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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The initial design of a supercomputer network topology including the design of the communications nodes along with the communications interface hardware and software is covered. Several space science applications that are proposed experiments by GSFC and JPL for a supercomputer network using the NASA ACTS satellite are also reported.
Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.
Many physiological, environmental, and operational risks exist for crewmembers during spaceflight. An understanding of these risks from an integrated perspective is required to provide effective and efficient mitigations during future exploration missions that typically have stringent limitations on resources available, such as mass, power, and crew time. The Human Research Program (HRP) is in the early stages of developing collaborative modeling approaches for the purposes of managing its science portfolio in an integrated manner to support cross-disciplinary risk mitigation strategies and to enable resilient human and engineered systems in the spaceflight environment. In this talk, we will share ideas being explored from fields such as network science, complexity theory, and system-of-systems modeling. Initial work on tools to support these explorations will be discussed briefly, along with ideas for future efforts.
The 2003 opportunity has the potential to carry out for the first time in Mars exploration history, coordinated measurements from the surface and from orbit that can address fundamental issues associated with the Martian global circulation and climate system. Coordinated measurements are defined here to mean collecting meteorological data from a network of 12-16 globally distributed surface stations simultaneously with an orbiter carrying an atmospheric sounder. With such measurements it is possible to define the horizontally varying (barotropic) and vertically varying (baroclinic) components of the global circulation from which the full 3-dimensional horizontal wind field can be reconstructed. It is also possible to precisely define the CO2 cycle, the main component of the current climate system. InterMarsNet, as currently envisioned, consists of 3-4 landers with a supporting communications orbiter that may carry some instrumentation. The landers are likely to touch down in low latitudes and will probably be configured to optimize seismological objectives. We propose to augment the InterMarsNet meteorological objectives by flying an additional 10-15 "MicroMet" landers equipped to measure surface pressure and nothing else. Surface pressure is the most fundamental meteorological parameter and it is the easiest to measure. The sensors are light, operate with minimal power, and do not require orientation or deployment. Consequently the landers can be very small (< 10 kg) and 10-15 of them can be delivered by a Med-lite launcher. This would enable global network science for meteorology and, when combined wAh the more sophisticated measurements from the InterMarsNet landers and the simultaneous temperature and dust profiling measurements from an atmospheric sounder aboard the orbiter, it would enable us to reconstruct global scale circulation patterns.
At different time periods in the future, Mars missions will overlap and previous studies indicate that during such periods existing deep space communication infrastructure will not be able to handle all Mars communication needs. A plausible solution is to perform optimal resource allocation for the Mars relay communication network; a network consisting of multiple surface units and orbiters on Mars and the Deep Space Stations. Unlike direct-to-earth, a relay communication, either in real-time or store-and-forward, can increase network science data return, reduce surface unit's direct-to-earth communication demands, and enable communication even when the surface unit is not facing Earth. It is the objective of this paper to take advantage of the relay operation to efficiently plan and schedule the network communications.
To increase the science rate for high data rates/volumes, Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define an edge to data center traffic shaping / steering transport capability featuring data event aware network shaping and forwarding. The keystone of this ESnet+JLab FPGA Accelerated Transport (EJFAT) is the joint development of an AI/ML directed dynamic compute work Load Balancer (LB) of UDP streamed data. The LB is a suite consisting of a Field Programmable Gate Array (FPGA) executing the dynamically configurable, low fixed latency LB data plane featuring real-time packet redirection and high throughput, and a control plane running on the FPGA host computer that monitors network and compute farm telemetry in order to make dynamic AI/ML guided decisions for destination compute host redirection/load balancing and destination resource provisioning. The LB provides for three-tier horizontal scaling across LB suites, core compute hosts, and CPUs within a host. The LB effectively provides seamless integration of edge/core computing to support direct experimental data processing for immediate use by JLab science programs and others such as the EIC as well as data centers of the future requiring high throughput and low latency for both hot and cooled data for both running experiment data acquisition systems and data center use cases.
Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking.
In this work we study the topological properties of temporal hypergraphs. Hypergraphs provide a higher dimensional generalization of a graph that is capable of capturing multi-way connections. As such, they have become an integral part of network science. A common use of hypergraphs is to model events as hyperedges in which the event can involve many elements as nodes. This provides a more complete picture of the event in comparison to the standard dyadic connection limitation of a graph. However, a common attribution to events is temporal information as an interval for when the event occurred. Consequently, a temporal hypergraph is born which accurately captures both the temporal information of events as well as their multi-way connections. Common tools for studying these temporal hypergraphs typically use summary statistics of snapshots from a sliding window procedure to capture changes in the underlying dynamics. However, these do not provide insight into how the changing structure of the hypergraph evolves and which components of the temporal hypergraph persist and are influential to the underlying system. To alleviate this need we leverage zigzag persistence from the field of Topological Data Analysis (TDA) to study the change in topological structure of time-evolving hypergraphs. We apply our pipeline to both a cyber security and social network dataset and show how the topological structure of their temporal hypergraphs change and can be used to understand the underlying dynamics.
SuperLab 2.0 (a five-lab demonstration) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, photovoltaics, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactors, control centers, and gas turbines, across five U.S. Department of Energy (DOE) national laboratories: National Laboratory of the Rockies (NLR), Idaho National Laboratory, National Energy Technology Laboratory, Lawrence Berkeley National Laboratory, and Sandia National Laboratories. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance DOE network, and are controlled via a centralized energy controller hosted at NLR's Advanced Research on Integrated Energy Systems facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility.
SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.
We report on the use of novel mathematical methods in hypergraph analytice over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech's ActiveDNS repository as hypergraphs allows us to fully represent the interactions between {\em collections} of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities. HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs, acting as a frontend. For the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.
Purpose: The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach: Here, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings: The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications: This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications: The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value: This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.
Sparse symmetric tensors are an important class of tensors, and their decompositions serve as powerful tools for revealing low-rank structures. This paper introduces SymProp, a novel approach for scaling sparse symmetric Tucker decomposition by propagating symmetry through intermediate computations. SymProp optimizes two key computational kernels: Sparse Symmetric Tensor Times Same Matrix chain (S3 TTMc) for Higher-Order Orthogonal Iteration (HOOI) and Sparse Symmetric Tensor Times Same Matrix chain Times Core (S3 TTMcTC) for Higher-Order QR Iteration (HOQRI). Our method employs a metaprogramming-based index iteration approach to efficiently handle the upper triangular parts of intermediate dense symmetric tensors. SymProp achieves up to 50.9× speedup over SPLATT and up to 360.8× over Compressed Sparse Symmetric (CSS) format on the S3 TTMc operation. Moreover, our S3 TTMc and S3 TTMcTC implementations support tensor orders four levels higher than state-of-the-art methods. Our HOQRI demonstrates superior scalability and up to a 33.6× speedup over optimized HOOI. By enabling more scalable Tucker decompositions for higher orders, decomposition ranks, and dimension sizes, SymProp opens new possibilities for analyzing complex hypergraph structures in fields such as network science, data mining, and machine learning.
Probabilistic breadth-first traversals (BPTs) are used in many network science and graph machine learning applications. In this paper, we are motivated by the application of BPTs in stochastic diffusion-based graph problems such as influence maximization. These applications heavily rely on BPTs to implement a Monte-Carlo sampling step for their approximations. Given the large sampling complexity, stochasticity of the diffusion process, and the inherent irregularity in real-world graph topologies, efficiently parallelizing these BPTs remains significantly challenging. In this paper, we present a new algorithm to fuse massive number of concurrently executing BPTs with random starts on the input graph. Our algorithm is designed to fuse BPTs by combining separate traversals into a unified frontier on distributed multi-GPU systems. To show the general applicability of the fused BPT technique, we have incorporated it into two state-of-the-art influence maximization parallel implementations (gIM and Ripples). Our experiments on up to 4K nodes of the OLCF Frontier supercomputer (32,768 GPUs and 196K CPU cores) show strong scaling behavior, and that fused BPTs can improve the performance of these implementations up to 34x (for gIM) and ~360x (for Ripples).
We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) computational simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee—a task motivated by a need for increased reliability, capacity, and performance. This task presented significant challenges: the need to move 29 million files twice under time pressure from aging storage hardware; a source file system bottleneck limiting throughput to 1.5 GB/s; frequent site maintenance windows; and the need for complete reliability at scale. We addressed these challenges using a simple replication tool that invoked Globus to transfer large bundles of files while tracking progress in a database, dynamically rerouting transfers to work around maintenance periods and file system limitations. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure. The replication tool is available at https://github.com/esgf2-us/data-replication-tools.
Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.
Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.
In February 2025, the DOE user facility Energy Sciences Network (ESnet) held a three-day Data and AI Workshop in Berkeley, California. The objective of the workshop was to identify challenges within ESnet that could be addressed through data-driven methods, to help define ESnet’s data-analysis requirements, and to shape its AI strategy, guiding data-stewardship efforts and the direction of AI research and AIOps exploration for ESnet7, the next iteration of ESnet’s network. This report summarizes the multi-faceted discussions and findings and presents a set of recommendations for next steps.