PAR<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e450" altimg="si137.gif"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>: Parallel Random Walk Particle Tracking Method for solute transport in porous media

Rizzo, Calogero B.; Nakano, Aiichiro; Barros, Felipe P. J. de

doi:10.1016/j.cpc.2019.01.013

Cited by 28 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, the algorithm used for generating the data needed for the DCNN, after which we explain the DCNN itself. algorithm was designed for solute transport in a given velocity field with non-periodic boundary conditions (Rizzo et al, 2019). Thus, two changes were made for implementing it in a pore-scale domain.…”

Section: The Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network

Kamrava

Barros

et al. 2021

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Measuring or computing the dispersion coefficient D L in flow through porous media, a fundamental characteristic of transport in geological formations and risk analysis, is a time-consuming endeaver. Moreover, although D L is sensitive to the morphology of a pore space, a direct link between the two has been missing. We proposed a deep convolutional neural network for predicting D L , using 3D images of porous media and their porosities. The accuracy of the predictions for the actual data indicates that the ability of the network for estimating the important flow and transport properties of porous media for new input data. The present work was at the core scale, on the order of the physical sizes of the sandstones used in our study. The same approach may be used at the eld scale. In that case, one generates the input data by following the same procedure, except that the data should be generated for models in which the permeability and porosity of the formation vary spatially, and represent correlated fields. Work in this direction is in progress. KAMRAVA ET AL.

show abstract

Section: The Methodologymentioning

confidence: 99%

“…, was proposed by Rizzo et al (2019) that utilizes graphics processing units (GPUs), and was employed to study transport of solutes at field scale (Rizzo & de Barros, 2017;Rizzo et al, 2019).…”

mentioning

confidence: 99%

Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network

Kamrava

Barros

et al. 2021

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…The governing equations of groundwater flow are solved using MODFLOW (Harbaugh, ) and the Python interface FloPy (Bakker et al, ), while solute transport is simulated using the open source GPU‐accelerated random walk particle tracking code PAR 2 (Rizzo et al, ). Details about the physical‐mathematical model adopted in this work can be found in Appendix .…”

Section: Identifying the Lrp Of A 3‐d Random Hydraulic Conductivity Fmentioning

confidence: 99%

“…Solute plume after 500 steps ( t =25) of the open source GPU‐accelerated random walk particle tracking code PAR 2 (Rizzo et al, ) using the synthetic true field

\overset{Y}{true}

(see Figure ). On the bottom projection, we compute the x 3 ‐integrated log concentration.…”

Section: Identifying the Lrp Of A 3‐d Random Hydraulic Conductivity Fmentioning

confidence: 99%

Minimum Hydraulic Resistance Uncertainty and the Development of a Connectivity‐Based Iterative Sampling Strategy

Rizzo

Barros

2019

Water Resources Research

Self Cite

View full text Add to dashboard Cite

The presence of well‐connected paths is commonly observed in spatially heterogeneous porous formations. Channels consisting of high hydraulic conductivity (K) values strongly affect fate and transport of dissolved species in the subsurface environment. Several studies have established a correlation between connectivity properties of the spatially variable K‐field and solute first arrival times. However, due to limited knowledge of the spatial structure of the K‐field, connectivity metrics are subject to uncertainty. In this work, we utilize the concept of the minimum hydraulic resistance and least resistance path to evaluate the connectivity of a K‐field in a stochastic framework. We employ a fast graph theory‐based algorithm to alleviate the computational burden associated with stochastic computations in order to investigate both the impact of the hydrogeological structural conceptualization and domain dimensionality (2‐D vs. 3‐D) on the uncertainty of the minimum hydraulic resistance. Finally, we propose an iterative data acquisition strategy that can be utilized to identify the least resistance path (which is linked to preferential flow channels) in real sites. A synthetic benchmark test is presented, showing the advantages of the proposed sampling strategy when compared to a regular sampling strategy. By using the iterative data sampling strategy, we were able to reduce first arrival time uncertainty by 47% (when compared to the regular sampling strategy), while maintaining site characterization efforts constant.

show abstract

“…The first type are the random‐walk particle tracking (RWPT) methods that simulate diffusion by Brownian motion‐based random walks (Benson & Meerschaert, 2008; Bolster et al., 2016; Paster et al., 2014; Rahbaralam et al., 2015; Schmidt et al., 2017; Sole‐Mari & Fernàndez‐Garcia, 2018; Sole‐Mari et al., 2017). These methods are popular for their ease of implementation, natural parallelism of transport calculations (Rizzo et al., 2019), and their attractive speed to accuracy trade‐offs, as compared to corresponding Eulerian methods (Benson et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Random‐Walk Modeling of Reactive Transport in Porous Media With a Reduced‐Order Chemical Basis of Conservative Components

Sole‐Mari

Schmidt

Bolster

et al. 2021

Water Resources Research

View full text Add to dashboard Cite

In this work we employ a reduced-order basis of conservative chemical components to model reactive transport using a Lagrangian (particle tracking) method. While this practice is wellunderstood in the Eulerian (grid-based) context, its adaptation to a Lagrangian context requires a novel reformulation of particle transport properties. Because the number of conservative-species particles need not change during simulation, spatial resolution stays constant in time, and there is no increase in computational expense due to increasing numbers of product particles. Additionally, this treatment simplifies the interaction between equilibrium and kinetic reactions and allows the use of speciesdependent transport operators at the same time. We apply this method to model a suite of simple test problems that include equilibrium and kinetic reactions, and results exhibit excellent match with base-case Eulerian results. Finally, we apply the new method to model a 2D problem concerning the mobilization of cadmium by a CO 2 leak, showing the potential applicability of the proposed methodology. SOLE-MARI ET AL.

show abstract

PAR2: Parallel Random Walk Particle Tracking Method for solute transport in porous media

Cited by 28 publications

References 21 publications

Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network

Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network

Minimum Hydraulic Resistance Uncertainty and the Development of a Connectivity‐Based Iterative Sampling Strategy

Random‐Walk Modeling of Reactive Transport in Porous Media With a Reduced‐Order Chemical Basis of Conservative Components

Contact Info

Product

Resources

About