scholarly journals POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder

2017 ◽  
Vol 8 (1) ◽  
pp. 210-236 ◽  
Author(s):  
Giovanni Stabile ◽  
Saddam Hijazi ◽  
Andrea Mola ◽  
Stefano Lorenzi ◽  
Gianluigi Rozza
Keyword(s):  
Circular Cylinder ◽  
Finite Volume ◽  
Vortex Shedding ◽  
Dimensional Space ◽  
Reduced Order Model ◽  
Galerkin Projection ◽  
Governing Equations ◽  
Reduced Basis ◽  
Order Model ◽  
Reduced Order

Abstract Vortex shedding around circular cylinders is a well known and studied phenomenon that appears in many engineering fields. A Reduced Order Model (ROM) of the incompressible ow around a circular cylinder is presented in this work. The ROM is built performing a Galerkin projection of the governing equations onto a lower dimensional space. The reduced basis space is generated using a Proper Orthogonal Decomposition (POD) approach. In particular the focus is into (i) the correct reproduction of the pres- sure field, that in case of the vortex shedding phenomenon, is of primary importance for the calculation of the drag and lift coefficients; (ii) the projection of the Governing equations (momentum equation and Poisson equation for pressure) performed onto different reduced basis space for velocity and pressure, respectively; (iii) all the relevant modifications necessary to adapt standard finite element POD-Galerkin methods to a finite volume framework. The accuracy of the reduced order model is assessed against full order results.

Application of a Reduced Order Model for Fuzzy Analysis of Linear Static Systems

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Marcos A. Valdebenito ◽  
Héctor A. Jensen ◽  
Pengfei Wei ◽  
Michael Beer ◽  
André T. Beck
Keyword(s):  
Dimensional Space ◽  
Reduced Order Model ◽  
Optimization Strategy ◽  
Reduced Basis ◽  
Order Model ◽  
Full System ◽  
Reduced Order ◽  
Fuzzy Analysis ◽  
Α Level ◽  
Static Systems

Abstract This contribution proposes a strategy for performing fuzzy analysis of linear static systems applying α-level optimization. In order to decrease numerical costs, full system analyses are replaced by a reduced order model that projects the equilibrium equations to a small-dimensional space. The basis associated with the reduced order model is constructed by means of a single analysis of the system plus a sensitivity analysis. This reduced basis is enriched as the α-level optimization strategy progresses in order to protect the quality of the approximations provided by the reduced order model. A numerical example shows that with the proposed strategy, it is possible to produce an accurate estimate of the membership function of the response of the system with a limited number of full system analyses.

scholarly journals A higher-order parametric nonlinear reduced-order model for imperfect structures using Neumann expansion

2021 ◽  
Author(s):  
J. Marconi ◽  
P. Tiso ◽  
D. E. Quadrelli ◽  
F. Braghin
Keyword(s):  
Reduced Order Model ◽  
Galerkin Projection ◽  
Order Model ◽  
Displacement Fields ◽  
Reduced Order ◽  
Total Displacement ◽  
Elastic Forces ◽  
Strain Formulation ◽  
The One ◽  
Definition Of

AbstractWe present an enhanced version of the parametric nonlinear reduced-order model for shape imperfections in structural dynamics we studied in a previous work. In this model, the total displacement is split between the one due to the presence of a shape defect and the one due to the motion of the structure. This allows to expand the two fields independently using different bases. The defected geometry is described by some user-defined displacement fields which can be embedded in the strain formulation. This way, a polynomial function of both the defect field and actual displacement field provides the nonlinear internal elastic forces. The latter can be thus expressed using tensors, and owning the reduction in size of the model given by a Galerkin projection, high simulation speedups can be achieved. We show that the adopted deformation framework, exploiting Neumann expansion in the definition of the strains, leads to better accuracy as compared to the previous work. Two numerical examples of a clamped beam and a MEMS gyroscope finally demonstrate the benefits of the method in terms of speed and increased accuracy.

A Reduced Order Model for Multi-Group Time-Dependent Parametrized Reactor Spatial Kinetics

2014 ◽  
Author(s):  
Alberto Sartori ◽  
Davide Baroli ◽  
Antonio Cammi ◽  
Lelio Luzzi ◽  
Gianluigi Rozza
Keyword(s):  
Reduced Order Model ◽  
Time Dependent ◽  
Element Approximation ◽  
Fissile Material ◽  
Spatial Effects ◽  
Reduced Basis ◽  
Order Model ◽  
Control Rod ◽  
Reduced Order ◽  
Group Time

In this work, a Reduced Order Model (ROM) for multi-group time-dependent parametrized reactor spatial kinetics is presented. The Reduced Basis method (built upon a high-fidelity “truth” finite element approximation) has been applied to model the neutronics behavior of a parametrized system composed by a control rod surrounded by fissile material. The neutron kinetics has been described by means of a parametrized multi-group diffusion equation where the height of the control rod (i.e., how much the rod is inserted) plays the role of the varying parameter. In order to model a continuous movement of the rod, a piecewise affine transformation based on subdomain division has been implemented. The proposed ROM is capable to efficiently reproduce the neutron flux distribution allowing to take into account the spatial effects induced by the movement of the control rod with a computational speed-up of 30000 times, with respect to the “truth” model.

scholarly journals Evolve Then Filter Regularization for Stochastic Reduced Order Modeling

2018 ◽  
Author(s):  
Xuping Xie ◽  
Feng Bao ◽  
Clayton G. Webster
Keyword(s):  
Stochastic Systems ◽  
Spatial Filter ◽  
Reduced Order Modeling ◽  
Reduced Order Model ◽  
Galerkin Projection ◽  
Order Model ◽  
Reduced Order ◽  
Stochastic Burgers Equation ◽  
Numerical Stabilization ◽  
Convection Dominated

In this paper, we introduce the evolve-then-filter (EF) regularization method for reduced order modeling of convection-dominated stochastic systems. The standard Galerkin projection reduced order model (G-ROM) yield numerical oscillations in a convection-dominated regime. The evolve-then-filter reduced order model (EF-ROM) aims at the numerical stabilization of the standard G-ROM, which uses explicit ROM spatial filter to regularize various terms in the reduced order model (ROM). Our numerical results based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better results than G-ROM.

Data-driven construction of a reduced-order model for supersonic boundary layer transition

2019 ◽  
Vol 874 ◽  
pp. 1096-1114 ◽  
Author(s):  
Ming Yu ◽  
Wei-Xi Huang ◽  
Chun-Xiao Xu
Keyword(s):  
Compressible Flows ◽  
Incompressible Flows ◽  
Temporal Evolution ◽  
Reduced Order Model ◽  
Boundary Layer Transition ◽  
Galerkin Projection ◽  
Data Driven ◽  
Dynamic Mode ◽  
Order Model ◽  
Reduced Order

In this study, a data-driven method for the construction of a reduced-order model (ROM) for complex flows is proposed. The method uses the proper orthogonal decomposition (POD) modes as the orthogonal basis and the dynamic mode decomposition method to obtain linear equations for the temporal evolution coefficients of the modes. This method eliminates the need for the governing equations of the flows involved, and therefore saves the effort of deriving the projected equations and proving their consistency, convergence and stability, as required by the conventional Galerkin projection method, which has been successfully applied to incompressible flows but is hard to extend to compressible flows. Using a sparsity-promoting algorithm, the dimensionality of the ROM is further reduced to a minimum. The ROMs of the natural and bypass transitions of supersonic boundary layers at $Ma=2.25$ are constructed by the proposed data-driven method. The temporal evolution of the POD modes shows good agreement with that obtained by direct numerical simulations in both cases.

Reduced-order model for laminar vortex-induced vibration of a rigid circular cylinder with an internal nonlinear absorber

2013 ◽  
Vol 18 (7) ◽  
pp. 1916-1930 ◽  
Author(s):  
Ravi Kumar R. Tumkur ◽  
Elad Domany ◽  
Oleg V. Gendelman ◽  
Arif Masud ◽  
Lawrence A. Bergman ◽  
...  
Keyword(s):  
Circular Cylinder ◽  
Reduced Order Model ◽  
Order Model ◽  
Vortex Induced Vibration ◽  
Reduced Order ◽  
Nonlinear Absorber

scholarly journals New approximations, and policy implications, from a delayed dynamic model of a fast pandemic

2020 ◽  
Author(s):  
C. P. Vyasarayani ◽  
Anindya Chatterjee
Keyword(s):  
Numerical Solutions ◽  
Initial Conditions ◽  
Reduced Order Model ◽  
Policy Implications ◽  
Galerkin Projection ◽  
Order Model ◽  
Reduced Order ◽  
Wave Approximation ◽  
Long Wave ◽  
Long Wave Approximation

AbstractWe study an SEIQR (Susceptible-Exposed-Infectious-Quarantined-Recovered) model for an infectious disease, with time delays for latency and an asymptomatic phase. For fast pandemics where nobody has prior immunity and everyone has immunity after recovery, the SEIQR model decouples into two nonlinear delay differential equations (DDEs) with five parameters. One parameter is set to unity by scaling time. The subcase of perfect quarantining and zero self-recovery before quarantine, with two free parameters, is examined first. The method of multiple scales yields a hyperbolic tangent solution; and a long-wave approximation yields a first order ordinary differential equation (ODE). With imperfect quarantining and nonzero self-recovery, the long-wave approximation is a second order ODE. These three approximations each capture the full outbreak, from infinitesimal initiation to final saturation. Low-dimensional dynamics in the DDEs is demonstrated using a six state non-delayed reduced order model obtained by Galerkin projection. Numerical solutions from the reduced order model match the DDE over a range of parameter choices and initial conditions. Finally, stability analysis and numerics show how correctly executed time-varying social distancing, within the present model, can cut the number of affected people by almost half. Alternatively, faster detection followed by near-certain quarantining can potentially be even more effective.

scholarly journals Numerical investigation of the POD reduced-order model for fast predictions of two-phase flows in porous media

2019 ◽  
Vol 29 (11) ◽  
pp. 4167-4204 ◽  
Author(s):  
Jingfa Li ◽  
Tao Zhang ◽  
Shuyu Sun ◽  
Bo Yu
Keyword(s):  
Projection Methods ◽  
Reduced Order Model ◽  
Governing Equations ◽  
Order Model ◽  
Numerical Accuracy ◽  
Two Phase ◽  
Content Type ◽  
Reduced Order ◽  
Computational Speed ◽  
Speed Up

Purpose This paper aims to present an efficient IMPES algorithm based on a global model order reduction method, proper orthogonal decomposition (POD), to achieve the fast solution and prediction of two-phase flows in porous media. Design/methodology/approach The key point of the proposed algorithm is to establish an accurate POD reduced-order model (ROM) for two-phase porous flows. To this end, two projection methods including projecting the original governing equations (Method I) and projecting the discrete form of original governing equations (Method II) are respectively applied to construct the POD-ROM, and their distinctions are compared and analyzed in detail. It is found the POD-ROM established by Method I is inapplicable to multiphase porous flows due to its failed introduction of fluid saturation and permeability that locate on the edge of grid cell, which would lead to unphysical results. Findings By using Method II, an efficient IMPES algorithm that can substantially speed up the simulation of two-phase porous flows is developed based on the POD-ROM. The computational efficiency and numerical accuracy of the proposed algorithm are validated through three numerical examples, and simulation results illustrate that the proposed algorithm displays satisfactory computational speed-up (one to two orders of magnitude) without sacrificing numerical accuracy obviously when comparing to the standard IMPES algorithm that without any acceleration technique. In addition, the determination of POD modes number, the relative errors of wetting phase pressure and saturation, and the influence of POD modes number on the overall performances of the proposed algorithm, are investigated. Originality/value 1. Two projection methods are applied to establish the POD-ROM for two-phase porous flows and their distinctions are analyzed. The reason why POD-ROM is difficult to be applied to multiphase porous flows is clarified firstly in this study. 2. A highly efficient IMPES algorithm based on the POD-ROM is proposed to accelerate the simulation of two-phase porous flows. 3. Satisfactory computational speed-up (one to two orders of magnitude) and prediction accuracy of the proposed algorithm are observed under different conditions.

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