Comparison of Reduced-Basis techniques for the model order reduction of parametric incompressible fluid flows

German, Péter; Tano, Mauricio; Ragusa, Jean C.; Fiorina, Carlo

doi:10.1016/j.pnucene.2020.103551

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Figure 2 shows the geometry and mesh used for the simulations together with the distribution of the power deposition. The mesh was adopted from [34] and contains 16,140 cells. For every scenario in this work, this distribution is considered to be fixed.…”

Section: Resultsmentioning

confidence: 99%

“…The computation of these coefficients from reduced forms of eddy-viscosity models is not widely utilized by the fluid dynamics ROM community; therefore, a non-intrusive, data-based approach-Radial Basis Function (RBF) interpolation-was chosen in this work. The effectiveness of this method has been demonstrated in [19][20][21]34]. This approach interpolates coefficients c η t and c α t within the parameter space and time using the snapshots as anchor nodes.…”

Section: Training Reduced-order Modelsmentioning

confidence: 99%

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Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media

German

Tano

Fiorina

et al. 2021

Fluids

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This work presents a data-driven Reduced-Order Model (ROM) for parametric convective heat transfer problems in porous media. The intrusive Proper Orthogonal Decomposition aided Reduced-Basis (POD-RB) technique is employed to reduce the porous medium formulation of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations coupled with heat transfer. Instead of resolving the exact flow configuration with high fidelity, the porous medium formulation solves a homogenized flow in which the fluid-structure interactions are captured via volumetric flow resistances with nonlinear, semi-empirical friction correlations. A supremizer approach is implemented for the stabilization of the reduced fluid dynamics equations. The reduced nonlinear flow resistances are treated using the Discrete Empirical Interpolation Method (DEIM), while the turbulent eddy viscosity and diffusivity are approximated by adopting a Radial Basis Function (RBF) interpolation-based approach. The proposed method is tested using a 2D numerical model of the Molten Salt Fast Reactor (MSFR), which involves the simulation of both clean and porous medium regions in the same domain. For the steady-state example, five model parameters are considered to be uncertain: the magnitude of the pumping force, the external coolant temperature, the heat transfer coefficient, the thermal expansion coefficient, and the Prandtl number. For transient scenarios, on the other hand, the coastdown-time of the pump is the only uncertain parameter. The results indicate that the POD-RB-ROMs are suitable for the reduction of similar problems. The relative L2 errors are below 3.34% for every field of interest for all cases analyzed, while the speedup factors vary between 54 (transient) and 40,000 (steady-state).

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Section: Resultsmentioning

confidence: 99%

Section: Training Reduced-order Modelsmentioning

confidence: 99%

Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media

German

Tano

Fiorina

et al. 2021

Fluids

Self Cite

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“…Further work [59] gives a technical road map in reduced-order modeling of parameterized multi-group diffusion k-eigenvalue problems, application of proper generalized decomposition to multigroup neutron diffusion eigenvalue calculations can be found in [60]. Comparison of Reduced-Basis techniques for the model order reduction of parametric incompressible fluid flows is shown in [61]. These approaches are referred to as intrusive ROM, in which one has to access the complicated nuclear simulation codes.…”

Section: Introductionmentioning

confidence: 99%

Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Gong

Cheng

Chen

et al. 2022

Nuclear Science and Engineering

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This paper proposes an approach that combines reduced-order models with machine learning in order to create physics-informed digital twins to predict high-dimensional output quantities of interest, such as neutron flux and power distributions in nuclear reactor cores. The digital twin is designed to solve forward problems given input parameters, as well as to solve inverse problems given some extra measurements. Offline, we use reduced-order modeling, namely, the proper orthogonal decomposition (POD) to assemble physics-based computational models that are accurate enough for fast predictive digital twin. The machine learning techniques, namely, k-nearest-neighbors (KNN) and decision trees (DT) are used to formulate the input-parameterdependent coefficients of the reduced basis, whereafter the high-fidelity fields are able to be reconstructed. Online, we use the real time input parameters to rapidly reconstruct the neutron field in the core based on the adapted physics-based digital twin. The effectiveness of the framework is illustrated through a real engineering problem in nuclear reactor physics -reactor core simulation in the life cycle of HPR1000 governed by the two-group neutron diffusion equations affected by input parameters, i.e., burnup, control rod inserting step, power level and temperature of the coolant, which shows potential applications for on-line monitoring purpose.

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