Národní úložiště šedé literatury Nalezeno 4 záznamů.  Hledání trvalo 0.01 vteřin. 
Estimation of EDZ zones in great depths by elastic-plastic models
Sysala, Stanislav
This contribution is devoted to modeling damage zones caused by the excavation of tunnels and boreholes (EDZ zones) in connection with the issue of deep storage of spent nuclear fuel in crystalline rocks. In particular, elastic-plastic models with Mohr-Coulomb or Hoek-Brown yield criteria are considered. Selected details of the numerical solution to the corresponding problems are mentioned. Possibilities of elastic and elastic-plastic approaches are illustrated by a numerical example.
Numerical realization of the Bayesian inversion accelerated using surrogate models
Bérešová, Simona
The Bayesian inversion is a natural approach to the solution of inverse problems based on uncertain observed data. The result of such an inverse problem is the posterior distribution of unknown parameters. This paper deals with the numerical realization of the Bayesian inversion focusing on problems governed by computationally expensive forward models such as numerical solutions of partial differential equations. Samples from the posterior distribution are generated using the Markov chain Monte Carlo (MCMC) methods accelerated with surrogate models. A surrogate model is understood as an approximation of the forward model which should be computationally much cheaper. The target distribution is not fully replaced by its approximation. Therefore, samples from the exact posterior distribution are provided. In addition, non-intrusive surrogate models can be updated during the sampling process resulting in an adaptive MCMC method. The use of the surrogate models significantly reduces the number of evaluations of the forward model needed for a reliable description of the posterior distribution. Described sampling procedures are implemented in the form of a Python package.
Reduced basis solver for stochastic Galerkin formulation of Darcy flow with uncertain material parameters
Béreš, Michal
In this contribution, we present a solution to the stochastic Galerkin (SG) matrix equations coming from the Darcy flow problem with uncertain material coefficients in the separable form. The SG system of equations is kept in the compressed tensor form and its solution is a very challenging task. Here, we present the reduced basis (RB) method as a solver which looks for a low-rank representation of the solution. The construction of the RB consists of iterative expanding of the basis using Monte Carlo sampling. We discuss the setting of the sampling procedure and an efficient solution of multiple similar systems emerging during the sampling procedure using deflation. We conclude with a demonstration of the use of SG solution for forward uncertainty quantification.
Užití pružně-plastických modelů při odhadování zón poškození způsobených ražbou hlubinných tunelů
Sysala, Stanislav
Příspěvek se věnuje kontinuálnímu modelování zón poškození způsobených ražbou tunelů a vrtů v souvislosti s problematikou hlubinného ukládání vyhořelého jaderného paliva v krystalických\nhorninách. Pro jednoduchost jsou uvažovány a porovnávány pružné a pružně-plastické přístupy k modelování na základě Mohr-Coulombova nebo Hoek-Brownova kritéria poškození. Pro implementaci byly vytvořeny vlastní kódy v Matlabu a Pythonu s inovativními prvky.