Iterative Learning-Based Path Optimization With Application to Marine Hydrokinetic Energy Systems
Not provided.
Automation & Control Systems↗
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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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FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets
This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Furthermore, our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.
42 ENGINEERING↗
Situational Awareness of Network System Roles (SANSR)
In a large enterprise it is difficult for cyber security analysts to know what services and roles every machine on the network is performing (e.g. file server, domain name server, email server). Understanding the roles of the systems in the network provides analysts with a situational awareness that will allow them to detect consequential changes in the network, initiate an incident response plan, and optimize their security posture. Using the network flow data, already collected by most enterprises, we developed a tool that enables analysts to automatically detect/classify services and roles of every machine that’s operating on a network (e.g. file server, domain name server, email server) for better situational awareness of potential threats to the network. his tool queries Elasticsearch for network flow data, creates a temporal behavior model of each system, uses unsupervised machine learning to cluster the models with a set of labeled temporal behavior models, and the resulting information can be printed to the console or programmatically accessed. The results include the likelihood that a machine has a labeled role and lists other machines that are most similar in behavior.
Huffer, KellyM.↗
Anomalous And Normal High Performance Computing Datacenter Activities
This code provides annotation and an interface to 10+ hours of video activities in a high performance datacenter with 20+ different types of anomalous activities. The purpose is to enable machine learning for video surveillance systems in high performance computing centers. This is the first code of this type addressing the space of high performance computing datacenters.
Anderson, Matthew↗
pnnl/deps_arXiv2020
This software implements data-driven control and dynamic systems models using deep learning methods. The particular application is building temperature control.
Tuor, Aaron↗
ECP libraries and tools: An overview
The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.
97 MATHEMATICS AND COMPUTING↗
Measurement-induced entanglement phase transitions in variational quantum circuits
Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many-body systems and improve machine learning algorithms using near-term quantum computers. Prominent challenges associated with this family of quantum-classical hybrid algorithms are the control of quantum entanglement and quantum gradients linked to their classical optimization. Known as the barren plateau phenomenon, these quantum gradients may rapidly vanish in the presence of volume-law entanglement growth, which poses a serious obstacle to the practical utility of VQAs. Inspired by recent studies of measurement-induced entanglement transition in random circuits, we investigate the entanglement transition in variational quantum circuits endowed with intermediate projective measurements. Considering the Hamiltonian Variational Ansatz (HVA) for the XXZ model and the Hardware Efficient Ansatz (HEA), we observe a measurement-induced entanglement transition from volume-law to area-law with increasing measurement rate. Moreover, we provide evidence that the transition belongs to the same universality class of random unitary circuits. Importantly, the transition coincides with a “landscape transition” from severe to mild/no barren plateaus in the classical optimization. Our work may provide an avenue for improving the trainability of quantum circuits by incorporating intermediate measurement protocols in currently available quantum hardware.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Captain Electronics Technical Report
This report details the current layout of the electronics for the mini-CAPTAIN detector system, as well as the lessons learned during commissioning. We will present some electronics suggestions for future LArTPC systems. Mini-CAPTAIN is one of three detectors used in the Cryogenic Apparatus for Precision Tests of Argon Interactions with Neutrino (CAPTAIN) project at Los Alamos National Lab (LANL). The detector uses many of the same electronic components as current and developing LArTPC experiments. This includes the motherboards, service cards, amplifiers and ADC made by Brookhaven National Laboratory (BNL) and the front-end modules (FEM) made by Nevis Labs. The cold electronics performed well in both vacuum and cryogenic environments after months of exposure. Mini-CAPTAIN was tested with two liquid nitrogen and three liquid argon run cycles. The final testing was performed at the Weapons Neutron Research (WNR) facility at LANSCE. Mini-CAPTAIN was able to maintain a clean electrical signal through most of the test cycles. Most noise reduction was achieved with power source filtering and careful attention to the ground configuration. Direct oscilloscope probing on the front-end output provided the fastest analysis and correction of noise sources. Ground path assumptions and integrity were verified with frequent impedance checks. A high voltage break down event damaged some of the channels on the motherboards and a number of the wire decoupling capacitors. This temporarily introduced significant noise on the wire planes due to current leakage. The detector was taken apart and reassembled three times, with successful noise reduction after each activity. The current raw signal-to-noise ratio is 9 to 1.
42 ENGINEERING↗
Building an Adaptive Optics Demonstrator System
Adaptive optics is used to correct the wavefront of a beam of light. It originated in the field of astronomy to correct for atmospheric turbulence. This project was to construct an adaptive optics (AO) system kit from Thorlabs. An AO system consists of three parts: a deformable mirror (DM), a wavefront sensor (WFS), and control software. This system is to be used for future teaching purposes to help others learn about adaptive optics. The AO system came with instruction manuals, which were followed to build and calibrate the system. The instructions included building and aligning the system, how to install and use the software, and how to calibrate deformable mirror. A calibrated system for this project means having the deformable mirror imaged onto the wavefront sensor, with the center of the mirror on the center of the WFS. Also, a flat wavefront, which is achieved by eliminating the tip/tilt of the mirror. Eliminating the tip/tilt reduces the RMS amplitude to a value below 0.200 microns. This causes the wavefront to fold in on itself. We set up a focal plane detector behind beam splitter as a demonstration of what would be seen if this were used with a telescope. The image that should be seen on the focal plane detector is the beam with a diffraction pattern called and Airy Function. The AO system is now ready to be used to teach others about adaptive optics.
79 ASTRONOMY AND ASTROPHYSICS↗
FAIR data infrastructure and tools for AI-assisted streamflow prediction
Focal Area(s) Areas: We discuss how the integration of AI into Earth Science models can impact streamflow predictions at both the science and data levels. Doing so, we address cross-cutting needs related to the goal of making data FAIR (Findable, Accessible, Interoperable, and Re-usable [1]) for seamless use with Artificial Intelligence/Machine Learning (AI/ML) in Earth System Science at DOE. A novel idea is that AI/ML itself can help with the FAIR data goal and address issues in targeted areas e.g. missing data, data quality and reduction. In addition, the interpretability of results obtained with new AI methods is poised to impact broader scientific challenges in hydrology..
54 ENVIRONMENTAL SCIENCES↗
STNS01-21 BEE - FY21 P6-2: Archive, clone, and re-run workflows [Slide]
BEE will give ECP a tool that great simplifies the deployment of containerized workflows on the next generation of pre-exascale and exascale systems, as well as public and private clouds. BEE allows scientists to describe their workflow using the Common Workflow Language and then deploy that workflow across the entire spectrum of systems without having to learn the specifics of each container runtime, HPC resource manager, or cloud API. BEE also streamlines the curation and sharing of common workflows among the scientific community.
97 MATHEMATICS AND COMPUTING↗
Machine Learning Based Fault Detection And Location In Electric Power Distribution Systems.
Abstract not provided.
Reno, Matthew↗
Verification of Spent Fuel Inside Dry Storage Casks by Cask Top Fast Neutron Mapping (FY2023 Mid-Year Report)
This project is developing a prototype scanner array verification system for detection of missing fuel assemblies in spent-fuel storage casks. The prototype consists of six fast-neutron scintillator detectors mounted to a linear actuator frame that is placed on the top of a spent fuel cask to scan across all fuel assembly positions. The scanner array was assembled and tested at LLNL in FY2022. A field test schedule has been requested at the Idaho National Laboratory (INL) Cask Farm site for FY2023. Note that the Cask Farm contractor determines this scheduling and not INL directly. Further system automation will be designed and implemented with the goal of obtaining a level of system operation that meets IAEA needs. This includes integration of the scanner array and data-acquisition control software into a single interface for operator use. In addition, commercial operators and the IAEA may have special requirements for portability, shipping, lifting, and installation. Prior to the Field Test at INL, the system will be operated at LLNL to exercise lifting procedure and linear actuators, monitor stability of detector energy and pulse-shape discrimination calibration, and test system software integration efforts. Following the Field Test, we will present results and discuss the technology with the IAEA. We will incorporate additional improvements to the system based on lessons learned from the field test and feedback from the IAEA. If successful, the technology can be transferred to the IAEA or other stakeholders for assessment.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
FY2024 Mid-Year Report: Verification of Spent Fuel Inside Dry Storage Casks by Cask Top Fast Neutron Mapping
This project is developing a prototype scanner array verification system for detection of missing fuel assemblies in spent-fuel storage casks. The prototype consists of six fast-neutron scintillator detectors mounted to a linear actuator frame that is placed on the top of a spent fuel cask to scan across all fuel assembly positions. The scanner array was assembled and tested at LLNL in prior years. A field test schedule has been requested at the Idaho National Laboratory (INL) Cask Farm site for FY2024. Following the Field Test, we will present results and discuss the technology with the IAEA. The IAEA may have special requirements for portability, shipping, lifting, and installation. We will incorporate additional improvements to the system based on lessons learned from the Field Test and feedback from the IAEA. If successful, the technology can be transferred to the IAEA or other stakeholders for assessment.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes
This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
International Monitoring System 3-component seismic signal detection using the PhaseNet deep learning modelStephen Heck, Jorge Garcia, Rigobert Tibi Sandia National Laboratories
Explore the source record for details and available documents.
Heck, Stephen↗
Data-Driven Decision Control for Complex Systems (DnC2S) (Final Technical Report - Award DE-SC0021321)
This final technical report summarizes the key research outcomes achieved by the University of California at Santa Barbara Team of the “Data-Driven Decision Control for Complex Systems (DnC2S)” project under award number DE-SC0021321.
97 MATHEMATICS AND COMPUTING↗