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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We suggest a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118-and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

alternating direction method of multipliers↗
DOE OSTI2022

The Demonstration of Power over Ethernet (PoE) Technologies in Commercial and Institutional Buildings

Power over Ethernet (PoE) technologies are currently ubiquitous in offices in the form of phones, access points, and IP security cameras. They are systems that are familiar to and managed by on-site IT staff. As low voltage DC-power capabilities have increased with the advancement of IT network switches within offices, PoE devices are now being integrated across additional building systems, including lighting, computers, and HVAC controls. The connection of PoE devices to network switches offers the opportunity for energy monitoring and management to be performed through IT network management software. Data and results from technology validations of PoE equipment remain very limited. This lack of information hinders expansion of PoE as an energy management strategy as its benefits and challenges have been insufficiently quantified in real-world scenarios. This final report presents the findings of a DOE/State of Minnesota-funded project that demonstrated and assessed the energy and non-energy benefits of Power over Ethernet (PoE) technologies at six sites: two offices, three schools, and a hotel. The goals of the demonstrations were to: (1) assess the receptiveness of commercial and institutional markets to these new technologies, (2) demonstrate the feasibility of PoE technologies within standard design/construction practices and commercial codes, (3) assess the energy and cost savings opportunities of these technologies, (4) provide energy management opportunities where not typically available, and (5) formulate approaches to spur adoption of these technologies in the marketplace.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Icypaw

The Intermode Communication Protocol Wrapper, or Icypaw, structures and facilitates communication between endpoints in a network. It is used to connect various independent software and hardware components into one loosely organized system controllable from one or more locations. The code uses the MQTT protocol to communicate and enhances it with a useful framework. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8293 O

Van Der Wall, JayW.↗
DOE OSTI2021

Short Term Plasticity for Artificial Neural Networks

Achieving efficient learning for AI systems was identified as a major challenge in the DOE's recently released, AI for Science , report. The human brain is capable of efficient and low-powered learning. It is likely that implementing brain-like principles will lead to more efficient AI systems. In this LDRD, I aim to contribute to this goal by creating a foundation for implementing and studying a brain phenomenon termed short term plasticity (STP) in spiking artificial neural networks within Sandia. First, data collected by the Allen Institute for Brain Science (AIBS) was analyzed to see if STP could be classified into types using the data collected. Although the data was inadequate at the time, AIBS has updated their database and created models that could be utilized in the future. Second, I began creating a software package to assess the ability of a Boltzmann machine utilizing STP to sample from national security data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MiniMOD

SAND2025-03854O MiniMod is a user-friendly software tool designed to assess the performance of high-performance computing (HPC) systems. Researchers can use the program to test communication methods and computational tasks to understand how different setups can affect application efficiency. This software is particularly useful for optimizing network performance in scientific research, simulations, and data analysis. MiniMod‘s flexible design allows users to make informed decisions about their computing environments, which can enhance productivity and results in real-world applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dosanjh, Matthew [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2026

EVs-at-RISC: A Secure and Resilient Interoperable SCM Control System Architecture for Electric Vehicle’s-at-Scale (Final Technical Report)

The EVs-at-RISC project was a five-year research, development, and demonstration initiative to create foundational tools for utility-scale fleet aggregation and Smart Charge Management (SCM) of Electric Vehicles (EV), Electric Vehicle Charging Infrastructure (EVCI), and related Distributed Energy Resources (DER). Rather than seeking to develop and demonstrate highly perfected SCM algorithms and control strategies, this project instead focused on creating foundational software solutions that enable unprecedented digital interoperability across the communications technologies and vendor platforms used to manage EV , EVCI, and DER, as well as existing energy management infrastructure operated by utilities, grid operators, and aggregators. This project then extends these novel interoperability capabilities to develop and deploy powerful middleware abstractions across grid edge networks and EVCI/DER fleet aggregations incorporating modern software tools and best practices, such as CI/CD, to bring the immense capabilities of infrastructure-as-code and policy-as-code to modern grid edge network environments. This addresses the foremost systemic issues preventing realization of any net operational benefits from scaled deployment of behind-the-meter EV, EVCI, and DER assets in electric power grids and markets today. The results of this approach and project unlock massive potential for new SCM capabilities to be easily prototyped, evaluated, and deployed at-scale within the existing grid edge network infrastructure and EVCI/DER technology ecosystem. The EVs-at-RISC project achieves this by extending Open Field Message Bus (OpenFMB), a conceptual model for digital interoperability and distributed intelligence in traditional front-of-meter utility SCADA networks, validating our hypothesis that OpenFMB could be similarly used to solve systemic digital interoperability issues in behind-the-meter environments and unlock real-world utility-scale SCM capabilities without requiring any new proprietary vendor solutions or significant infrastructure reconfiguration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Securing Inverter Communication: Proactive Intrusion Detection and Mitigation System to Tap, Analyze, and Act

The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.

14 SOLAR ENERGY↗
DOE OSTI2023

VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose the Distributed Smart City framework for Vision, or VDiSC. VDiSC serves as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. VDiSC additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. VDiSC ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), VDiSC also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.

Brogan, Joel↗
DOE OSTI2020

Advanced Tri-lab Software Environment (ATSE)

The Advanced Tri-lab Software Environment (ATSE) is an effort to build an open, modular, extensible, community-engaged, and vendor-adaptable software ecosystem that enables the prototyping of new technologies for improving the ASC computing environment. The initial target for ATSE is to accelerate the maturity of the Arm ecosystem for supporting ASC computing and the high-performance computing community more broadly. ATSE provides an integrated and optimized software stack that includes: 1) Application development environment and libraries including compilers, math libraries, tools, MPI, and OpenMP, 2) Low-level system software including optimized Linux, network stack, file systems, containers, and virtual machines, 3) Job scheduling and management including workload manager, application launcher, and user tools, and 4) System administration and management tools supporting booting, monitoring, and operating system image management. SAND2020-12377 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Younge, Andrew J.↗
DOE OSTI2026

Human Supervision of Autonomous Vehicle Fleet Operations and Associated Passenger Communications: Preprint

Advances in automated vehicle (AV) technology and expanded operations are rapidly emerging with Automated Mobility District (AMD) deployments in global cities. NLR's AMD research addresses critical elements of human supervision of AV fleet operations and associated passenger communications for vehicles in which no driver or safety attendant is present. Although sufficiently advanced AVs no longer have direct oversight by a driver, fleet management remains staffed with operations personnel at the operations command and control (OCC) facility. This paper examines the functionality of the OCC, drawing comparisons of how automated train control and automated people mover OCCs operate. Within an AMD, the OCC manages various vehicle types, sizes, and operational modes, including on-demand and fixed route service, to facilitate a 'network of networks' for transport within a metropolitan area. The OCC serves as oversight for multiple AV fleets assisting AVs via remote operation of vehicles, communication, and dispatching personnel to resolve problems. The OCC also coordinates system operation, geographically staging vehicles, and managing weather, police, and emergency events. Informed by traffic management center (TMC) strategies using highly integrated software and communications, OCCs facilitate seamless information flows. OCC personnel remotely assist passengers and oversee multi-party operation to ensure safety and security. Although social norms mitigate large-capacity unattended vehicle operations, social interaction in multi-party automated small vehicles has little precedent. This poses a new frontier for society and requires research to effectively understand and manage. Future research will monitor OCC implementations, passenger interfaces, and deployment scaling of initial AMD systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Security Vulnerability and Mitigation in Photovoltaic Systems

Software and firmware vulnerabilities pose security threats to photovoltaic (PV) systems. When patches are not available or cannot be timely applied to fix vulnerabilities, it is important to mitigate vulnerabilities such that they cannot be exploited by attackers or their impacts will be limited when exploited. However, the vulnerability mitigation problem for PV systems has received little attention. This paper analyzes known security vulnerabilities in PV systems, proposes a multi-level mitigation framework and various mitigation strategies including neural network-based attack detection inside inverters, and develops a prototype system as a proof-of-concept for building vulnerability mitigation into PV system design.

14 SOLAR ENERGY↗
DOE OSTI2023

ZPAL v.1.0.0

SAND2024-01003O ZPAL is a Python software development kit designed for use by network automation engineers. It is an application programming interface (API) wrapper that is compatible with ZPE System's Nodegrid API. ZPE produces networking equipment. ZPAL simplifies connections to the ZPE Nodegrid API and makes configuration changes on the associated networking equipment. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hill, Roscoe↗
DOE OSTI2024

DER Translate

SAND2024-13723O This software translates and maps any .xlsx-based distributed energy resource (DER) device from the SunSpec certification registry (https://sunspec.org/certified-registry/) to a JSON-based DER models map. It is then read by DER monitoring tools for applications such as network intrusion detection and system health monitoring. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fragkos, Georgios↗
DOE OSTI2020

The Nuclear Digital I&C System Supply Chain Cyber-Attack Surface

The nuclear supply chain attack surface is a large, complex network of interconnected stakeholders and activities. The global economy has widened and deepened the supply chain, resulting in larger numbers of geographically dispersed locations and increased difficulty ensuring the authenticity and security of digital assets. Although the nuclear industry has made significant strides in securing facilities from cyber-attacks, the supply chain remains vulnerable. This paper outlines supply chain threats and vulnerabilities and provides a Digital I&C System Supply Chain Cyber-Attack Surface diagram to illustrate the complexity of securing hardware, firmware, software, and system information throughout the entire supply chain lifecycle. The knowledge presented in this paper provides a foundation to use in cybersecurity supply chain risk analysis and to guide future supply chain research and development efforts leading to enhancement of a nuclear facility’s overall security posture.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

scANN

SAND2025-00656O scANN, also known as sampling by coinflips artificial neural networks, is a software tool that estimates uncertainty in artificial intelligence by performing Monte Carlo analysis on the weight matrices of feedforward neural networks. This computationally intensive process aims to explore the potential value added by future probabilistic hardware. The program’s output helps researchers gain insights into how probabilistic neural networks work. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2024

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. Here we present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tight Practical Bounds for Subgraph Densities in Ego-centric Networks

SAND2025-11782O Tight Practical Bounds for Subgraph Densities in Ego-centric Networks is a software tool for calculating the “subgraph spread ratio” for social network analysis. This value is useful in network analysis for determining the amount of exogenous and endogenous pressure on a graph. It can distinguish between networks coming from different sources, e.g. distinguishing a graph of Facebook data versus a graph of Wikipedia data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Mattes, Connor↗