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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Isotopic gamma lines for identification of shielding materials

Identifying the constituting materials of concealed objects is crucial in a wide range of sectors, such as medical imaging, geophysics, nonproliferation, national security investigations, and so on. Existing methods face limitations, particularly when multiple materials are involved or when there are challenges posed by scattered radiation and large areal mass. Here we introduce a novel brute-force statistical approach for material identification using high spectral resolution detectors, such as HPGe. The method relies upon updated semianalytic formulae for computing uncollided flux from source of gamma radiation, shielded by a sequence of nested spherical or cylindrical materials. These semianalytical formulae make possible rapid flux estimation for material characterization via combinatorial search through all possible combinations of materials, using a high-resolution HPGe counting detector. An important prerequisite for the method is that the geometry of the objects is known (for example, from X-ray radiography). We demonstrate the viability of this material characterization technique in several use cases with both simulated and experimental data in spherical geometry.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Logical Activation Functions v.1.1

SAND2024-01501O Logical Activation Functions software is a PyTorch implementation from the paper, "Logical Activation Functions for Training Arbitrary Probabilistic Boolean Logic." The activation functions approximate logit-space marginalization of probabilistic truth tables from probabilistic interpretations of inputs. They also provide a general methodology to approximate logical relationships between abstract antecedents and consequents for machine learning architectures. They do not target any specific application or use-case. By training probabilistic truth tables, these activation functions can capture more expressive relationships in a neural network than typical elementwise activation functions. This code is only designed for a single compute node with a GPU and is limited to machine learning architectures than can fit within the memory of a single GPU. 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.

Duersch, Jed↗
DOE OSTI2025

MJO CP Tensor Decompositions

SAND2025-14273O MJO CP Tensor Decompositions is a Python tool for computing canonical polyadic (CP) decompositions of MERRA2 observational data to understand the onset and evolution of the Madden-Julian Oscillation (MJO). 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.

Bull, Diana [Sandia National Lab. (SNL-CA), Liverm↗
DOE OSTI2026

nnopf

SAND2026-19433O The nnopf tool addresses the optimal power flow (OPF) problem by optimizing electricity delivery from generating plants to consumers. At the same time, it minimizes costs and adheres to power grid constraints. While traditional methods for solving OPF can be computationally intensive, the tool mitigates this challenge by training neural networks on power grids to predict optimal solutions for the OPF problem. 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.

Hart, William [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2021

DOE Fffice of Nuclear Energy cybersecurity research, development and demonstration program plan

This document describes the Cybersecurity Research Development and Demonstration (RD&D) Program, established by the Department of Energy Office of Nuclear Energy (NE) to provide sciencebased methods and technologies necessary for cost-effective, cyber-secure digital instrumentation, control and communication in collaboration with nuclear energy stakeholders. It provides an overview of program goals, objectives, linkages to organizational strategies, management structure, and stakeholder and cross-program interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

LANL Interactive Display (LID) - DC Vault Updates and FY24 proposals

The LANL Interactive Display (LID) is an interactive touchscreen with a custom-built application designed to provide a geographic-based, virtual tour of the Laboratory’s organizations, facilities, programs, products, and capabilities. The Display is mounted on a portable stand and displayed in the classified vault of the Forrestal Building in Washington, D.C. The October 2023 updates include the latest inputs, including the addition of Nuclear Security Enterprise (NSE) information. Other major upgrades are detailed below.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Attack Surface of Wind Energy Technologies in the United States

Low cost, reliable electrical energy production from wind relies upon automation and control systems, arguably more so than traditional thermal generation. These same systems, however, can serve as the target of adversaries’ cyber-attacks. Idaho National Laboratory (INL), at the request of the Department of Energy’s (DOE’s) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) and Energy Efficiency and Renewable Energy’s (EERE’s) Wind Energy Technologies Office (WETO), evaluated a generalized wind plant architecture to understand the classes of potential threat actors and the vectors that could enable a cyber-attack. This evaluation explores the attack surface of a representative wind plant, identifying potential methods and vectors that an adversary could leverage to conduct a cyber-attack. Included in this assessment are some recommended mitigations and approaches. Each recommendation requires a full security evaluation, cost/benefit analysis, and risk analysis by each owner and operator.

17 WIND ENERGY↗
NASA NTRS1987

Documenting the Development of Software

Some routine supervisory functions performed automatically. Program Management Facility (PMF) computer program integrated software-development and control system. Applicable to large software systems involving as many as several hundred programmers and one million lines of codes, it ensures timely and orderly planning, development, implementation, and documentation of software. Designed as support tool. Has many features providing efficient processing and utilization of space for development programmer. Incorporates security system to prevent improper maintenance. Provides full set of cross-referenced reports and supervisory functions for detailed management information. Written in assembler. IBM program TSO required.

Source record↗
DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗
DOE OSTI2025

LandScan HD: a high-resolution gridded ambient population methodology for the world

Unwarned population distributions accounting for routine human activities are needed to address many global human security challenges, including disasters, conflict, and infrastructure demand. LandScan High Definition (LSHD) supports this need through gridded ambient population estimates that measure average human presence between daytime and nighttime at a high spatial resolution of 3 arcseconds (approximately 90 m). Although LSHD has traditionally been produced on a country-specific basis, advances in global foundational data and computational resources now enable scaling its methodology to the world. Combining aspects of top-down and bottom-up gridded population methods, LSHD allocates subnational population totals from authoritative statistics to built-up areas based on occupancy estimates for multiple facility types (e.g., residential, commercial) and then reaggregates these estimates to a global population grid. We scale this approach by organizing the LSHD data stack into a 1° resolution tileset of vector analytic features, enabling an efficient and repeatable workflow for all countries worldwide. Examining the Philippines as an output of the global LSHD baseline dataset, we contrast unwarned and residential (WorldPop) population distributions by (1) exploring a practical application of flood risk assessment and (2) evaluating their congruence with outcomes of collective human activities (subnational CO 2 emissions). Finally, we discuss plans to address current LSHD limitations through data/modeling and uncertainty quantification improvements and provide outlook for workflow automation and extending the model to social, demographic and economic population characteristics.

Building morphology↗
DOE OSTI2020

Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

active distribution networks↗
DOE OSTI2021

Restoration Strategy for Active Distribution Systems Considering Endogenous Uncertainty in Cold Load Pickup

Cold load pickup (CLPU) phenomenon is identified as the persistent power inrush upon a sudden load pickup after an outage. Under the active distribution system (ADS) paradigm, where distributed energy resources (DERs) are extensively installed, the decreased outage duration can induce a strong interdependence between CLPU pattern and load pickup decisions. In this paper, we propose a novel modelling technique to tractably capture the decision-dependent uncertainty (DDU) inherent in the CLPU process. Subsequently, a two-stage stochastic decision-dependent service restoration (SDDSR) model is constructed, where first stage searches for the optimal switching sequences to decide step-wise network topology, and the second stage optimizes the detailed generation schedule of DERs as well as the energization of switchable loads. Further, to tackle the computational burdens introduced by mixed-integer recourse, the progressive hedging algorithm (PHA) is utilized to decompose the original model into scenario-wise subproblems that can be solved in parallel. The numerical test on modified IEEE 123-node test feeders has verified the efficiency of our proposed SDDSR model and provided fresh insights into the monetary and secure values of DDU quantification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Python HITMIX

SAND2024-10158O Python HITMIX is a software package for computing hitting time moments of vertices in a graph. Hitting time moments can be used to rank the strengths of relationships between vertices in a graph. Examples are provided for computing and using hitting time moments on generic graphs and similarity graphs arising from the analysis of text documents in information retrieval 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.

Dunlavy, Daniel↗
DOE OSTI2024

YOLO for Radio Frequency Signal Classification

Radio frequency signal classification plays a pivotal role in various applications, including spectrum management, wireless security, and cognitive radio. Extant signal classification methods require significant data throughput and are not multilabel. We propose a novel approach to radio frequency signal classification by leveraging the You Only Look Once (YOLO) object detection method. YOLO is a state-of-the-art deep learning model renowned for its real-time object detection capabilities in computer vision applications. We adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image. Index Terms—radio-frequency analysis, object detection, neural networks, machine learning, deep learning.

42 ENGINEERING↗
DOE OSTI2021

A Proxy Signature-Based Drone Authentication in 5G D2D Networks

5G is the beginning of a new era in cellular communication, bringing up a highly connected network with the incorporation of the Internet of Things (IoT). To flexibly operate all the IoT devices over a cellular network, Device-toDevice (D2D) communication standard was developed. However, IoT devices such as drones utilizing 5G D2D services could be a perfect target for malicious attacks as they pose several safety threats if they are compromised. Furthermore, there will be heavy traffic with an increased number of IoT devices connected to the 5G core. Therefore, we propose a lightweight, fast, and reliable authentication mechanism compatible with the 5G D2D ProSe standard mechanisms. Specifically, we propose a distributed authentication with a delegation-based scheme instead of the repeated access to the 5G core network key management functions. Hence, a legitimate drone is authorized by the core network via offering a proxy signature to authenticate itself to other drones. We implemented the proposed protocol in ns-3 that supports 5G D2D-based communication. We also conducted computational calculations on the RaspberryPi3 IoT device to mimic the drone calculation process and delays. The results demonstrate that the proposed protocol is lightweight and reliable

5G security↗
DOE OSTI2021

Universal Utility Data Exchange (UUDEX) Phase 4 Demonstration Environment and Results

This report provides an overview of the environment used by the Pacific Northwest National Laboratory (PNNL) in the Universal Utility Data Exchange (UUDEX) Phase 4 demonstration, and description of the demonstrations performed in the environment. The UUDX Phase 4 demonstration took place on March 31, 2021, with representatives from PNNL and the two project sub-contractors, OATI and MITRE, performing the demonstration for members of the UUDEX Industry Advisory Board (IAB). The demonstration tasks were based on the previous Phase 3 demonstration but conducted in a multi-site environment. Additional demonstrations showed features that were developed since the Phase 3 demonstration. The development environment was constructed by PNNL programming staff using the previously developed UUDEX Functional Design, Protocol Design, and Workflow Design documents as a starting point. The environment was augmented with subject creation management and access control security data structures and workflows, and data exchange structure documents developed as part of Phase 3 and Phase 4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

gollnlp and magos (Final Scientific/Technical Report)

In 2018, the Advanced Research Project Agency – Energy (ARPA-E) launched the Grid Optimization Competition (GOC), a series of competitive challenges intended to accelerate innovation in decision support software used to schedule power grid operations, making them as efficient as possible, while respecting operational constraints of power equipment and operational security. This report covers the participation of teams led by the Lawrence Livermore National Laboratory (LLNL) in Challenge 1 and Challenge 2 of the competition.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

​​Integrity Enhancing Protocols (IEP) Evaluation Framework​

​Protocols play an essential role in Advance Reactor systems. A diverse set of protocols are available to these reactors. Advanced Reactors benefit from technologies that can minimize their resource utilization and costs. Evaluation frameworks are often used when assessing protocols and processes related to cryptographic security systems. The following report discusses the various characteristics associated with these protocol evaluation frameworks, and derives a novel evaluative framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗