Results 11 to 20 of about 1,914,780 (311)
Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model
Yuanzineng kexue jishu, 2023 In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
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Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation
Fluids, 2018 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-
Xuping Xie +2 more
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An Artificial Compression Reduced Order Model [PDF]
SIAM Journal on Numerical Analysis, 2020 We propose a novel artificial compression, reduced order model (AC-ROM) for the numerical simulation of viscous incompressible fluid flows. The new AC-ROM provides approximations not only for velocity, but also for pressure, which is needed to calculate forces on bodies in the flow and to connect the simulation parameters with pressure data. The new AC-
Victor P. DeCaria +4 more
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Reduced-order modeling of hidden dynamics [PDF]
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016 5 pages, 2 ...
Héas, Patrick, Herzet, Cédric
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Reduced-order modelling numerical homogenization [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2014 A general framework to combine numerical homogenization and reduced-order modelling techniques for partial differential equations (PDEs) with multiple scales is described. Numerical homogenization methods are usually efficient to approximate the effective solution of PDEs with multiple scales.
Abdulle Assyr, Bai Yun
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Experimental Study of the Transient Behavior of a Wind Turbine Wake Following Yaw Actuation
Energies, 2023 Wind tunnel experiments were performed to investigate the response of a wind turbine model immersed in a replicated atmospheric boundary layer to dynamic changes in the yaw angle.
Derek Micheletto +2 more
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Error estimation of a proper orthogonal decomposition reduced model of a permanent magnet synchronous machine [PDF]
, 2014 Model order reduction methods, like the proper orthogonal decomposition (POD), enable to reduce dramatically the size of a finite element (FE) model.
MAC, Hung +2 more
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Verifiability of the Data-Driven Variational Multiscale Reduced Order Model
, 2022 International audienceIn this paper, we focus on the mathematical foundations of reduced order model (ROM) closures. First, we extend the verifiability concept from large eddy simulation to the ROM setting.
Wang, Zhu +5 more
core +1 more source
Proper general decomposition (PGD) for the resolution of Navier–Stokes equations [PDF]
, 2011 In this work, the PGD method will be considered for solving some problems of fluid mechanics by looking for the solution as a sum of tensor product functions. In the first stage, the equations of Stokes and Burgers will be solved. Then, we will solve the
ALLERY, Cyrille +5 more
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Proper Generalized Decomposition method for incompressible Navier–Stokes equations with a spectral discretization [PDF]
, 2013 http://dx.doi.org/10.1016/j.amc.2013.02.022Proper Generalized Decomposition (PGD) is a method which consists in looking for the solution to a problem in a separate form.
ALLERY, Cyrille +2 more
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