114 research outputs found

    Spatiotemporal adaptive multiscale multiphysics simulations of two-phase flow

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    We present a spatiotemporal adaptive multiscale algorithm, which is based on the Multiscale Finite Volume method. The algorithm offers a very efficient framework to deal with multiphysics problems and to couple regions with different spatial resolution. We employ the method to simulate two-phase flow through porous media. At the fine scale, we consider a pore-scale description of the flow based on the Volume Of Fluid method. In order to construct a global problem that describes the coarse-scale behavior, the equations are averaged numerically with respect to auxiliary control volumes, and a Darcy-like coarse-scale model is obtained. The space adaptivity is based on the idea that a fine-scale description is only required in the front region, whereas the resolution can be coarsened elsewhere. Temporal adaptivity relies on the fact that the fine-scale and the coarse-scale problems can be solved with different temporal resolution (longer time steps can be used at the coarse scale). By simulating drainage under unstable flow conditions, we show that the method is able to capture the coarse-scale behavior outside the front region and to reproduce complex fluid patterns in the front region

    Local-global splitting for spatiotemporal-adaptive multiscale methods

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    We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the standard MsFV approach, we propose to start directly with a local-global splitting that allows to locally retain the original degree of coupling. This is crucial for highly non-linear systems or in the presence of physical instabilities. To obtain an accurate and efficient algorithm, we devise new adaptive criteria for global update that are based on changes of coarse-scale quantities rather than on fine-scale quantities, as it is routinely done before in the adaptive MsFV method. By means of a complexity analysis we show that the adaptive approach gives a noticeable speed-up with respect to the standard MsFV algorithm. In particular, it is efficient in case of large upscaling factors, which is important for multiphysics problems. Based on the observation that local time stepping acts as a smoother, we devise a self-correcting algorithm which incorporates the information from previous times to improve the quality of the multiscale approximation. We present results of multiphase flow simulations both for Darcy-scale and multiphysics (hybrid) problems, in which a local pore-scale description is combined with a global Darcy-like description. The novel spatiotemporal-adaptive multiscale method based on the local-global splitting is not limited to porous media flow problems, but it can be extended to any system described by a set of conservation equations

    A dual-tube model for gas dynamics in fractured nanoporous shale formations

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    Gas flow through fractured nanoporous shale formations is complicated by a hierarchy of structural features (ranging from nanopores to microseismic and hydraulic fractures) and by several transport mechanisms that differ from the standard viscous flow used in reservoir modelling. In small pores, self-diffusion becomes more important than advection; also, slippage effects and Knudsen diffusion might become relevant at low densities. We derive a nonlinear effective diffusion coefficient that describes the main transport mechanisms in shale-gas production. In dimensionless form, this coefficient depends only on a geometric factor (or dimensionless permeability) and on the kinetic model that describes the gas. To simplify the description of the complex structure of fractured shales, we observe that the production rate is controlled by the flow from the shale matrix (which has the smallest diffusivity) into the fracture network, which is assumed to produce instantaneously. Therefore, we propose to model the flow in the shale matrix and estimate the production rate with a simple bundle-of-dual-tubes model (BoDTM), in which each tube is characterized by two diameters (one for transport and the other for storage). The solution of a single tube is approximately self-similar at early time, but not at late time, when the gas flux decays exponentially owing to the finite length of the tube. To construct a BoDTM, a reliable estimate of the joint statistics of the matrix-porosity parameters is required. This can be either inferred from core measurements or postulated on the basis of some a priori assumptions when information from laboratory and field measurements is scarce. By comparison with field production data from the Barnett shale-gas field, we demonstrate that BoDTM can be calibrated to estimate structural parameters of the shale formation and to predict the cumulative production of shale gas. Our framework has enough flexibility to construct models of increasing complexity that can be employed in the presence of a complex dataset or when more information is availabl

    A dual-tube model for gas dynamics in fractured nanoporous shale formations

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    AbstractGas flow through fractured nanoporous shale formations is complicated by a hierarchy of structural features (ranging from nanopores to microseismic and hydraulic fractures) and by several transport mechanisms that differ from the standard viscous flow used in reservoir modelling. In small pores, self-diffusion becomes more important than advection; also, slippage effects and Knudsen diffusion might become relevant at low densities. We derive a nonlinear effective diffusion coefficient that describes the main transport mechanisms in shale-gas production. In dimensionless form, this coefficient depends only on a geometric factor (or dimensionless permeability) and on the kinetic model that describes the gas. To simplify the description of the complex structure of fractured shales, we observe that the production rate is controlled by the flow from the shale matrix (which has the smallest diffusivity) into the fracture network, which is assumed to produce instantaneously. Therefore, we propose to model the flow in the shale matrix and estimate the production rate with a simple bundle-of-dual-tubes model (BoDTM), in which each tube is characterized by two diameters (one for transport and the other for storage). The solution of a single tube is approximately self-similar at early time, but not at late time, when the gas flux decays exponentially owing to the finite length of the tube. To construct a BoDTM, a reliable estimate of the joint statistics of the matrix-porosity parameters is required. This can be either inferred from core measurements or postulated on the basis of somea prioriassumptions when information from laboratory and field measurements is scarce. By comparison with field production data from the Barnett shale-gas field, we demonstrate that BoDTM can be calibrated to estimate structural parameters of the shale formation and to predict the cumulative production of shale gas. Our framework has enough flexibility to construct models of increasing complexity that can be employed in the presence of a complex dataset or when more information is available.</jats:p

    On the Diurnal Soil Water Content Dynamics during Evaporation using Dielectric Methods

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    The water content dynamics in the upper soil surface during evaporation is a key element in land–atmosphere exchanges. Previous experimental studies have suggested that the soil water content increases at the depth of 5 to 15 cm below the soil surface during evaporation, while the layer in the immediate vicinity of the soil surface is drying. In this study, the dynamics of water content profiles exposed to solar radiative forcing was monitored at a high temporal resolution using dielectric methods both in the presence and absence of evaporation. A 4-d comparison of reported moisture content in coarse sand in covered and uncovered buckets using a commercial dielectric-based probe (70 MHz ECH2O-5TE, Decagon Devices, Pullman, WA) and the standard 1-GHz time domain reflectometry method. Both sensors reported a positive correlation between temperature and water content in the 5- to 10-cm depth, most pronounced in the morning during heating and in the afternoon during cooling. Such positive correlation might have a physical origin induced by evaporation at the surface and redistribution due to liquid water fluxes resulting from the temperature-gradient dynamics within the sand profile at those depths. Our experimental data suggest that the combined effect of surface evaporation and temperature-gradient dynamics should be considered to analyze experimental soil water profiles. Additional effects related to the frequency of operation and to protocols for temperature compensation of the dielectric sensors may also affect the probes response during large temperature changes

    Clinical Implementation of Cardiac Resynchronization Therapy-Regional Disparities across Selected ESC Member Countries.

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    BACKGROUND: The present analysis aimed to estimate the penetration of cardiac resynchronization therapy (CRT) on the basis of the prevalence and incidence of eligible patients in selected European countries and in Israel. METHODS AND RESULTS: The following countries were considered: Italy, Slovakia, Greece, Israel, Slovenia, Serbia, the Czech Republic, Poland, Romania, Hungary, Ukraine, and the Russian Federation. CRT penetration was defined as the number of patients treated with CRT (CRT patients) divided by the prevalence of patients eligible for CRT. The number of CRT patients was estimated as the sum of CRT implantations in the last 5 years, the European Heart Rhythm Association (EHRA) White Book being used as the source. The prevalence of CRT indications was derived from the literature by applying three epidemiologic models, a synthesis of which indicates that 10% of heart failure (HF) patients are candidates for CRT. HF prevalence was considered to range from 1% to 2% of the general population, resulting in an estimated range of prevalence of CRT indication between 1000 and 2000 patients per million inhabitants. Similarly, the annual incidence of CRT indication, representing the potential target population once CRT has fully penetrated, was estimated as between 100 and 200 individuals per million. The results showed the best CRT penetration in Italy (47-93%), while in some countries it was less than 5% (Romania, Russian Federation, and Ukraine). CONCLUSION: CRT penetration differs markedly among the countries analyzed. The main barriers are the lack of reimbursement for the procedure and insufficient awareness of guidelines by the referring physicians

    Functional error modeling for uncertainty quantification in hydrogeology

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    Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization
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