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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Generating SBoMs Utilizing Structured Threat Information Expression JSON bundles

With cyber-attacks on the rise, information is one of the first lines of defense. Now more than ever, security experts and developers need efficient ways to know if their software may be outdated or vulnerable to emerging threats. By generating a Software Bill of Material based on STIX2/JSON bundles, security experts and developers can ensure that they know where to find what can go wrong with their systems before the hackers do. As part of the Infrastructure Expression project, Idaho National Laboratory (INL) is currently working on ways that can aid in the development of efficient SBOMs using Structured Threat Information Expression (STIX). Doing so can provide an efficient first line of defense for detection and monitoring that can be easily used by cyber personnel from beginners to highly skilled experts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Tool for the Risk-Informed Management of Critical Mission Resilience

We describe a methodology and tool for the risk-informed management and planning of mission resilience. By mapping concepts of resilience onto the elements of a streamlined risk model we are able to tap the substantial portfolio of established risk concepts to provide rapid insights in the evaluation and high-level screening of prospective resilience enhancement measures. This provides a risk-informed, levelized basis for the comparison of disparate resilience solutions and the means of establishing preferences. The methodology begins with identification of critical missions met by a site, and establishment of the supporting physical assets. Scenarios that would result in failure of these assets are systematically identified. Each scenario comprises three elements: realization of a hazard or threat resulting in loss of resources (the current focus being on power, natural gas, and water) to the asset, failure of measures in place to protect the asset against those losses, and realization of the consequent impacts. These scenarios are quantified in terms of their probabilities of occurrence and the magnitude of the resultant consequences (mission outage time), allowing risk-prioritization to focus resilience enhancement considerations. What-If? analyses are conducted through adjusting elements of the risk calculation to reflect the deployment of prospective resilience measures, by which means the efficacies of each of those measures can be compared using common risk metrics across diverse resilience strategies. This paper will also describe the insights from example applications.

resilience, risk, risk assesment, energy, water↗
DOE OSTI2023

Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks

We develop an aggregate photovoltaic generation estimation methodology that uses diverse inputs and can reason on its current input-dependent predictive uncertainty. Named PVPHEst, for PhotoVoltaic Physics- & Harmonics-driven Estimator, the resulting tool is intelligently weighing and fusing information carried by the output of physics models, harmonics, and line sensors, using Bayesian neural networks and related techniques aimed at solving machine learning problems with intrinsic uncertainty quantification. As each of the three input classes carries heterogeneous information that only sheds light on one facet of the estimation problem but its value can diminish in the face of diverse grid phenomena, PV-PHEst with its estimation and uncertainty reasoning capabilities perform a nontrivial and potentially mission-critical task of value to grid operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The Importance of Inventory for Informing Waste Strategy - 20280

A waste inventory aims to present the best available information about the nature, quantity and location of radioactive wastes and materials at a facility, site or in a country. Quality inventory data is necessary for developing appropriate strategies and plans for managing radioactive wastes and materials, with high standards of protection for people and the environment. The inventory of wastes will change over time due to a wide range of technical, commercial or policy reasons, for example, if reactor lifetimes are extended, if decommissioning strategies change or if new technologies are introduced for managing waste. Inventory information can also improve as better waste characterisation data becomes available. At a facility or site level, good quality inventory data are needed to underpin decision making about the waste management options and to demonstrate compliance with the chosen treatment or disposal route. At a site and country level, knowledge of the inventory can drive the improvement to waste strategy; waste management asset and service planning; and implementation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Physics-informed Data-driven Degradation and Prognostic For Hydropower System

The hydropower system is a cyber-physical system, and the industry is experiencing high operation and maintenance (O&M) costs along with increasing unplanned outages, primarily due to aging equipment and new asset loading patterns. Current (O&M) practices in asset management are predominantly reactive rather than predictive and systematic. To address these challenges, a proposed physics-informed data-driven degradation model aims to simulate the health of components in various environments. Its purpose is to accurately characterize wear and fatigue in the pilot components of hydropower systems. Additionally, the model provides informed confidence in prognostic predictions concerning asset degradation and remaining life.

13 HYDRO ENERGY↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Nonlinear Information from DESI Luminous Red Galaxies: An Emulator-Based Analysis of Pre- and Post-Reconstruction Power Spectra

We present joint measurements of the pre- and post-reconstruction power spectra, $P_{\rm pre}$ and $P_{\rm post}$, together with their cross-power spectrum, $P_{\rm cross}$, for the Luminous Red Galaxies (LRGs) in the DESI Data Release 1 (DR1). We jointly analyse these observables with an emulator-based full-shape modeling framework, thereby, for the first time, we extract complementary nonlinear information from the galaxy density field before and after reconstruction in real survey data. Specifically, including $P_{\rm post}$ and $P_{\rm cross}$ in addition to $P_{\rm pre}$ (hereafter $P_{\rm all}$) yields an improvement of approximately $18$-$27\%$ in the $σ_8$ constraint in both $Λ$CDM and $w$CDM, depending on the redshift bin, relative to the $P_{\rm pre}$-only analysis with the cosmic microwave background distance priors (hereafter CMB). In $w$CDM, the joint CMB+$P_{\rm all}$ analysis can tighten the constraints on $w$ by approximately $5$-$15\%$ across the two LRG redshift bins, compared to the CMB+$P_{\rm pre}$ combination. Further incorporating the Type Ia supernova dataset and comparing the cosmological constraints in $w$CDM from each individual power-spectrum component with those from the full combination, we find that $P_{\rm all}$ consistently provides the tightest constraints. From the joint CMB+$P_{\rm all}$+DES-Dovekie dataset, we obtain $Ω_m = 0.314 \pm 0.0048$ and $w = -0.988 \pm 0.023$ for the \texttt{LRG1} sample, and $Ω_m = 0.318 \pm 0.0046$ and $w = -0.988 \pm 0.025$ for \texttt{LRG2}. These results demonstrate that combining pre- and post-reconstruction power spectra with their cross-correlation enables DESI to harvest additional nonlinear information, leading to tighter constraints on cosmological parameters.

Wang, Yuting [NAOC, Beijing; Beijing, GUCAS] (ORCI↗
DOE OSTI2026

A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint

The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Universal Spreading of Conditional Mutual Information in Noisy Random Circuits

For this work, we study the evolution of conditional mutual information (CMI) in generic open quantum systems, focusing on one-dimensional random circuits with interspersed local noise. Unlike in noiseless circuits, where CMI spreads linearly while being bounded by the light cone, we find that noisy random circuits with an error rate 𝑝 exhibit superlinear propagation of CMI, which diverges far beyond the light cone at a critical circuit depth 𝑡 𝑐 ∝ 𝑝 −1 . We demonstrate that the underlying mechanism for such rapid spreading is the combined effect of local noise and a scrambling unitary, which selectively removes short-range correlations while preserving long-range correlations. To analytically capture the dynamics of CMI in noisy random circuits, we introduce a coarse-graining method, and we validate our theoretical results through numerical simulations. Furthermore, we identify a universal scaling law governing the spreading of CMI.

decoherence↗
DOE OSTI2021

A Primer on Organizational Use of Energy Management and Information Systems (EMIS), Second edition

The document provides a high-level overview of Energy Management and Information Systems (EMIS), which are combined hardware and software products that comprise a broad family of tools and services to manage commercial building energy use. It provides a common reference that can be used to understand key distinguishing factors and core attributes of different solutions within the family of EMIS technologies. This is a 2021 update to the original 2015 edition.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Risk-Informed Initiating Events and Accident Response

Emerging nuclear technologies and advanced reactor developments have brought proposed changes to the regulatory framework which can be leveraged to improve safety implementation and regulatory oversight processes at existing operating nuclear power plants. Specifically, opportunities exist to make plants’ regulatory compliance processes more efficient. This can be done by decreasing reliance on purely deterministic and prescriptive approaches and expansion of the use of risk informed and performance-based approaches to demonstrate reactor safety while achieving economic gains and efficiencies. In this report, we researched possibilities of economic benefits potentially available from the application of concepts associated with a modernized regulatory framework developed for advanced reactors to the existing light water reactors. We used timing factors to demonstrate the complexity of the existing risk assessments and described how these complexities affect regulatory compliance activities at the plants. We also investigated regulatory framework for both existing and new reactors and provided an overview of potential improvements. Lastly, we evaluated various areas of existing plant operations where modernization of compliance activities can offer substantial benefits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Energy Management and Information Systems (EMIS) Specification and Procurement Support Materials

This annotated Request for Proposal (RFP) template includes an Energy Management and Information System (EMIS) technology specification, an ongoing services specification, RFP evaluation criteria, and more. It is designed to guide building professionals through the specification, procurement, and selection of an EMIS or related building performance monitoring and diagnostic technology.

building performance↗
DOE OSTI2024

Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Here, we present a novel physics-informed neural networks (PINNs) framework for modeling interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks for any two subdomains separated by a sharp interface such that the neural networks differ only through their activation functions while the other parameters remain identical. The performance of I-PINNs, conventional PINNs, and other existing domain-decomposition PINNs methods such as extended PINNs (XPINNs) and multi-domain PINN (M-PINN) is compared through several one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems. The results demonstrate that I-PINNs provides a root-mean-square-error accuracy, at least two orders of magnitude better than conventional PINNs and XPINNs at approximately one-tenth of the computational cost of conventional PINNs and half the cost of XPINNs. Additionally, while I-PINNs and M-PINN provide comparable accuracies, M-PINN is found to be approximately 50% more expensive.

42 ENGINEERING↗
DOE OSTI2020

Detecting and tracking drift in quantum information processors

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Generalized measure of quantum Fisher information

Here, we present a lower bound on the quantum Fisher information (QFI) which is efficiently computable on near-term quantum devices. This bound itself is of interest, as we show that it satisfies the canonical criteria of a QFI measure. Specifically, it is essentially a QFI measure for subnormalized states, and hence it generalizes the standard QFI in this sense. Our bound employs the generalized fidelity applied to a truncated state, which is constructed via the m largest eigenvalues and their corresponding eigenvectors of the probe quantum state ρ θ . Focusing on unitary families of exact states, we analyze the properties of our proposed lower bound, and demonstrate its utility for efficiently estimating the QFI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cyber-Informed Engineering Case Study of an Integrated Hydrogen Generation Plant

Strategies for securing digital instrumentation and control (I&C) systems within the nuclear industry are provided by multiple standards and guidance documents. However, since selection and use of security controls outlined in these documents are frequently only considered during or after installation, there are often limitations on their use, such as technological constraints related to design or operation. Furthermore, alternative controls intended to provide the same or similar security countermeasure as the primary control may also be infeasible at these stages, leaving the I&C system vulnerable to cyber-attacks. The limitations associated with ‘bolting on’ security controls late in the systems engineering lifecycle can be reduced by integrating Cyber-Informed Engineering (CIE) into the process. This paper evaluates the use of CIE during the high-level design stage of a hydrogen generation project where heat and electricity are provided by a nuclear power plant. Applying CIE to this project highlighted potential cyber vulnerabilities of the initial design, leading to recommendations for process flow and I&C system design modifications to reduce, and at times eliminate, the risk from both deliberate and unintentional cyber incidents.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗