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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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DOE OSTI2021

Definitions for Testing Whether Evaluated Nuclear Data Relative Uncertainties are Realistic in Size

This document describes various tests that can be used to check whether evaluated relative uncertainties stored in nuclear-data covariances are realistic. To be more specific, these tests check whether nuclear-data uncertainties could be either over- or under-estimated given the input that usually enters the evaluation of nuclear-data mean values and covariances. Warning and error messages on the reliability of nuclear-data uncertainties will result from these tests. If uncertainties of one specific nuclear-data observable trigger warning messages from multiple tests, an evaluator should counter-check the reliability of the relative uncertainties of this particular nuclear-data covariance matrix.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization

Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

RELIABILITY MODELS OF AGING PASSIVE COMPONENTS INFORMED BY MATERIALS DEGRADATION METRICS TO SUPPORT LONG-TERM REACTOR OPERATIONS

Paper describes a methodology for the synthesis of nuclear power plant service data with expert-elicited materials degradation information to estimate the future failure rates of passive components. This method should be an important resource to long-term plant operations and reactor life extension. Conventional probabilistic risk assessments (PRAs) are not well suited to addressing long-term reactor operations. Since passive structures and components are among those for which replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant; yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA presents challenges. Service data to quantify component reliability models are sparse, and this is exacerbated by the greater data demands of age-dependent reliability models. Another factor is that there can be numerous potential degradation mechanisms associated with the materials and operating environment of a given component. This deepens the data problem since risk-informed management of component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates metrics of materials degradation susceptibility with available plant service data to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by predicted long-term operational risk.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Analysis of the electrical double layer using electrochemical X-ray photoelectron spectroscopy

The element-sensitivity of X-ray spectroscopies offers the potential to disentangle the individual chemistries of water, ions, and adsorbates at the electrode-electrolyte interface in an element-by-element manner. However, targeted experimental design is needed to establish interface-sensitive in situ X-ray spectroscopy in a realistic electrochemical environment. Here, we demonstrate how electrochemical X-ray photoelectron spectroscopy (EC-XPS) in the dip-and-pull geometry can be used to specifically probe the behavior of ions in the electrical double layer. Taking the case study of a polycrystalline Au foil in 50 mM KClO 4 electrolyte, we tracked the electrochemical response of interfacial K + cations across a broad potential range. We show how, in combination with modeling, key parameters such as the potential of zero charge (PZC), ion packing behavior, dielectric saturation, and the electrostatic potential decay in the double layer can be extracted from the data. Importantly, we also analyze how the experimental conditions and non-idealities can influence the results and put forward criteria for reliable experimentation and data analysis.

Dip-and-pull↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Task 1 Report - Assessment of Data Availability to Inform Energy Planning Analyses: Energy Alternatives Study for the Lao People's Democratic Republic: Smart Infrastructure for the Mekong Program

In an effort to address concerns such as energy security, reliability, affordability, and other objectives, the Government of the Lao People's Democratic Republic (Lao PDR) is seeking to advance its expertise and experience in energy system analysis and planning to explore energy alternatives. Assessing the potential and alternatives for deploying energy technology options is often an early step - and, in most cases, an ongoing process - in planning for the development of the energy sector as a whole. Reliable and robust data are crucial to conducting these types of planning-related analyses in a transparent manner that builds confidence among power sector stakeholders and encourages investment in future energy project development and infrastructure opportunities. This report represents the first output of the Energy Alternatives Study for the Lao PDR (Energy Alternatives Study), a collaboration between Ministry of Energy and Mines and the United States Agency for International Development (USAID) under the auspices of the Smart Infrastructure for the Mekong (SIM) program. The Energy Alternatives Study includes five tasks that build upon each other to meet the goal of the project. The report summarizes the availability, quality, and accessibility of data that serve as key inputs to energy planning activities for the power sector. The purpose of this data assessment is two-fold: 1. To facilitate the informed use of existing data by highlighting applications for these data as they relate to priority energy planning analyses; and 2. To inform future investments in energy data collection and management by identifying significant data gaps and providing guidance on how to fill these gaps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗
DOE OSTI2013

PhenoDigm: analyzing curated annotations to associate animal models with human diseases

The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene–disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene–disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly datasupported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm’s web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Control and automation of the Pegasus multi-point Thomson scattering system

A new control system for the Pegasus Thomson scattering diagnostic has recently been deployed to automate the laser operation, data collection process, and interface with the system-wide Pegasus control code. Automation has been extended to areas outside of data collection, such as manipulation of beamline cameras and remotely controlled turning mirror actuators to enable intra-shot beam alignment. Additionally, the system has been upgraded with a set of fast (∼1 ms) mechanical shutters to mitigate contamination from background light. Modification and automation of the Thomson system have improved both data quality and diagnostic reliability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework

Making reliable predictions in the presence of uncertainty is critical to high-consequence modeling and simulation activities, such as those encountered at Sandia National Laboratories. Surrogate or reduced-order models are often used to mitigate the expense of performing quality uncertainty analyses with high-fidelity, physics-based codes. However, phenomenological surrogate models do not always adhere to important physics and system properties. This project develops surrogate models that integrate physical theory with experimental data through a maximally-informative framework that accounts for the many uncertainties present in computational modeling problems. Correlations between relevant outputs are preserved through the use of multi-output or co-predictive surrogate models; known physical properties (specifically monotoncity) are also preserved; and unknown physics and phenomena are detected using a causal analysis. By endowing surrogate models with key properties of the physical system being studied, their predictive power is arguably enhanced, allowing for reliable simulations and analyses at a reduced computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

SONET: a semantic ontological network graph for managing points of interest data heterogeneity

Scalability, standardization, and management are important issues when working with very large Volunteered Geographic Information (VGI). VGI is a rich and valuable source of Points of Interest (POI) information, but its inherent heterogeneity in content, structure, and scale across sources present major challenges for interlinking data sources for analysis. To be useful at scale, the raw information needs to be transformed into a standardized schema that can be easily and reliably used by data analysts. In this work, we tackle the problem of unifying POI categories (e.g. restaurants, temple, and hotel) across multiple data sources to aid in improving land use maps and population distribution estimation as well as support data analysts wishing to fuse multiple data sources with the OpenStreetMap (OSM) mapping platform or working with projects that are already configured in the OSM schema and wish to add additional sources of information. Graph theory and its implementation through the SONET graph database, provides a programmatic way to organize, store, and retrieve standardized POI categories at multiple levels of abstraction. Additionally, it addresses category heterogeneity across data sources by standardizing and managing categories in a way that makes cross-domain analysis possible.

Palumbo, Rachel↗
DOE OSTI2015

Ultrasonic Evaluation and Imaging

Ultrasonic evaluation of materials for material characterization and flaw detection is as simple as manually moving a single-element probe across a speci-men and looking at an oscilloscope display in real time or as complex as automatically (under computer control) scanning a phased-array probe across a specimen and collecting encoded data for immediate or off-line data analyses. The reliability of the results in the second technique is greatly increased because of a higher density of measurements per scanned area and measurements that can be more precisely related to the specimen geometry. This chapter will briefly discuss applications of the collection of spatially encoded data and focus primarily on the off-line analyses in the form of data imaging. Pacific Northwest National Laboratory (PNNL) has been involved with as-sessing and advancing the reliability of inservice inspections of nuclear power plant components for over 35 years. Modern ultrasonic imaging techniques such as the synthetic aperture focusing technique (SAFT), phased-array (PA) technolo-gy and sound field mapping have undergone considerable improvements to effec-tively assess and better understand material constraints.

flaw detection↗
DOE OSTI2015

Analysis of rapidly synthesized guest-filled porous complexes with synchrotron radiation: practical guidelines for the crystalline sponge method

This report describes complete practical guidelines and insights for the crystalline sponge method, which have been derived through the first use of synchrotron radiation on these systems, and includes a procedure for faster synthesis of the sponges. These guidelines will be applicable to crystal sponge data collected at synchrotrons or in-house facilities, and will allow researchers to obtain reliable high-quality data and construct chemically and physically sensible models for guest structural determination. A detailed set of synthetic and crystallographic guidelines for the crystalline sponge method based upon the analysis of expediently synthesized crystal sponges using third-generation synchrotron radiation are reported. The procedure for the synthesis of the zinc-based metal–organic framework used in initial crystal sponge reports has been modified to yield competent crystals in 3 days instead of 2 weeks. These crystal sponges were tested on some small molecules, with two being unexpectedly difficult cases for analysis with in-house diffractometers in regard to data quality and proper space-group determination. These issues were easily resolved by the use of synchrotron radiation using data-collection times of less than an hour. One of these guests induced a single-crystal-to-single-crystal transformation to create a larger unit cell with over 500 non-H atoms in the asymmetric unit. This led to a non-trivial refinement scenario that afforded the best Flack x absolute stereochemical determination parameter to date for these systems. The structures did not require the use of PLATON/SQUEEZE or other solvent-masking programs, and are the highest-quality crystalline sponge systems reported to date where the results are strongly supported by the data. A set of guidelines for the entire crystallographic process were developed through these studies. In particular, the refinement guidelines include strategies to refine the host framework, locate guests and determine occupancies, discussion of the proper use of geometric and anisotropic displacement parameter restraints and constraints, and whether to perform solvent squeezing/masking. The single-crystal-to-single-crystal transformation process for the crystal sponges is also discussed. The presented general guidelines will be invaluable for researchers interested in using the crystalline sponge method at in-house diffraction or synchrotron facilities, will facilitate the collection and analysis of reliable high-quality data, and will allow construction of chemically and physically sensible models for guest structural determination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

A causal perspective on reliability assessment

Causality in an engineered system pertains to how a system output changes due to a controlled change or intervention on the system or system environment. Engineered systems designs reflect a causal theory regarding how a system will work, and predicting the reliability of such systems typically requires knowledge of this underlying causal structure. The aim of this work is to introduce causal modeling tools that inform reliability predictions based on biased data sources. We present a novel application of the popular structural causal modeling (SCM) framework to reliability estimation in an engineering application, illustrating how this framework can inform whether reliability is estimable and how to estimate reliability given a set of data and assumptions about the subject matter and data generating mechanism. When data are insufficient for estimation, sensitivity studies based on problem-specific knowledge can inform how much reliability estimates can change due to biases in the data and what information should be collected next to provide the most additional information. We apply the approach to a pedagogical example related to a real, but proprietary, engineering application, considering how two types of biases in data can influence a reliability calculation.

42 ENGINEERING↗