A generic grid interface for parallel and adaptive scientific computing. Part I: abstract framework

The intermittent production of the renewable energy imposes the necessity to temporarily store it. Large amounts of exceeding electricity can be stored in geological strata in the form of hydrogen. The conversion of hydrogen to electricity and vice versa can be performed in electrolyzers and fuel elements by chemical methods. The nowadays technical solution accepted by the European industry consists of injecting small concentrations of hydrogen in the existing storages of natural gas. The progressive development of this technology will finally lead to the creation of underground storages of pure hydrogen. Due to the low viscosity and low density of hydrogen, it is expected that the problem of an unstable displacement, including viscous fingering and gravity overriding, will be more pronounced. Additionally, the injection of hydrogen in geological strata could encounter chemical reactivity induced by various species of microorganisms that consume hydrogen for their metabolism. One of the products of such reactions is methane, produced from Sabatier reaction between H 2 and CO 2 . Other hydrogenotrophic reactions could be caused by acetogenic archaea, sulfate-reducing bacteria and iron-reducing bacteria. In the present paper, a mathematical model is presented which is capable to reflect DuMuX was used to model the evolution of a hypothetical underground storage of hydrogen. We have revealed that the behavior of an underground hydrogen storage is different than that of a natural gas storage. Both, the hydrodynamic and the bio-chemical effects, contribute to the different characteristics.

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“…DuMuX is a DUNE [3] [11]. For spatial discretization, the Box-method (vertex-centered finite volume method [20]) was used.…”

Section: Numerical Implementationmentioning

confidence: 99%

Hydrogenization of underground storage of natural gas

et al. 2015

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“…All implementation was done using the Distributed and Unified Numerics Environment (DUNE), see [49,50,51,52]. For the results shown in this section we will use the true error in an energy norm as error measure:…”

Section: Numerical Experimentsmentioning

confidence: 99%

The localized reduced basis multiscale method for two‐phase flows in porous media

Kaulmann¹,

Flemisch

Haasdonk³

et al. 2014

Numerical Meth Engineering

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In this work, we propose a novel model order reduction approach for twophase flow in porous media by introducing a formulation in which the mobility, which realizes the coupling between phase saturations and phase pressures, is regarded as a parameter to the pressure equation. Using this formulation, we introduce the Localized Reduced Basis Multiscale method to obtain a low-dimensional surrogate of the high-dimensional pressure equation. By applying ideas from model order reduction for parametrized partial differential equations, we are able to split the computational effort for solving the pressure equation into a costly offline step that is performed only once and an inexpensive online step that is carried out in every time step of the two-phase flow simulation, which is thereby largely accelerated. Usage of elements from numerical multiscale methods allows us to displace the computational intensity between the offline and online step to reach an ideal runtime at acceptable error increase for the two-phase flow simulation.

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“…Lots of papers discuss special aspects in one step in detail. An example is description of the implementation of the concept of geometric multigrid algorithms and hierarchical local grid refinement [1]. Those kinds of paper depict in each case not the whole simulation (preprocessing, solving, and postprocessing phase).…”

Section: Related Workmentioning

confidence: 99%

“…Because of their importance, understanding quality of these algorithms have attracted several research projects. However, most of them focus on subareas, for instance, optimizing matrix solver [1] or other performance aspects [2]. FEM algorithms, in general, consume or produce input data (e.g.…”

Section: Introductionmentioning

confidence: 99%

On Analyzing Quality of Data Influences on Performance of Finite Elements Driven Computational Simulations

Reiter

Truong

Dustdar

et al. 2012

Euro-Par 2012 Parallel Processing

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Abstract. For multi-scale simulations, the quality of the input data as well as the quality of algorithms and computing environments will strongly impact the intermediate results, the final outcome, and the performance of the simulation. To date, little attention has been paid on understanding the impact of quality of data (QoD) on such multi-scale simulations. In this paper, we present a critical analysis of how QoD influences the results and performance of basic simulation building blocks for multi-scale simulations. We analyze the impact of QoD for Finite Element Method (FEM) based simulation building blocks, and study the dependencies between the QoD of input data and results as well as the performance of the simulation. We devise and implement novel QoD metrics for data intensive, FEM-based simulations and show experiments with real-world applications by demonstrating how QoD metrics can be efficiently used to control and tune the execution of FEM-based simulation at runtime.

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A generic grid interface for parallel and adaptive scientific computing. Part I: abstract framework

Cited by 295 publications

References 1 publication

Hydrogenization of underground storage of natural gas

Hydrogenization of underground storage of natural gas

The localized reduced basis multiscale method for two‐phase flows in porous media

On Analyzing Quality of Data Influences on Performance of Finite Elements Driven Computational Simulations

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