On the treatment of high-frequency issues in numerical simulation for dynamic systems by model order reduction via the proper orthogonal decomposition

Deokar, Rohit R.; Shimada, Masao; Lin, Chia-Yen; Tamma, Kumar K.

doi:10.1016/j.cma.2017.07.003

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…The above is an effect that is observed in highly oscillatory multi-body dynamic systems, and is due to the fact that the ODE23tb and the LTI are implicit and explicit schemes, respectively [52,53]. Specifically, it is known that as an intrinsic property of implicit schemes such as the ODE23tb, damping may be introduced in high frequency oscillations present in the solution in order to maintain stability, in a process that is referred to as under-relaxation [47].…”

Section: Predicted Torque At Individual Crankshaft Locationsmentioning

confidence: 99%

Systematic Investigation of a Large Two-Stroke Engine Crankshaft Dynamics Model

et al. 2020

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The crankshaft dynamics model is of vital importance to a multitude of aspects on engine diagnostics; however, systematic investigations of such models performance (especially for large two-stroke diesel engines that are widely used in the power generation and shipping industries) have not been reported in the literature. This study aims to cover this gap by systematically investigating the parameters that affect the performance of a two-stroke diesel engine crankshaft dynamics model, such as the numerical scheme as well as the engine components inertia and friction. Specifically, the following alternatives are analysed: (a) two optimal performing numerical schemes, in particular, a stiff ordinary differential equations (ODE) solver and a fast solver based on a piecewise Linear Time-Invariant (LTI) scheme method, (b) the linear and the non-linear inertia-speed approaches, and (c) three engine friction submodels of varying complexity. All the potential combinations of the alternatives are investigated, and the crankshaft dynamics model performance is evaluated by employing Key Performance Indicators (KPIs), which consider the results accuracy compared to the measured data, the computational time, and the energy balance error. The results demonstrate that the best performing combination includes the stiff ODE solver, the constant inertia-speed approach and the most simplistic engine friction submodel. However, the LTI numerical scheme is recommended for applications that require fast response due to the significant savings in computational time with an acceptable compromise in the model results accuracy.

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Section: Predicted Torque At Individual Crankshaft Locationsmentioning

confidence: 99%

Systematic Investigation of a Large Two-Stroke Engine Crankshaft Dynamics Model

et al. 2020

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“…, called POD modes (POM) and POD values (POV) respectively. The total energy e contained in the relevant data can be computed by e total = ∑ i λ i and The energy captured by the first m POM is given by (Deokar et al, 2017;Kerschen et al, 2005;Volkwein, 2013):…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

“…In dynamic particulate mechanics simulations, high-frequency modes may lead to the spurious numerical results and also cause stability and convergence issues in explicit scheme (Deokar et al, 2017). In the case where concurrent multiscale coupling takes place in a handshake domain, the high frequent component may destabilize the numerical simulations and must be eliminated in the transition zone (Badia et al, 2007;Chamoin et al, 2010;Sun et al, 2017;Sun and Mota, 2014).…”

Section: Application 1: Pod For Eliminating High-frequency Issues In mentioning

confidence: 99%

“…There exists a substantial amount of literature on the numerical dissipation scheme algorithms (Fung, 2003;Hulbert and Chung, 1996;Maheo et al, 2013). However, numerical damping also causes energy dissipation that affects the accuracy of the simulations (Deokar et al, 2017;Maheo et al, 2013). Instead of proposing another ad hoc damping model that may inevitably cause non-physical energy dissipation, we propose a procedure that leverages the POD to resolve the high-frequency issue while using an energy-preserving time integrator [cf.…”

Section: Application 1: Pod For Eliminating High-frequency Issues In mentioning

confidence: 99%

“…The procedure of the POD-based approach is similar to the one proposed for dynamic finite element simulations first proposed by Deokar et al (2017). The step-by-step procedure is in the following.…”

Section: Pod-based Model Reduction Approachmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Reduced-Dimensional Discrete Element Model for Dynamic Responses of Granular Materials With High-Frequency Noises

Zhong

Sun

2018

Int J Mult Comp Eng

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We present a dimensional-reduction framework based on proper orthogonal decomposition (POD) for the nondissipative explicit dynamic discrete element method (DEM) simulations. Through Galerkin projection, we introduce a finite dimensional space with lower number of degree of freedoms such that the discrete element simulations are not only faster but also free of high-frequency noises. Since this method requires no injection of artificial or numerical damping, there is no need to tune damping parameters. The suppression of high-frequency responses allows a larger time step for faster explicit integration. To capture the highly nonlinear behaviors due to particle rearrangement, an automatic mode-update scheme is formulated such that the most efficient basis can be used to predict mechanical responses. Numerical examples including the wave propagation simulations and uniaxial extension and compression tests are used to demonstrate the capacity of the reduced-order model.

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Parametric Nonlinear Model Reduction Using Machine Learning on Grassmann Manifold with an Application on a Flow Simulation

Sukuntee,

Chaturantabut

2024

J Nonlinear Sci

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On the treatment of high-frequency issues in numerical simulation for dynamic systems by model order reduction via the proper orthogonal decomposition

Cited by 11 publications

References 9 publications

Systematic Investigation of a Large Two-Stroke Engine Crankshaft Dynamics Model

Systematic Investigation of a Large Two-Stroke Engine Crankshaft Dynamics Model

An Adaptive Reduced-Dimensional Discrete Element Model for Dynamic Responses of Granular Materials With High-Frequency Noises

Parametric Nonlinear Model Reduction Using Machine Learning on Grassmann Manifold with an Application on a Flow Simulation

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