress at the contacts.

Comparison of two model reduction approaches of an industrial drying process

2021 ◽  
Vol 69 (8) ◽  
pp. 667-682
Author(s):  
Marc Oliver Berner ◽  
Martin Mönnigmann
Keyword(s):  
Dynamic Models ◽  
Orthogonal Decomposition ◽  
Wood Chips ◽  
Residual Water ◽  
Balanced Truncation ◽  
Reduced Order Models ◽  
Drying Process ◽  
Reduced Order ◽  
Proper Orthogonal ◽  
Many Particles

Abstract Dynamic models have proven to be helpful for determining the residual water content in combustible biomass. However, these models often require partial differential equations, which render simulations impracticable when several thousand particles need to be considered, such as in the drying of wood chips. Reduced-order models help to overcome this problem. We compare proper orthogonal decomposition (POD) based to balanced truncation based reduced-order models. Both reduced models are lean enough for an application to systems with many particles, but the model based on balanced truncation shows more accurate results.

scholarly journals Constrained reduced-order models based on proper orthogonal decomposition

2017 ◽  
Vol 321 ◽  
pp. 18-34 ◽  
Author(s):  
Sohail R. Reddy ◽  
Brian A. Freno ◽  
Paul G.A. Cizmas ◽  
Seckin Gokaltun ◽  
Dwayne McDaniel ◽  
...  
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Proper Orthogonal

scholarly journals Construction of reduced-order models for fluid flows using deep feedforward neural networks

2019 ◽  
Vol 872 ◽  
pp. 963-994 ◽  
Author(s):  
Hugo F. S. Lui ◽  
William R. Wolf
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Fluid Flows ◽  
Leading Edge ◽  
Feedforward Neural Networks ◽  
Orthogonal Decomposition ◽  
Reduced Order Models ◽  
Order Model ◽  
Sparse Regression ◽  
Reduced Order ◽  
Proper Orthogonal

We present a numerical methodology for construction of reduced-order models (ROMs) of fluid flows through the combination of flow modal decomposition and regression analysis. Spectral proper orthogonal decomposition is applied to reduce the dimensionality of the model and, at the same time, filter the proper orthogonal decomposition temporal modes. The regression step is performed by a deep feedforward neural network (DNN), and the current framework is implemented in a context similar to the sparse identification of nonlinear dynamics algorithm. A discussion on the optimization of the DNN hyperparameters is provided for obtaining the best ROMs and an assessment of these models is presented for a canonical nonlinear oscillator and the compressible flow past a cylinder. Then the method is tested on the reconstruction of a turbulent flow computed by a large eddy simulation of a plunging airfoil under dynamic stall. The reduced-order model is able to capture the dynamics of the leading edge stall vortex and the subsequent trailing edge vortex. For the cases analysed, the numerical framework allows the prediction of the flow field beyond the training window using larger time increments than those employed by the full-order model. We also demonstrate the robustness of the current ROMs constructed via DNNs through a comparison with sparse regression. The DNN approach is able to learn transient features of the flow and presents more accurate and stable long-term predictions compared to sparse regression.

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