This paper summarises the results of a benchmark study that compares a number of mathematical and numerical models applied to specific problems in the context of carbon dioxide (CO 2 ) storage in geologic formations. The processes modelled comprise ad-H. Class (B) · A. Ebigbo · R. Helmig · M. Darcis · B. Flemisch vective multi-phase flow, compositional effects due to dissolution of CO 2 into the ambient brine and nonisothermal effects due to temperature gradients and the Joule-Thompson effect. The problems deal with leakage through a leaky well, methane recovery enhanced P. Audigane BRGM, French Geological Survey, 410 Comput Geosci (2009) 13:409-434 by CO 2 injection and a reservoir-scale injection scenario into a heterogeneous formation. We give a description of the benchmark problems then briefly introduce the participating codes and finally present and discuss the results of the benchmark study.

Pore-scale models are becoming increasingly useful as predictive tools for modeling flow and transport in porous media. These models can accurately represent the 3D pore-structure of real media. Currently first-principles modeling methods are being employed for obtaining qualitative and quantitative behavior. Generally, artificial, simple boundary conditions are imposed on a model that is used as a stand-alone tool for extracting macroscopic parameters. However, realistic boundary conditions, reflecting flow and transport in surrounding media, may be necessary for behavior that occurs over larger length scales or including pore-scale models in a multiscale setting. Here, pore-scale network models are coupled to adjacent media (additional pore-scale or continuum-scale models) using mortars. Mortars are 2D finite-element spaces employed to couple independent subdomains by enforcing continuity of pressure and flux at shared boundary interfaces. While mortars have been used in the past to couple subdomains of different models, physics, and meshes, they are extended here for the first time to pore-scale models. The approach is demonstrated by modeling single-phase flow in coupled pore-scale models, but the methodology can be utilized to model dynamic processes and perform multiscale modeling in 3D continuum simulators for flow and transport.

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It is well known that surface of ZnO acts both as a reactant and a catalytic reaction template in rubber vulcanization by activating and bringing together reactants. The particles of accelerators, fatty acid, and sulfur diffuse through the polymer matrix and get adsorbed on the surface of ZnO, forming intermediate complexes.Hence dispersion of ZnO in the elastomer matrix is a determining parameter. Capping is one of the novel techniques for increasing ZnO-stearic acid/accelerator interaction, thereby enhancing their activities. During the sol-gel precipitation of nano ZnO, if a suitable capping agent is added, agglomeration of ZnO particles gets reduced, leading to the formation of nano particles. Since only very few studies are found reported on synthesis of acceleratorcapped ZnO and its application in rubber vulcanization, attempts have been made in this study to synthesize our novel accelerator N-benzylimine aminothioformamide (BIAT)-capped-stearic acid-coated nano ZnO (ZOBS), and BIAT-capped ZnO (ZOB) to investigate their effects in natural rubber (NR) vulcanization. Efforts have also been made to synthesize stearic acid-coated nano zinc phosphate (ZPS) with an aim to find an alternative to conventional ZnO in vulcanization. Mechanical properties like tensile strength, tear resistance, abrasion resistance, and compression set were found out. Swelling values of the vulcanizates as a measure of crosslink densities were also determined. Optimum dosage of ZOBS and a combination of ZOB and ZPS were also derived and found that capped ZnO is superior in NR vulcanization to conventional ZnO in improving cure properties including scorch safety and mechanical properties.

Previous work in the Instrumented Oil-Field DDDAS project has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This work has led to the implementation of advanced multiphysics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications. The stack implements a Gridbased adaptive execution engine, distributed data management services for real-time data access, exploration, and coupling, and self-managing middleware services for seamless discovery and composition of components, services, and data on the Grid. This paper investigates how these solutions can be leveraged and applied to address another DDDAS application of strategic importance -the data-driven management of Ruby Gulch Waste Repository.

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