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EPA technical exchange : Culebra flow directions and well network optimization analysis
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Graph Neural Network Models of Multiphase Flow from Boundary Integral Methods
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Systems and methods for optimal power flow on a radial network
Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control. One embodiment includes a node controller including a distributed power control application; a plurality of node operating parameters describing the operating parameter of a node and a set of at least one node selected from the group consisting of an ancestor node and at least one child node; wherein send node operating parameters to nodes in the set of at least one node; receive operating parameters from the nodes in the set of at least one node; calculate a plurality of updated node operating parameters using an iterative process to determine the updated node operating parameters using the node operating parameters that describe the operating parameters of the node and the set of at least one node, where the iterative process involves evaluation of a closed form solution; and adjust node operating parameters.
Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks
Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fair rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.
Generating one-column grids with fractal flow dimension
The grid generation capability built into the numerical simulator TOUGH for multi-phase fluid and heat flow through geologic media can create one-column grids with linear or radial geometry, corresponding to one-dimensional or two-dimensional radial flow, respectively. The integral-finite-difference-method that TOUGH employs for spatial discretization makes it very simple to generalize the grid-generation algorithm from integer to non-integer (fractal) flow dimension. Here the grid-generation algorithm is generalized to create one-column grids with fractal flow dimension ranging from less than 1 to 3. The fractal grid generation method is verified by comparing numerical simulation results to an analytical solution for a generalized Theis solution for integer and non-integer flow dimensions between 0.4 and 3. It is then applied to examine gas production decline curves from hydraulically fractured shale that is modeled as a fractal-dimensioned fracture network with flow dimensions between 0.25 and 3. Grids with fractal flow dimension are useful for representing flow through fracture networks or highly heterogeneous geologic media with fractal geometry, and may be particularly useful for inverse methods.
Three‐Phase CO 2 Flow in a Basalt Fracture Network
Abstract Geologic CO 2 sequestration in basalt reservoirs is predicated on permanent CO 2 isolation via rapid mineralization reactions. This process is supported by a substantial body of evidence, including laboratory experiments documenting rapid mineralization rates, regional storage estimates indicating large, accessible storage reservoirs, and two successful pilot‐scale studies. Nevertheless, there remains significant uncertainty in the behavior of CO 2 flow within basalt fracture networks, particularly in the context estimating physical trapping potential in early time and as CO 2 undergoes phase change. In this study, a Monte Carlo numerical model is designed to simulate a supercritical CO 2 plume infiltrating a low‐permeability flood basalt entablature. The fracture network model is based on outcrop‐scale LiDAR mapping of Columbia River Basalt, and CO 2 flow is simulated within fifty equally probable realizations of the fracture network. The spatial distribution of fracture permeability for each realization is randomly drawn from a basalt aperture distribution, and ensemble results are analyzed with e‐type estimates to compute mean and standard deviation of fluid pressure and CO 2 saturation. Results of this model after 10 years of simulation suggest that (1) CO 2 flow converges on a single dominant flow path, (2) CO 2 accumulates at fracture intersections, and (3) variability in permeability can account for a 1.6 m depth interval within which free CO 2 may change phase from supercritical fluid to subcritical liquid or gas. In the context of CO 2 sequestration in basalt, these results suggest that physical CO 2 trapping may be substantially enhanced as carbonate minerals precipitate within the basalt fracture network.
Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges
Here, we examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors.
Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways
Discrete fracture networks (DFN) are often used to model flow and transport in fractured porous media. The accurate resolution of flow and transport behavior on a large DFN involving thousands of fractures is computationally expensive. This makes uncertainty quantification studies of quantities of interest such as travel time through the network computationally intractable, since hundreds to thousands of runs of the DFN model are required to get bounds on the uncertainty of the predictions. Prior works on the subject demonstrated that the complexity of a DFN could be reduced by considering a sub-network of it (often termed a “backbone” sub-network), one whose flow and transport properties were then shown to be similar to that of the full network. The technique is tantamount to partitioning the complete set of fractures of a network into two disjoint sets, one of which is the backbone sub-network while the other its complement. It is in this context that we present a system-reduction technique for DFNs using supervised machine learning via a Random Forest Classifier that selects a backbone sub-network from the full set of fractures. The in-sample errors (in terms of precision and recall scores) of the trained classifier are found to be very accurate indicators of the out-of-sample errors, thus exhibiting that the classifier generalizes well to test data. Moreover, this system-reduction technique yields sub-networks as small as 12% of the full DFN that still recover transport characteristics of the full network such as the peak dosage and tailing behavior for late times. Most importantly, the sub-networks remain connected, and their size can be controlled by a single dimensionless parameter. Furthermore, measures of KL-divergence and KS-statistic for the breakthrough curves of the sub-networks with respect to the full network show physically realistic trends in that the measures decrease monotonically as the size of the sub-networks increase. In conclusion, the computational efficiency gained by this technique depends on the size of the sub-network, but large reductions in computational time can be expected for small sub-networks, yielding as much as 90% computational savings for sub-networks that are as small as 10-12% of the full network.
Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-based Feedback Control
This research delves into applying the Macroscopic Fundamental Diagram (MFD) concept to structured airspace networks for comprehensive aggregate modeling and introduces a feedback-based departure function aimed at optimizing traffic flow. Previous studies have rarely examined structured airspace networks featuring non-stationary vehicles through the MFD perspective. We devised a scenario grounded in practical applications, featuring a multi-lane network with explicit lane-changing behavior. The MFD effectively captured the open-loop response, displaying a low-scatter, unimodal curve on the flow versus occupancy plot. Drawing inspiration from the ground transportation ramp-metering strategies, a proportional-integral-based controller was developed. Extensive simulation outcomes suggest that feedback control, informed by MFD, holds significant potential for managing traffic flow in Urban Air Mobility (UAM) environments; a reduction of 80% in the peak number of vehicles in a holding pattern was observed for a slight reduction in throughput in this study.
Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-based Feedback Control
This research delves into applying the Macroscopic Fundamental Diagram (MFD) concept to structured airspace networks for comprehensive aggregate modeling and introduces a feedback-based departure function aimed at optimizing traffic flow. Previous studies have rarely examined structured airspace networks featuring non-stationary vehicles through the MFD perspective. We devised a scenario grounded in practical applications, featuring a multi-lane network with explicit lane-changing behavior. The MFD effectively captured the open-loop response, displaying a low-scatter, unimodal curve on the flow versus occupancy plot. Drawing inspiration from the ground transportation ramp-metering strategies, a proportional-integral-based controller was developed. Extensive simulation outcomes suggest that feedback control, informed by MFD, holds significant potential for managing traffic flow in Urban Air Mobility (UAM) environments; a reduction of 80% in the peak number of vehicles in a holding pattern was observed for a slight reduction in throughput in this study.
Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions
Here we present a fully analytical, heuristic model – the “Analytical Transport Network Model” – for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5–1.5 and 5–6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics for the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.
Method and Apparatus for Predicting Unsteady Pressure and Flow Rate Distribution in a Fluid Network
A method and apparatus for analyzing steady state and transient flow in a complex fluid network, modeling phase changes, compressibility, mixture thermodynamics, external body forces such as gravity and centrifugal force and conjugate heat transfer. In some embodiments, a graphical user interface provides for the interactive development of a fluid network simulation having nodes and branches. In some embodiments, mass, energy, and specific conservation equations are solved at the nodes, and momentum conservation equations are solved in the branches. In some embodiments, contained herein are data objects for computing thermodynamic and thermophysical properties for fluids. In some embodiments, the systems of equations describing the fluid network are solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods.
The Freight Analysis Framework Verson 4 (FAF4) - Building the FAF4 Regional Database: Data Sources and Estimation Methodologies
The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas by all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns the flows to the transportation network, and projects freight flow patterns into the future. The FAF4 is the fourth database of its kind, FAF1 provided estimates for truck, rail, and water tonnage for calendar year 1998, FAF2 provided a more complete picture based on the 2002 Commodity Flow Survey (CFS) and FAF3 made further improvements building on the 2007 CFS. Since the first FAF effort, a number of changes in both data sources and products have taken place. The FAF4 flow matrix described in this report is used as the base-year data to forecast future freight activities, projecting shipment weights and values from year 2020 to 2045 in five-year intervals. It also provides the basis for annual estimates to the FAF4 flow matrix, aiming to provide users with the timeliest data. Furthermore, FAF4 truck freight is routed on the national highway network to produce the FAF4 network database and flow assignments for truck. This report details the data sources and methodologies applied to develop the base year 2012 FAF4 database. An overview of the FAF4 components is briefly discussed in Section 2. Effects on FAF4 from the changes in the 2012 CFS are highlighted in Section 3. Section 4 provides a general discussion on the process used in filling data gaps within the domestic CFS matrix, specifically on the estimation of CFS suppressed/unpublished cells. Over a dozen CFS OOS components of FAF4 are then addressed in Section 5 through Section 11 of this report. This includes discussions of farm-based agricultural shipments in Section 5, shipments from fishery and logging sectors in Section 6. Shipments of municipal solid wastes and debris from construction and demolition activities are covered in Section 7. Movements involving OOS industry sectors on Retail, Services, and Household/Business Moves are addressed in Section 8. Flows of OOS commodity on crude petroleum and natural gas are presented in Sections 9 and 10, respectively. Discussions regarding shipments of foreign trade, including trade with Canada/Mexico, international airfreight, and waterborne foreign trade, are then discussed in Section 11. Several appendices are also provided at the end of this report to offer additional information.
Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd
Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.