A SURROGATE MODEL BASED ON PROPER ORTHOGONAL DECOMPOSITION AND RADIAL BASIS FUNCTIONS FOR THE ANALYSIS OF PHASE CHANGE MATERIALS CONTAINING NANOPARTICLES

2019 ◽  
Author(s):  
Bruno Jaccoud ◽  
Helcio Orlande ◽  
Marcelo Colaco ◽  
Ryszard Bialecki ◽  
Zbigniew Buliński ◽  
...  
Keyword(s):  
Phase Change ◽  
Proper Orthogonal Decomposition ◽  
Radial Basis Functions ◽  
Surrogate Model ◽  
Phase Change Materials ◽  
Orthogonal Decomposition ◽  
Basis Functions ◽  
Model Based ◽  
Radial Basis ◽  
Proper Orthogonal

scholarly journals Stochastic Fracture Analysis Using Scaled Boundary Finite Element Methods Accelerated by Proper Orthogonal Decomposition and Radial Basis Functions

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaowei Shen ◽  
Haowen Hu ◽  
Zhongwang Wang ◽  
Xiuyun Chen ◽  
Chengbin Du
Keyword(s):  
Finite Element ◽  
Proper Orthogonal Decomposition ◽  
Radial Basis Functions ◽  
Orthogonal Decomposition ◽  
Basis Functions ◽  
Uncertain Variables ◽  
Computation Efficiency ◽  
Radial Basis ◽  
Proper Orthogonal ◽  
Scaled Boundary Finite Element

This paper presents a stochastic analysis method for linear elastic fracture mechanics using the Monte Carlo simulations (MCs) and the scaled boundary finite element method (SBFEM) based on proper orthogonal decomposition (POD) and radial basis functions (RBF). The semianalytical solutions obtained by the SBFEM enable us to capture the stress intensity factors (SIFs) easily and accurately. The adoption of POD and RBF significantly reduces the model order and increases computation efficiency, while maintaining the versatility and accuracy of MCs. Numerical examples of cracks in homogeneous and bimaterial plates are provided to demonstrate the effectiveness and reliability of the proposed method, where the crack inclination angles are set as uncertain variables. It is also found that the larger the scale of the problem, the more advantageous the proposed method is.

scholarly journals Proper Orthogonal Decomposition as Surrogate Model for Aerodynamic Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Valentina Dolci ◽  
Renzo Arina
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Parameter Space ◽  
Surrogate Model ◽  
Orthogonal Decomposition ◽  
High Fidelity ◽  
Aerodynamic Optimization ◽  
Model Based ◽  
Flow Past ◽  
First Case ◽  
Proper Orthogonal

A surrogate model based on the proper orthogonal decomposition is developed in order to enable fast and reliable evaluations of aerodynamic fields. The proposed method is applied to subsonic turbulent flows and the proper orthogonal decomposition is based on an ensemble of high-fidelity computations. For the construction of the ensemble, fractional and full factorial planes together with central composite design-of-experiment strategies are applied. For the continuous representation of the projection coefficients in the parameter space, response surface methods are employed. Three case studies are presented. In the first case, the boundary shape of the problem is deformed and the flow past a backward facing step with variable step slope is studied. In the second case, a two-dimensional flow past a NACA 0012 airfoil is considered and the surrogate model is constructed in the (Mach, angle of attack) parameter space. In the last case, the aerodynamic optimization of an automotive shape is considered. The results demonstrate how a reduced-order model based on the proper orthogonal decomposition applied to a small number of high-fidelity solutions can be used to generate aerodynamic data with good accuracy at a low cost.

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