183 research outputs found
A p-multigrid method enhanced with an ILUT smoother and its comparison to h-multigrid methods within Isogeometric Analysis
Over the years, Isogeometric Analysis has shown to be a successful
alternative to the Finite Element Method (FEM). However, solving the resulting
linear systems of equations efficiently remains a challenging task. In this
paper, we consider a p-multigrid method, in which coarsening is applied in the
approximation order p instead of the mesh width h. Since the use of classical
smoothers (e.g. Gauss-Seidel) results in a p-multigrid method with
deteriorating performance for higher values of p, the use of an ILUT smoother
is investigated. Numerical results and a spectral analysis indicate that the
resulting p-multigrid method exhibits convergence rates independent of h and p.
In particular, we compare both coarsening strategies (e.g. coarsening in h or
p) adopting both smoothers for a variety of two and threedimensional
benchmarks
Isogeometric Analysis and Harmonic Stator-Rotor Coupling for Simulating Electric Machines
This work proposes Isogeometric Analysis as an alternative to classical
finite elements for simulating electric machines. Through the spline-based
Isogeometric discretization it is possible to parametrize the circular arcs
exactly, thereby avoiding any geometrical error in the representation of the
air gap where a high accuracy is mandatory. To increase the generality of the
method, and to allow rotation, the rotor and the stator computational domains
are constructed independently as multipatch entities. The two subdomains are
then coupled using harmonic basis functions at the interface which gives rise
to a saddle-point problem. The properties of Isogeometric Analysis combined
with harmonic stator-rotor coupling are presented. The results and performance
of the new approach are compared to the ones for a classical finite element
method using a permanent magnet synchronous machine as an example
Machine learning discovery of optimal quadrature rules for isogeometric analysis
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and non-uniform knot sequences, and then the generated optimal rules are used for integration over higher-dimensional spaces using tensor products. The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on adaptive gradient-descent. However, since the optimization space is highly non-convex, the success of the search strongly depends on the number of quadrature points and the parameter initialization. Thus, we use a dynamic programming strategy that initializes the parameters from the optimal solution over the spline space with a lower number of knots. With this method, we found optimal quadrature rules for spline spaces when using IGA discretizations with up to 50 uniform elements and polynomial degrees up to 8, showing the generality of the approach in this scenario. For non-uniform partitions, the method also finds an optimal rule in a reasonable number of test cases. We also assess the generated optimal rules in two practical case studies, namely, the eigenvalue problem of the Laplace operator and the eigenfrequency analysis of freeform curved beams, where the latter problem shows the applicability of the method to curved geometries. In particular, the proposed method results in savings with respect to traditional Gaussian integration of up to 44% in 1D, 68% in 2D, and 82% in 3D spaces.Euskampus Foundation through the ORLEG-IA project in the Misiones Euskampus 2.0 program.
RYC2021-032853-I/MCIN/AEI/10.13039/501100011033 funded by the Spanish Ministry of Science and Innovation and by the European Union NextGenerationEU/PRTR
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