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RISS 인기검색어
- 검색키워드
- 저자 : Andy Keane (검색결과 3 건)
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Robust design optimization using surrogate models
Andy J. Keane,Ivan I. Voutchkov 한국CDE학회 2020 Journal of computational design and engineering Vol.7 No.1
The use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up design search and optimization in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with uncertainty quantification of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. Here, we tackle the problem of robust design optimization from the direction of Gaussian process models (Kriging). We contrast two previously studied models, co-Kriging and combined Kriging (sometimes called level 1 Kriging), and propose a new combined approach called combined co-Kriging that attempts to make best use of the key ideas present in these methods.
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(Re-) Meshing using interpolative mapping and control point optimization
Voutchkov, Ivan,Keane, Andy,Shahpar, Shahrokh,Bates, Ron Society for Computational Design and Engineering 2018 Journal of computational design and engineering Vol.5 No.3
This work proposes a simple and fast approach for re-meshing the surfaces of smooth-featured geometries prior to CFD analysis. The aim is to improve mesh quality and thus the convergence and accuracy of the CFD analysis. The method is based on constructing an interpolant based on the geometry shape and then mapping a regular rectangular grid to the shape of the original geometry using that interpolant. Depending on the selected interpolation algorithm the process takes from less than a second to several minutes. The main interpolant discussed in this article is a Radial Basis Function with cubic spline basis, however other algorithms are also compared. The mesh can be optimized further using active (flexible) control points and optimization algorithms. A range of objective functions are discussed and demonstrated. The difference between re-interpolated and original meshes produces a metric function which is indicative of the mesh quality. It is shown that the method works for flat 2D surfaces, 3D surfaces and volumes.
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(Re-) Meshing using interpolative mapping and control point optimization
Ivan Voutchkov,Andy Keane,Shahrokh Shahpar,Ron Bates 한국CDE학회 2018 Journal of computational design and engineering Vol.5 No.3
This work proposes a simple and fast approach for re-meshing the surfaces of smooth-featured geome-tries prior to CFD analysis. The aim is to improve mesh quality and thus the convergence and accuracy of the CFD analysis. The method is based on constructing an interpolant based on the geometry shape and then mapping a regular rectangular grid to the shape of the original geometry using that interpolant. Depending on the selected interpolation algorithm the process takes from less than a second to several minutes. The main interpolant discussed in this article is a Radial Basis Function with cubic spline basis, however other algorithms are also compared. The mesh can be optimized further using active (flexible) control points and optimization algorithms. A range of objective functions are discussed and demon-strated. The difference between re-interpolated and original meshes produces a metric function which is indicative of the mesh quality. It is shown that the method works for flat 2D surfaces, 3D surfaces and volumes.
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