2,103 research outputs found
A Hybrid Multiscale Framework for Subsurface Flow and Transport Simulations
AbstractExtensive research is aimed at improving predictive ability of biogeochemical earth and environmental system simulators, with applications ranging from contaminant transport and remediation to impacts of carbon and nitrogen cycling on local ecosystems and climate. Most process-based numerical models are designed for a single characteristic length and time scale. For application-relevant scales, it is necessary to introduce approximations and empirical parameterizations to describe complex systems because of limitations on process understanding, system characterization and computation. Using emerging understanding of biological and environmental processes at fundamental scales to advance predictions of the larger system behavior requires the development of multiscale simulators, and there is strong interest in coupling microscale and macroscale models together in a hybrid multiscale simulation. A limited number of hybrid multiscale simulations have been developed for biogeochemical systems, mostly using application-specific approaches for model coupling. We are developing a generalized approach to hierarchical model coupling designed for high-performance computational systems, based on the Swift computing workflow framework. In this presentation we will describe the generalized approach and provide two use cases: 1) simulation of a mixing-controlled biogeochemical reaction coupling pore- and continuum-scale models, and 2) simulation of biogeochemical impacts of groundwater–river water interactions coupling fine- and coarse-grid model representations. This generalized framework can be customized for use with any pair of linked models (microscale and macroscale) with minimal intrusiveness to the at-scale simulators. It combines a set of python scripts with the Swift workflow environment to execute a complex multiscale simulation utilizing an approach similar to the well-known Heterogeneous Multiscale Method. User customization is facilitated through user-provided input and output file templates and processing function scripts, and execution within a high-performance computing environment is handled by Swift, such that minimal to no user modification of at-scale codes is required
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
On pore-scale modeling and simulation of reactive transport in 3D geometries
Pore-scale modeling and simulation of reactive flow in porous media has a
range of diverse applications, and poses a number of research challenges. It is
known that the morphology of a porous medium has significant influence on the
local flow rate, which can have a substantial impact on the rate of chemical
reactions. While there are a large number of papers and software tools
dedicated to simulating either fluid flow in 3D computerized tomography (CT)
images or reactive flow using pore-network models, little attention to date has
been focused on the pore-scale simulation of sorptive transport in 3D CT
images, which is the specific focus of this paper. Here we first present an
algorithm for the simulation of such reactive flows directly on images, which
is implemented in a sophisticated software package. We then use this software
to present numerical results in two resolved geometries, illustrating the
importance of pore-scale simulation and the flexibility of our software
package.Comment: 15 pages, 6 figure
A machine learning approach for efficient uncertainty quantification using multiscale methods
Several multiscale methods account for sub-grid scale features using coarse
scale basis functions. For example, in the Multiscale Finite Volume method the
coarse scale basis functions are obtained by solving a set of local problems
over dual-grid cells. We introduce a data-driven approach for the estimation of
these coarse scale basis functions. Specifically, we employ a neural network
predictor fitted using a set of solution samples from which it learns to
generate subsequent basis functions at a lower computational cost than solving
the local problems. The computational advantage of this approach is realized
for uncertainty quantification tasks where a large number of realizations has
to be evaluated. We attribute the ability to learn these basis functions to the
modularity of the local problems and the redundancy of the permeability patches
between samples. The proposed method is evaluated on elliptic problems yielding
very promising results.Comment: Journal of Computational Physics (2017
Non-negative mixed finite element formulations for a tensorial diffusion equation
We consider the tensorial diffusion equation, and address the discrete
maximum-minimum principle of mixed finite element formulations. In particular,
we address non-negative solutions (which is a special case of the
maximum-minimum principle) of mixed finite element formulations. The discrete
maximum-minimum principle is the discrete version of the maximum-minimum
principle.
In this paper we present two non-negative mixed finite element formulations
for tensorial diffusion equations based on constrained optimization techniques
(in particular, quadratic programming). These proposed mixed formulations
produce non-negative numerical solutions on arbitrary meshes for low-order
(i.e., linear, bilinear and trilinear) finite elements. The first formulation
is based on the Raviart-Thomas spaces, and is obtained by adding a non-negative
constraint to the variational statement of the Raviart-Thomas formulation. The
second non-negative formulation based on the variational multiscale
formulation.
For the former formulation we comment on the affect of adding the
non-negative constraint on the local mass balance property of the
Raviart-Thomas formulation. We also study the performance of the active set
strategy for solving the resulting constrained optimization problems. The
overall performance of the proposed formulation is illustrated on three
canonical test problems.Comment: 40 pages using amsart style file, and 15 figure
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
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