A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations

Salloum, Maher; Sargsyan, Khachik; Jones, Reese E.; Debusschere, Bert; Najm, Habib N.; Adalsteinsson, Helgi

doi:10.1137/110844404

Cited by 19 publications

(42 citation statements)

References 33 publications

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“…We combine different measured kinetics data in order to cover a bigger temperature range and increase the amount data. Doing so decreases the uncertainty in the fitting parameters of the Arrhenius relationship according to the central limit theorem [23]. All experimental data fit Figure 1.…”

Section: Experimental Datamentioning

confidence: 99%

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Empirical and physics based mathematical models of uranium hydride decomposition kinetics with quantified uncertainties.

Salloum

Gharagozloo

2013

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Metal particle beds have recently become a major technique for hydrogen storage. In order to extract hydrogen from such beds, it is crucial to understand the decomposition kinetics of the metal hydride. We are interested in obtaining a a better understanding of the uranium hydride (UH 3 ) decomposition kinetics. We first developed an empirical model by fitting data compiled from different experimental studies in the literature and quantified the uncertainty resulting from the scattered data. We found that the decomposition time range predicted by the obtained kinetics was in a good agreement with published experimental results. Secondly, we developed a physics based mathematical model to simulate the rate of hydrogen diffusion in a hydride particle during the decomposition. We used this model to simulate the decomposition of the particles for temperatures ranging from 300K to 1000K while propagating parametric uncertainty and evaluated the kinetics from the results. We compared the kinetics parameters derived from the empirical and physics based models and found that the uncertainty in the kinetics predicted by the physics based model covers the scattered experimental data. Finally, we used the physics-based kinetics parameters to simulate the effects of boundary resistances and powder morphological changes during decomposition in a continuum level model. We found that the species change within the bed occurring during the decomposition accelerates the hydrogen flow by increasing the bed permeability, while the pressure buildup and the thermal barrier forming at the wall significantly impede the hydrogen extraction. 3 AcknowledgmentThe authors would like to acknowledge Andrew Shugard for providing valuable discussions and feedback that were helpful to accomplish this work.

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Section: Experimental Datamentioning

confidence: 99%

“…Based on this data, we infer according to the method described in [23] an Arrhenius dependence of the kinetics coefficient as a function of the temperature i.e. a linear dependence between log(k) and 1/T .…”

Section: Experimental Datamentioning

confidence: 99%

Empirical and physics based mathematical models of uranium hydride decomposition kinetics with quantified uncertainties.

Salloum

Gharagozloo

2013

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“…Predictive simulation of engineering systems are often challenged by different phenomena operating over a broad range of length and times scales spreading from atomistic to continuum levels. Such phenomena are encountered for example in different engineering applications such as nanotechnology [25,78,54,104,112] and fluid dynamics [22,50,77,5,84,37]. The ubiquity of such dynamics is illustrated by the need to supply constitutive relationships in mathematical models of physical phenomena to compensate for the unresolved degrees of freedom.…”

Section: Solid-fluid Systemmentioning

confidence: 99%

“…We infer these parameters and express as polynomial chaos expansions (PCE) due to the facility with which they propagating uncertainty in continuum simulations [31,56,57,55,84]. We use Bayesian inference [29,87,62] to build the constitutive law.…”

Section: Solid-fluid Systemmentioning

confidence: 99%

Bridging the gap between atomistic phenomena and continuum behavior in electrochemical energy storage processes.

Davidson¹,

Griffiths²,

Lee³

et al. 2012

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One of the most significant impediments to advances in electrochemical energy storage lies in the gap between fundamental understanding of atomistic phenomena and our understanding of the impact of these phenomena on system performance at device scales. Atomistic models (DFT, MD, MC) provide insight into such phenomena along with a means of quantification, but such models are too computationally intensive to address device-scale behavior. Similarly, device-scale insight for design and optimization can be obtained through continuum models that are sufficiently fast, but these models account for only the simplest atomistic phenomena. There is thus a large gap between our ability to develop fundamental understanding and our ability to use this understanding to make rapid advances in energy storage technologies. The goal of this work is to help bridge this gap through an innovative synthesis of atomistic and continuum approaches in which atomistic phenomena are captured through fast reduced-order integral methods that can be imbedded into continuum-like models describing device-scale behavior.3 Acknowledgment

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“…Previous work in this context can be found in Refs. [118,117] and [111,112]. In the latter, e.g., the authors focused on analyzing the effect of parametric uncertainty and intrinsic noise in isothermal, isobaric MD simulations of TIP4P water.…”

Section: Introductionmentioning

confidence: 99%

Multiscale modeling for fluid transport in nanosystems.

Lee¹,

Jones²,

Mandadapu³

et al. 2013

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Atomistic-scale behavior drives performance in many micro-and nano-fluidic systems, such as mircrofludic mixers and electrical energy storage devices. Bringing this information into the traditionally continuum models used for engineering analysis has proved challenging. This work describes one such approach to address this issue by developing atomistic-to-continuum multi scale and multi physics methods to enable molecular dynamics (MD) representations of atoms to incorporated into continuum simulations. Coupling is achieved by imposing constraints based on fluxes of conserved quantities between the two regions described by one of these models. The impact of electric fields and surface charges are also critical, hence, methodologies to extend finite-element (FE) MD electric field solvers have been derived to account for these effects. Finally, the continuum description can have inconsistencies with the coarse-grained MD dynamics, so FE equations based on MD statistics were derived to facilitate the multi scale coupling. Examples are shown relevant to nanofluidic systems, such as pore flow, Couette flow, and electric double layer.

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A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations

Cited by 19 publications

References 33 publications

Empirical and physics based mathematical models of uranium hydride decomposition kinetics with quantified uncertainties.

Empirical and physics based mathematical models of uranium hydride decomposition kinetics with quantified uncertainties.

Bridging the gap between atomistic phenomena and continuum behavior in electrochemical energy storage processes.

Multiscale modeling for fluid transport in nanosystems.

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