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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

User Guide to the Facility Cybersecurity Capability Maturity Model (F-C2M2) Assessment

The Facility Cybersecurity Capability Maturity Model (F-C2M2) assessment was designed to help facilities address their unique cybersecurity challenges. Building upon the foundational principles of the U.S. Department of Energy (DOE) Cybersecurity Capability Maturity Model (C2M2), the F-C2M2 was explicitly tailored for federal facilities to help evaluate, benchmark, and enhance the cybersecurity capabilities of both their information technology (IT) and operational technology (OT) environments. This user guide was developed to facilitate a thorough understanding of the tool. Whether users are familiar with the original C2M2 or are new to the maturity model concept, this guide was designed to offer clarity, direction, and support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Connectivity Troubleshooting Guide for Advanced Electricity Meters

This guide is designed to walk users through a troubleshooting process for disconnected advanced electricity meters. The guide centers on two connectivity troubleshooting decision trees related to power issues and network connectivity issues, respectively. Additionally, it contains additional background information such as definitions, diagrams, and checklists that can help a user prepare to troubleshoot meter connectivity issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Cyber-Informed Engineering Power Generation Guide [Slides]

The CIE for Power Generation: Insights and Case Studies guide is being developed to assist engineers at utilities, asset owner-operators developers, and cybersecurity teams to build in robustness and cyber resiliency into their designs using cyber-informed engineering practices. This guide will break out these topics including use cases by chapters for areas such as Nuclear, IBRs, Geothermal, natural gas, etc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

MACCS User Guide - Version 5.2

MACCS is used by the Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This user guide is intended to assist analysts in understanding the MACCS/MACCS-UI User Interface (UI) model and to provide information regarding the code. This user guide version describes MACCS Version 5.2, model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

User’s Manual for RESRAD-RDD&IND Code Version 2: Vol. 2—User’s Guide for RESRAD-RDD&IND Code

Version 2.0 of the RESRAD-RDD&IND computer code is designed to support the implementation of protective action guides (PAGs) after a nuclear emergency incident including a radiological dispersal device (RDD) and/or an improvised nuclear device (IND) incident (EPA 2017). Eight different group types, addressing various decisions, are available for selection. The RESRAD-RDD&IND code calculates radiological doses, stay times, etc., for the selected group that the user wishes to focus on. (That is, the results for all the groups are not calculated simultaneously, and the input for those other groups do not matter, although some parameter values are shared between groups.) Version 2.0 has a user-friendly interface so that the RESRAD-RDD&IND code can be used with minimal training. For example, the user can select the major characteristics of the problem-event type, source term, and decision type from the left side of the interface and then calculate the results with the default assumptions for the exposure scenarios. More in-depth analysis would include specifying site-specific exposure scenario characteristics in the right side of the interface. The procedures for data entry and results viewing are self-explanatory. This is because common window maneuvering features and text instructions were incorporated in the interface design. General and context-specific help are available to aid users entering parameter values, as well. The RESRAD-RDD&IND computer code gives the user the option to select either an RDD or IND incident for analysis. For an RDD event analysis, 11 radionuclides (Am-241, Cf-252, Cm-244, Co-60, Cs-137, Ir-192, Po-210, Pu-238, Pu-239, Ra-226, and Sr-90) are included. These 11 radionuclides are the radionuclides most likely used for an RDD. More than 90 radionuclides can be selected for an IND event analysis. Initial default concentrations are provided for 44 radionuclides for a uranium-fueled IND event. These 44 radionuclides are those that would contribute significantly to the radiation dose associated with a uranium-fueled bomb detonation. The radionuclides generated from ingrowth of these 44 initial radionuclides are also automatically included in the analysis. Pu-239, Cs-134m, Ru-105, and Rb-89 and their progeny can be selected for analysis if they are detected and their concentrations are determined. This user’s guide, which is Volume 2 of the User’s Manual for RESRAD-RDD&IND Code Version 2, provides instructions to users on how to install the RESRAD-RDD&IND code, navigate the interface, and use the various features, including those discussed above, to set up an analysis and view/print the results in text outputs. Volume 1 of the User’s Manual for RESRAD-RDD&IND Code Version 2 (Yu et al. 2026), which contains descriptions of the methodology and theoretical basis for dose modeling and the mathematical equations implemented in the code, can be accessed and viewed through the Help menu in the code or can be downloaded from the RESRAD website (https://resrad.evs.anl.gov).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Concise Guide on Hydrogen for Ports

This Concise Guide on Hydrogen for Ports provides port stakeholders with a comprehensive overview of hydrogen as an emerging energy solution for maritime and land-based operations. It explains hydrogen’s fundamental properties, production pathways, and emissions profiles, while highlighting its potential benefits and associated challenges. The document explores practical applications for hydrogen at ports alongside considerations for storage, delivery, and integration. It also outlines implementation frameworks, stakeholder engagement approaches, and risk assessment methodologies to support informed decision-making. The guide aims to provide a pathway forward for ports that choose to consider implementation of hydrogen technology at their facility.

08 HYDROGEN↗
DOE OSTI2026

Distribution System Plan Template and Guide for Electric Utilities for Adaptation by States and Utilities [Slides]

LBNL researchers developed a template and companion guide for structuring distribution system plans. The template covers 10 key sections in utility distribution system plans and underlying topics: -Executive Summary -Planning Objectives -Current Distribution System -Planning Approach -Asst Management, Reliability and Resilience -O&M Expenses -Capacity Expansion Planning -Solution Identification -Cost-effectiveness -Implementation For each section in the template, the companion guide provides: a description of the topics covered and their importance; the type of content to include in the plan; an example utility approach; and references to other utility plans that serve as models for the topic.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗
DOE OSTI2022

Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning

Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of the Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

Jiang, Peishi↗
DOE OSTI2020

Physics-guided Deep Learning for Power System State Estimation

In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Laser Guide Star for Large Segmented-aperture Space Telescopes. I. Implications for Terrestrial Exoplanet Detection and Observatory Stability

Precision wavefront control on future segmented-aperture space telescopes presents significant challenges, particularly in the context of high-contrast exoplanet direct imaging. We present a new wavefront control architecture that translates the ground-based artificial guide star concept to space with a laser source on board a second spacecraft, formation flying within the telescope’s field of view. We describe the motivating problem of mirror segment motion and develop wavefront sensing requirements as a function of guide star magnitude and segment motion power spectrum. Several sample cases with different values for transmitter power, pointing jitter, and wavelength are presented to illustrate the advantages and challenges of having a non-stellar-magnitude noise limited wavefront sensor for space telescopes. These notional designs allow increased control authority, potentially relaxing spacecraft stability requirements by two orders of magnitude and increasing terrestrial exoplanet discovery space by allowing high-contrast observations of stars of arbitrary brightness.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Electron and Proton Heating in Transrelativistic Guide Field Reconnection

The plasma in low-luminosity accretion flows, such as the one around the black hole at the center of M87 or Sgr A* at our Galactic Center, is expected to be collisioness and of two temperatures, with protons hotter than electrons. Here, particle heating is expected to be controlled by magnetic reconnection in the transrelativistic regime ${\sigma }_{w}\sim 0.1\mbox{--}1$, where the magnetization ${\sigma }_{w}$ is the ratio of magnetic energy density to plasma enthalpy density. Using large-scale 2D particle-in-cell simulations, we explore for a fiducial ${\sigma }_{w}=0.1$ how the dissipated magnetic energy is partitioned between electrons and protons as a function of ${\beta }_{{\rm{i}}}$ (the ratio of proton thermal pressure to magnetic pressure) and of the strength of a guide field ${B}_{{\rm{g}}}$ perpendicular to the reversing field B 0. At low ${\beta }_{{\rm{i}}}\ (\lesssim 0.1)$, we find that the fraction of initial magnetic energy per particle converted into electron irreversible heat is nearly independent of ${B}_{{\rm{g}}}/{B}_{0}$, whereas protons are heated much less with increasing ${B}_{{\rm{g}}}/{B}_{0}$. As a result, for large ${B}_{{\rm{g}}}/{B}_{0}$, electrons receive the overwhelming majority of irreversible particle heating (~93% for ${B}_{{\rm{g}}}/{B}_{0}=6$). This is significantly different than the antiparallel case ${B}_{{\rm{g}}}/{B}_{0}=0$, in which irreversible electron heating accounts for only ~18% of the total particle heating (Rowan et al. 2017). At ${\beta }_{{\rm{i}}}\sim 2$, when both species start already relativistically hot (for our fiducial ${\sigma }_{w}=0.1$), electrons and protons each receive ~50% of the irreversible particle heating, regardless of the guide field strength. Our results provide important insights into the plasma physics of electron and proton heating in hot accretion flows around supermassive black holes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Role of Parallel Solenoidal Electric Field on Energy Conversion in 2.5D Decaying Turbulence with a Guide Magnetic Field

We perform 2.5D particle-in-cell simulations of decaying turbulence in the presence of a guide (out-of-plane) background magnetic field. The fluctuating magnetic field initially consists of Fourier modes at low wavenumbers (long wavelengths). With time, the electromagnetic energy is converted to plasma kinetic energy (bulk flow+thermal energy) at the rate per unit volume of J· E for current density J and electric field E. Such decaying turbulence is well known to evolve toward a state with strongly intermittent plasma current. Here we decompose the electric field into components that are irrotational, E ir , and solenoidal (divergence-free), E so . E ir is associated with charge separation, and J · E ir is a rate of energy transfer between ions and electrons with little net change in plasma kinetic energy. Therefore, the net rate of conversion of electromagnetic energy to plasma kinetic energy is strongly dominated by J · E so , and for a strong guide magnetic field, this mainly involves the component E so,∥ parallel to the total magnetic field B. We examine various indicators of the spatial distribution of the energy transfer rate J ∥ · E so,∥ , which relates to magnetic reconnection, the best of which are (1) the ratio of the out-of-plane electric field to the in-plane magnetic field, (2) the out-of-plane component of the nonideal electric field, and (3) the magnitude of the estimate of current helicity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Abstract. Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the conterminous United States (CONUS). For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting “horizon curves” specify for each dam the inferred inflow forecast horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium- to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (1553±50 out of 1927 dams with at least 10 Mm3 storage capacity) adopt medium- to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than 6 weeks ahead) are detected in the operations of reservoirs located in high-elevation regions of the western US, where snowpack information likely guides the release. A simulation exercise conducted on four key western US reservoirs indicates that forecast-informed models of reservoir operations may outperform models that neglect the horizon curve – including during flood and drought conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Cyber-Informed Engineering (CIE) Power Generation Guide [Slides]

This guide offers suggestions for applying CIE principles to technologies used to generate electric power. It addresses issues of design, implementation, and maintenance, preemptively addressing cybersecurity threats to electric generation. The intended audience for this guide includes practitioners across the energy and cybersecurity sectors, such as energy industry professionals (e.g., engineers, system designers, operators, and researchers) and cybersecurity experts (e.g., communication system designers, information technology/operational technology [IT/OT] administrators, and penetration testers).

13 HYDRO ENERGY↗
DOE OSTI2026

Cyber-Informed Engineering: Standards Development Organization Quick Start Guide

Cyber-Informed Engineering (CIE) is an emerging methodology focused on identifying and reducing high-consequence events that may affect physical critical infrastructure systems as a result of their dependence on digital technology. CIE, developed by National Laboratories and promoted by the Department of Energy (DOE), incorporates consequence-focused planning into the design and engineering process from the earliest stages of a project. This guide provides a concise overview of CIE and offers practical insight into how Standards Development Organizations (SDOs) can interpret CIE principles and apply those concepts in updates to various standards. It is important to remember that CIE extends beyond a compliance checklist, emphasizing a broader, interpretive approach. Instead, it encourages an interpretive mindset - a "turning of 'what if' to 'even if'" approach that anticipates and engineers out high-consequence events. The authors encourage SDOs to establish and promote CIE principles as enhancements for more resilient-by-design outcomes across critical infrastructure energy sectors. The 12 core principles of CIE outline specific behaviors and actions that SDOs and engineers may adopt to enhance system resilience. This guide applies these principles in a manner intended to be relevant across various technologies and threat landscapes. We welcome institutions and vendors to identify new or different framework alignments and mappings as we collectively work towards a safer and more reliable digital landscape.

97 MATHEMATICS AND COMPUTING↗