CyberDep: Enhanced Generation of Bayesian Networks Through The Inclusion of Bidirectional Data Flow Dependencies in Cyber-Physical Power Systems
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Al-Homoud, Leen [Texas A&M University]↗
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Finding Hidden Patterns in High Resolution Wind Flow Model Simulations
Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.
97 MATHEMATICS AND COMPUTING↗
Direct Numerical Simulation of the Flow through a Randomly Packed Pebble Bed
The proposition for molten salt and high-temperature gas-cooled reactors has increased the focus on the dynamics and physics in randomly packed pebble beds. Research is being conducted on the validity of these designs as a possible contestant for the fourth-generation nuclear power systems. A detailed understanding of the coolant flow behavior is required in order to ensure proper cooling of the reactor core during normal and accident conditions. In order to increase the understanding of the flow through these complex geometries and enhance the accuracy of lower-fidelity modeling, high-fidelity approaches such as direct numerical simulation (DNS) can be utilized. Nek5000, a spectral-element computational fluid dynamics (CFD) code, was used to develop DNS fluid flow data. The flow domain consisted of 147 pebbles enclosed by a bounding wall. In the work presented, the Reynolds numbers ranged from 430 to 1050 based on the pebble diameter and inlet velocity. Characteristics of the flow domain such as volume averaged porosity, axial porosity, and radial porosity were studied and compared with correlations available in the literature. Friction factors from the DNS results for all Reynolds numbers were compared with correlations in the literature. The first- and second-order statistics show good agreement with the available experimental data. Turbulence length scales were analyzed in the flow. Reynolds stress anisotropy was characterized by utilizing invariant analysis. Overall, the results of the analysis in this study provide deeper understanding of the flow behavior and the effect of the wall in packed beds.
Yildiz, Mustafa Alper↗
IRIS-MEMFLOW: Data Flow-Enabled Portable Memory Orchestration in IRIS Runtime for Diverse Heterogeneity
Task-based programming models and execution paradigms provide a means to decompose a computation by expressing it as a graph in which each node represents a specific computation operating on memory objects and the edges define the dependencies in the execution flow. In this execution model, independent nodes in the graph can be executed concurrently in different computing devices, making it suitable for heterogeneous systems in which computing devices with different architectures coexist. However, careful memory orchestration across heterogeneous devices is needed because copies of the same memory object may reside in multiple devices during execution. Manually ensuring such an orchestration is quite challenging. Not only must an application developer guard against race conditions, but they must also optimize data movement between the host and devices because unnecessary data movement significantly impacts performance. To mitigate these challenges, we enhance the IRIS heterogeneous runtime and introduce IRIS-MEMFLOW–a data flow–enabled portable memory abstraction for seamlessly orchestrating memory in diverse heterogeneous computing environments. By using data-flow analysis, IRIS-MEMFLOW guards against race conditions while multiple heterogeneous devices access memory objects. IRIS-MEMFLOW also optimizes data movement between the host and devices without manual intervention. As a result, IRIS provides improved programming productivity, performance, and portability for multidevice heterogeneous executions in high-performance computing and cloud systems that run diverse architectures from different vendors. The efficacy of IRIS-MEMFLOW is evaluated through experiments that show its capability in terms of programming productivity, multidevice heterogeneity, portability, and low overhead versus the state of the art.
Monil, M. A. H. [ORNL] (ORCID:0000000334194037)↗
Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems
This Git repository contains codes for the 'Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems' paper that will be published in 2023 SIGKDD. This work uses physical-informed prior (PiP) information to learn and predict fluid dynamics in a reduced mesh space. We propose a two-stage graph-based model for fluid velocity field reconstruction and prediction. In the first stage, we learn a subgraph autoencoder to summarize the information in a mesh-reduced space using physical-informed priors. In the second stage, we learn a dynamics predictor to predict subgraph evolution. We demonstrate the effectiveness of our model on two fluid flow datasets: lid-driven cavity flow data and cylinder flow data. This code is also applicable for any other dynamic systems with corresponding data.
Lei, Bo↗
Cluster-to-cluster data transfer with data compression over wide-area networks
The recent emergence of ultra high-speed networks up to 100 Gb/s has posed numerous challenges and has led to many investigations on efficient protocols to saturate 100 Gb/s links. However, end-to-end data transfers involve many components, not only protocols, affecting overall transfer performance. These components include disk I/O subsystem, additional computation associated with data streams, and network adapters. For example, achievable bandwidth by TCP may not be implementable if disk I/O or CPU becomes a bottleneck in end-to-end data transfer. In this paper, we first model all the system components involved in end-to-end data transfer as a graph. We then formulate the problem whose goal is to achieve maximum data transfer throughput using parallel data flows. We also propose a variable data flow GridFFP XIO stack to improve data transfer with data compression. Our contributions lie in how to optimize data transfers considering all the system components involved rather than in accurately modeling all the system components involved. Our proposed formulations and solutions are evaluated through experiments on the ESnet 100G testbed and a wide-area cluster-to-cluster testbed. The experimental results on the ESnet 100G testbed show that our approach is several times faster than Globus Online-8 x faster for datasets with many 10 MB files and 3-4 x faster for other datasets of larger size files. The experimental results on the cluster-to-cluster testbed show that our variable data flow approach is up to 4 x faster than a normal cluster data transfer.
Disk-to-Disk Transfer↗
Communication device with persistent configuration and verification
The present disclosure pertains to systems and methods for establishing communication with a remote communication device in a software defined network (SDN) during time when an SDN controller is unavailable. In one embodiment, a local communication device may be configured to receive a plurality of data flows from an SDN controller and to store the plurality of data flows in a persistent data memory. The device may generate a unique identifier for the local communication device that is transmitted to a remote communication device. Following a disruption the results in the SDN controller being unavailable, the local communication device may recover into a default configured state based on the plurality of data flows in the persistent data memory. The local communication device may then transmit the unique identifier to the remote communication device after the disruption to begin a process of reestablishing communication with the remote communication device.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Analysis of Daily Peaking and Run-of-River Operations with Flow Variability Metrics, Considering Subdaily to Seasonal Time Scales
Environmental flows are an important consideration in licensing hydropower projects because operational flow releases can result in adverse conditions to downstream ecological communities. Flow variability assessments have typically focused on pre- and post-dam conditions using metrics based on daily-averaged flow values. This study used subdaily and daily flow data to assess environmental flow response to changes in hydropower operations from daily-peaking to run-of-river. An analysis tool was developed to quantify subdaily to seasonal flow variability metrics and was applied to four hydropower projects that underwent operational changes based on regulatory requirements. Results indicate that the distribution of flows is significantly different between daily-peaking and run-of- river operations and that daily-peaking operations are flashier than run-of-river operations; these differences are seen using hourly-averaged flow datasets and are less pronounced or not noticeable using daily-averaged flow datasets. Of all variability metrics analyzed, hydrograph rise and fall rates were the most sensitive to using daily versus subdaily flow data. This outcome has implications for the development of flow-ecology relationships that quantify effects of rate of change on processes such as fish stranding and displacement, along with habitat stability. The quantification of flow variability statistics should be done using subdaily datasets and metric to accurately represent the nature of hydropower operations , especially for facilities that utilize daily-peaking operations.
54 ENVIRONMENTAL SCIENCES↗
Building the Next Generation of Regional Geothermal Potential Maps: Examples from the Great Basin Region, Western USA
Over the past decade research has greatly advanced our understanding of structurally controlled geothermal systems and the potential for “deep stratigraphic reservoirs” in the Great Basin region, western USA. For example, studies have shown that structurally controlled geothermal systems are associated with specific structural settings, such as terminations of major normal faults, accommodation zones, pull-aparts in strike-slip faults, displacement transfer zones, or step-overs in range-front faults. However, we know that not every structure (e.g., termination of a major normal fault or step-over in a range-front fault) hosts an active geothermal system. Recent research has also shown that regional strain rate model as determined from GPS geodesy, the composition of that strain (extensional versus shear or compressive strain), recency of Quaternary faulting, and slip and dilation tendency of faults correlate positively with the distribution of both high and low-temperature, structurally controlled geothermal systems within the Great Basin. Combining these strain data and fault slip history data sets with existing temperature gradient data, heat flow data, spring temperatures, fluid geothermometry, and geophysical data provides an effective platform for evaluating the geothermal potential of respective structural plays. Recent studies focused on deep stratigraphic reservoirs indicate the best conditions for economically viable development are associated with thick sedimentary basins with low-thermal-conductivity sedimentary cap rocks overlying permeable host rocks (e.g. carbonates) and co-located with areas of high heat flow. Some of the factors that contribute to the potential for deep stratigraphic reservoirs and conventional structurally controlled geothermal systems apply to both types of resources, and in some places the potential for these two types of resources overlap in space, suggesting the potential for hybrid reservoirs (structurally controlled deep stratigraphic reservoirs). Effective and complete evaluation of geothermal potential in the Great Basin should include parameters that cover both types of resources. In this study, two areas with a combined area of 26,000 km 2 were evaluated for geothermal resource potential, equating to <4% of the total 680,000 km 2 size of the Great Basin region. Within these two study areas, 73 potential structural targets and 8 potential deep sedimentary target areas were identified. Four of the 73 potential structural target areas are associated with known thermal anomalies. All 73 structural target areas were ranked according to multiple data sets.
15 GEOTHERMAL ENERGY↗
Hestia-SWIFL: hourly anthropogenic fossil fuel CO2 and heat on the 2km WRF grid, version 1.1
The Hestia-SWIFL version 1.1 anthropogenic heat (AH) and fossil fuel CO2 (FFCO2) emissions data product represent emissions due to the combustion of fossil fuel and cement production within the state of Arizona from 2019 to 2022. This product was developed as part of the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL) project, which aims to provide new knowledge and tools that address extreme heat, air quality, climate change and related urban environmental issues by integrating high-resolution observations, modeling, and civic engagement. The emissions are generated using a bottom-up/engineering approach and are tied to results generated by the Vulcan Project version 4, an effort to quantify space/time-resolved FFCO2 & AH emissions for the entire United States landscape. A large number of data sources are combined to best estimate the emissions at fine scales such as air quality emissions data, traffic flow data, building information, sociodemographic information, and fuel statistics. The AH product provides emissions for two emissions sources (transportation and point source emissions) in units of Watts per hour per square meter (W/m2) per year (annual files) or per hour (hourly files). The FFCO2 product provides emissions from nine individual emission sectors as well as the total, and in units of tons of carbon (tC) per grid cell per year or per hour. The output made available here places the native spatial resolution of the Hestia FFCO2 & AH emissions data product (points, lines, and polygons) into a regularized 2km x 2km grid at hourly and annual temporal resolutions, and stored in netCDF files. The exact spatial extent is defined by the ASU Weather Research Forecast (WRF) simulation grid. All data are processed using R/Python pm high-performance computing system. 2-27-2026 updates: Bugs in airport hourly profile (both AH and FFCO2) and building spatial patterns (FFCO2 only) were fixed. Hourly emissions are reprocessed for all years to reflect those changes.
54 ENVIRONMENTAL SCIENCES↗
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. Here, we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. Finally, the effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Visualizing Electrical Power Systems as Flow Fields
We describe a method for visualizing data flows on large networks. We transform data flow on fixed networks into a vector field, which can be directly visualized using scientific flow visualization techniques. We evaluated the method on power flowing through two transmission power networks: a small, regional, IEEE test system (RTS-96) and a large national-scale system (the Eastern Interconnection). For the larger and more complex transmission system, the method illustrates features of the power flow that are not accessible when visualizing the power transmission with traditional network visualization techniques.
97 MATHEMATICS AND COMPUTING↗
The Nuclear Energy Advanced Modeling and Simulation Safeguards and Separations Reprocessing Plant Toolkit
This report details the progress made in the development of the Reprocessing Plant Toolkit (RPTk) for the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. RPTk is an ongoing development effort intended to provide users with an extensible, integrated, and scalable software framework for the modeling and simulation of spent nuclear fuel reprocessing plants by enabling the insertion and coupling of user-developed physicochemical modules of variable fidelity. The NEAMS Safeguards and Separations IPSC (SafeSeps) and the Enabling Computational Technologies (ECT) supporting program element have partnered to release an initial version of the RPTk with a focus on software usability and utility. RPTk implements a data flow architecture that is the source of the system's extensibility and scalability. Data flows through physicochemical modules sequentially, with each module importing data, evolving it, and exporting the updated data to the next downstream module. This is accomplished through various architectural abstractions designed to give RPTk true plug-and-play capabilities. A simple application of this architecture, as well as RPTk data flow and evolution, is demonstrated in Section 6 with an application consisting of two coupled physicochemical modules. The remaining sections describe this ongoing work in full, from system vision and design inception to full implementation. Section 3 describes the relevant software development processes used by the RPTk development team. These processes allow the team to manage system complexity and ensure stakeholder satisfaction. This section also details the work done on the RPTk ``black box'' and ``white box'' models, with a special focus on the separation of concerns between the RPTk user interface and application runtime. Section 4 and 5 discuss that application runtime component in more detail, and describe the dependencies, behavior, and rigorous testing of its constituent components.
Enabling Computational Technologies↗
An Integrated Hydrological Model for Long-Term Monitoring in Tims Branch Watershed, SC - 19435
The Tims Branch watershed in Savannah River Site (SRS), South Carolina is a small-scale watershed that has been impacted by contaminated runoff and discharge wastewater from nuclear research facilities located within the watershed over the past century. Innovative remediation technologies, such as tin injection and pump and treat, have been implemented to remove heavy metals and volatile organic compounds (VOCs) from the watershed. Today, the Tims Branch watershed serves as a test-bed for monitoring the performance of remediation activities. This study describes the development of the two-dimensional integrated surface water/groundwater model for Tims Branch watershed to assess the impact of cleanup activities on hydrological processes in this watershed. This model will serve as a tool to simulate the effect of climatic events on overland flow distribution and further on will be used to assess contaminant transport. This paper illustrates the application of the developed model for simulation of overland flow depth and velocity in Tims Branch watershed and streamflow in A-014 outfall tributary. The watershed includes the second order Tims Branch stream, several outfalls, and a network of tributaries. Simulation of non-calibrated overland flow was conducted for the years 2016 using available stream flow data within the watershed. Recent flow data from a newly deployed stage gauge was also used to assess the model results. The results show the model ability to simulate overland flow and stream flow and can be used as a tool to represent the hydrological conditions in the Tims Branch watershed. The results of this model simulation will be further used to develop contaminant transport model. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Communication device for implementing selective encryption in a software defined network
The present disclosure pertains to systems and methods for selectively encrypting data flows within a software defined network (SDN). In one embodiment, a communication device may be configured to receive a plurality of unencrypted data packets. The communication device may receive from an SDN controller a criterion used to identify at least one of the unencrypted data flows to be encrypted. Based on the criterion, an encryption subsystem may generate an encrypted data flow the unencrypted data packets based on an encryption key. In some embodiments, the encryption system may parse the packets and encrypt the data payloads without encrypting the routing information associated with the packet. In other embodiments, the encryption subsystem may be configured to encapsulate and encrypt the entire unencrypted data packet. In some embodiments, the encryption subsystem may further be configured to authenticate a sending device and/or to verify the integrity of a message.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Communication device for implementing selective encryption in a software defined network
The present disclosure pertains to systems and methods for selectively encrypting data flows within a software defined network (SDN). In one embodiment, a communication device may be configured to receive a plurality of unencrypted data packets. The communication device may receive from an SDN controller a criterion used to identify at least one of the unencrypted data flows to be encrypted. Based on the criterion, an encryption subsystem may generate an encrypted data flow the unencrypted data packets based on an encryption key. In some embodiments, the encryption system may parse the packets and encrypt the data payloads without encrypting the routing information associated with the packet. In other embodiments, the encryption subsystem may be configured to encapsulate and encrypt the entire unencrypted data packet. In some embodiments, the encryption subsystem may further be configured to authenticate a sending device and/or to verify the integrity of a message.
97 MATHEMATICS AND COMPUTING↗
Recent progress with the top to bottom approach to vectorization in GeantV
SIMD acceleration can potentially boost by factors the application throughput. Achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes however way beyond breaking some loop dependencies and relying on the compiler. Since the refactoring effort scales with the number of lines of code, it is important to understand what kind of performance gains can be expected in such complex cases. We started to investigate a couple of years ago a top to bottom vectorization approach to particle transport simulation. Percolating vector data to algorithms was mandatory since not all the components can internally vectorize. Vectorizing low-level algorithms is certainly necessary, but not sufficient to achieve relevant SIMD gains. In addition, the overheads for maintaining the concurrent vector data flow and copy data have to be minimized. In the context of a vectorization R&D for simulation we developed a framework to allow different categories of scalar and vectorized components to co-exist, dealing with data flow management and real-time heuristic optimizations. The paper describes our approach on coordinating SIMD vectorization at framework level, making a detailed quantitative analysis of the SIMD gain versus overheads, with a breakdown by components in terms of geometry, physics and magnetic field propagation. We also present the more general context of this R&D work and goals for 2018.
97 MATHEMATICS AND COMPUTING↗
Internship Final Report on the unsupervised learning sensor fusion (ULSF) approach
This paper describes a summer internship project undertaken at Sandia National Labs (SNL), both current status and future work. The project was to explore various machine learning approaches for use on turbulent flow data. Specifically, unsupervised classification of turbulent flow data was explored. First, the usage of models in this field is discussed, and several issues in the common usage of the models are identified. Solutions to these issues are then proposed, in the form of a Bayesian filtering approach which probabilistically incorporates multiple sources of data to improve confidence in a result. Several types of sensors are suggested for this method, the incorporation of which range from semi-supervised learning approaches to fully unsupervised. These approaches are then tested on several turbulent flow cases.
97 MATHEMATICS AND COMPUTING↗