Abstract

A simple method to automatically update the finite element mesh of the analysis domain is proposed. The method considers the mesh as a fictitious elastic body subjected to prescribed displacements at selected boundary points. The mechanical properties of each mesh element are appropriately selected in order to minimize the deformation and the distortion of the mesh elements. Different selection strategies have been used and compared in their application to simple examples. The method avoids the use of remeshing in the solution of shape optimization problems and reduces the number of remeshing steps in the solution of coupled fluid–structure interaction problems

Abstract

A simple method to automatically update the finite element mesh of the analysis domain is proposed. The method considers the mesh as a fictitious elastic body subjected to prescribed displacements at selected boundary points. The mechanical properties of each mesh element are appropriately selected in order to minimize the deformation and the distortion of the mesh elements. Different selection strategies have been used and compared in their application to simple examples. The method avoids the use of remeshing in the solution of shape optimization problems and reduces the number of remeshing steps in the solution of coupled fluid–structure interaction problems

Abstract

A simple method to automatically update the finite element mesh of the analysis domain is proposed for cases when the shape of the domain in modified. The method considers the mesh as a fictitious elastic body subjected to prescribed displacement at selected boundary points. The mechanical properties of each mesh element are appropriately selected in order to minimize the deformation and the distortion of the mesh elements. Different selection strategies have been used of remeshing in the solution of shape optimization problems and reduces the number of the remeshing step in the solution of coupled fluid-structure interaction problems

Abstract

Machine learning entails powerful information processing algorithms that are relevant for modelling, optimization, and control of fluids. Currently, machine-learning capabilities are advancing at an incredible rate, and fluid mechanics is beginning to tap into the full potential of these powerful methods. Many tasks in fluid mechanics, such as reduced-order modelling, shape optimization and uncertainty quantification, may be posed as optimization and regression tasks. Machine learning can dramatically improve optimization performance and reduce convergence time. In this paper, the potential of tree-based machine learning techniques for the aerodynamic prediction of pressure coefficients of an AIRBUS XRF1 aircraft wing-body configuration has been assessed. For this purpose, a dataset including computational fluid dynamics (CFD) simulations has been employed to train the different models, with and without the use of proper orthogonal decomposition (POD) and having their hyperparameters values optimized to obtain the optimal subspace. A deep comparison of decision tree regressors and random forest algorithms has been performed, showing that the random forest regressor model performs better on all configurations.