Numerical Model Reduction and Error Control for Computational Homogenization of Transient Problems
Doctoral thesis, 2021
In this thesis the concept of Numerical Model Reduction (NMR) is applied for reducing the RVE problems by constructing a reduced spatial basis using Spectral Decomposition (SD) and Proper Orthogonal Decomposition. Computational homogenization of two different transient model problems have been studied: heat flow and consolidation. In both cases the RVE problem reduces to a system of ordinary differential equations, with dimension much smaller than of the finite element system.
With the reduced basis and decreased computational time comes also loss of accuracy. Thus, in order to assess results from a reduced computation, it is useful to quantify the error. This thesis focuses solely on estimation of the error stemming from the reduced basis by assuming the fully resolved finite element solution to be exact, thereby ignoring e.g. time- and space-discretization errors. For the linear model problems guaranteed, fully computable, bounds are derived for the error in (i) a constructed "energy" norm and (ii) a user-defined quantity of interest within the realm of goal-oriented error estimation. In the non-linear case approximate, fully computable, bounds are derived based on the linearized error equation.
In all cases an associated (non-physical) symmetrized variational problem in space-time is introduced as a "driver" for the estimate. From this residual-based estimates with low computational cost are obtained. In particular, no extra modes than the ones used for the reduced basis approximation are required. The performance of the estimator is demonstrated with numerical examples, and, for both the heat flow problem and the poroelastic problem, the error is overestimated by an order of magnitude, which is deemed acceptable given that the estimate is fully explicit and the extra cost is negligible.
Chalmers, Industrial and Materials Science, Material and Computational Mechanics
A posteriori error estimation for numerical model reduction in computational homogenization of porous media
International Journal for Numerical Methods in Engineering,; Vol. 121(2020)p. 5350-5380
On error controlled numerical model reduction in FE2-analysis of transient heat flow
International Journal for Numerical Methods in Engineering,; Vol. 119(2019)p. 38-73
Numerical model reduction with error control in computational homogenization of transient heat flow
Computer Methods in Applied Mechanics and Engineering,; Vol. 326(2017)p. 193-222
Combining spectral and POD modes to improve error estimation of numerical model reduction for porous media
Computational Mechanics,; Vol. 69(2022)p. 767-786
Efficient Two-Scale Modeling of Porous Media Using NumericalModel Reduction with Fully Computable Error Bounds
Current Trends and Open Problems in Computational Mechanics,; (2022)p. 121-129
Numerical Model Reduction with error estimation for computational homogenization of non-linear consolidation
Computer Methods in Applied Mechanics and Engineering,; Vol. 389(2022)
Even though computers become more powerful every year there are situations where it is not possible to carry out this type of simulation. One example of such a demanding application is the detailed analysis of transport processes in concrete. At a first glance concrete appears to be a homogeneous material, however, when looking closer it becomes clear that this is not the case and performing a detailed simulation will require lots of computational resources. In order to remedy this problem, the concept of multiscale modeling has been developed where the material is analyzed at different scales. In this case the original expensive simulation is split up into many, less demanding, simulations: one simulation for the macroscopic behavior, and many small simulations for the microscopic behavior.
This thesis investigates methods for further reducing the computational cost of simulating all of the microscopic problems. As point of departure, the simulations are established using the finite element method, where the sought solution fields are discretized using a finite, but large, set of degrees of freedom. In order to reduce the computation cost, numerical model reduction is employed, whereby characteristic response modes of the solution are identified. Each simulation can thereby be carried out using a much smaller number of degrees of freedom, and thus at a reduced computational cost. While such an approximation results in faster simulations it also introduces an error in the answer. An important part of this thesis has therefore been to develop methods for quantifying this error to make sure that the accuracy of the approximation is within acceptable bounds.
Numerisk modellreduktion vid beräkningsbaserad homogenisering av deformation och strömning i porösa medier
Swedish Research Council (VR) (2015-05422), 2016-01-01 -- 2019-12-31.
Numerical model reduction for computational homogenization of polycrystals
Swedish Research Council (VR) (2019-05080), 2020-01-01 -- 2023-12-31.
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4974
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Opponent: Professor Ludovic Chamoin, LMT (Laboratory of Mechanics and Technology), ENS Paris-Saclay, France