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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Experimental Analysis on the CNS Simulator Comparing Human Performance between Operators and Students

This study, as a previous study of the SHEEP project, aims to identify differences in human performance data through experiments with students and operators using CNS (Compact Nuclear Simulator). To achieve this goal, this experiment seeks to analyze the correlation of measured human performance between students and operators through a randomized factorial experiment design. Five human performances are measured. In addition, several scenarios and related procedures have been developed for the CNS experiments.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A Comparison of Human Error Probabilities Collected from HuREX and SHEEP Frameworks

This paper discusses how different are the HEPs collected from HuREX and SHEEP frameworks. This study is a preceding research to infer full-scope HRA data based on the data collected from the SHEEP framework. For the comparison, we used HEPs in the HuREX database published in KAERI-TR-6649 [6]. The HEPs have been collected from actual licensed operators when manipulating MCR simulators of Westinghouse type and Optimized Power Reactor (OPR1000) type in South Korea. For the HEPs based on the SHEEP framework, these have been collected from actual licensed operators and students when using a simplified simulator, i.e., Rancor Microworld.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

An Experimental Investigation of Students? Learning Effects When Using a Simplified Nuclear Simulator

This study focuses on investigating students' learning effects and performance trends over a certain period using the Rancor Microworld Simulator. Specifically, it aims to determine the training required to collect HRA data from non-experts (i.e., students) using Rancor Microworld and the differences in human performance measures between students and professional operators. A longitudinal experiment is conducted with sixteen undergraduate students, using four Rancor Microworld scenarios in each of the four experiment trials. The study considers four human performance measurements workload, situation awareness, time, and error. Finally, the trend of students' performance is compared with operator data collected from the previous experiment. Overall, this research complements previous studies by providing insights into how much training is required to collect HRA data from non-experts and the differences in human performance measures between students and professional operators.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

NREL's Advanced Distribution Management System (ADMS) Test Bed

NREL's advanced distribution management system (ADMS) research helps utilities meet customer expectations of reliability, power quality, renewable energy use, data security, and resilience to natural disasters and other threats.

Advanced Research on Integrated Energy Systems↗
DOE OSTI2025

I Can't Patch My OT Systems! A Look at CISA's KEVC Workarounds & Mitigations for OT

We examine the state of publicly available information about known exploitable vulnerabilities applicable to operational technology (OT) environments. Specifically, we analyze the Known Exploitable Vulnerabilities Catalog (KEVC) maintained by the US Department of Homeland Security Cybersecurity and Infrastructure Security Agency (CISA) to assess whether currently available data is sufficient for effective and reliable remediation in OT settings. Our team analyzed all KEVC entries through July 2025 to determine the extent to which OT environments can rely on existing remediation recommendations. We found that although most entries in the KEVC could affect OT environments, only 13% include vendor workarounds or mitigations as alternatives to patching. This paper also examines the feasibility of developing such alternatives based on vulnerability and exploit characteristics, and we present early evidence of success with this approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Best practices: Organizational execution

This article, the fourth and final installment in the DOE–IDEA series, focuses on organizational execution and how strong management practices, teamwork, and preparedness contribute to successful district energy systems. It highlights case studies from Ashley Energy and Cornell University to illustrate effective operational strategies. Ashley Energy demonstrates the importance of emergency preparedness and rapid response. After a major flood disrupted its plant, the organization restored service in under 72 hours by relying on pre-established plans, vendor relationships, and trained staff. The case emphasizes proactive contingency planning, understanding insurance processes, and empowering skilled personnel to improvise during crises. Cornell University’s example highlights the role of collaboration and transparency in long-term success. Its district energy system benefits from strong data sharing, real-time energy monitoring, and active involvement of faculty and students in system planning and innovation. This culture of teamwork and data-driven decision-making supports sustainability goals and continuous system improvement. Overall, the article shows that effective organizational execution—through preparedness, collaboration, and data transparency—is essential for maintaining reliable, efficient, and sustainable district energy systems.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Data-driven global ocean modeling for seasonal to decadal prediction

Accurate modeling of ocean dynamics is crucial for enhancing our understanding of complex ocean circulation processes, predicting climate variability, and tackling challenges posed by climate change. Although great efforts have been made to improve traditional numerical models, predicting global ocean variability over multiyear scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), a data-driven three-dimensional ocean model for seasonal to decadal prediction of global ocean dynamics. ORCA-DL accurately simulates the three-dimensional structure of global ocean dynamics with high physical consistency and outperforms state-of-the-art numerical models in capturing extreme events, including El Niño–Southern Oscillation and upper ocean heat waves. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections. Our results demonstrate the high potential of data-driven models for providing efficient and accurate global ocean modeling and prediction.

Science & Technology - Other Topics↗
DOE OSTI2023

Electrical Energy Storage Data Submission Guidelines, Version 3

The knowledge of long-term health and reliability of energy storage systems is still unknown, yet these systems are proliferating and are expected increasingly to assist in the maintenance of grid reliability. Understanding long-term reliability and performance characteristics to the degree of knowledge similar to that of traditional utility assets requires operational data. This guideline is intended to inform numerous stakeholders on what data are needed for given functions, how to prescribe access to those data and the considerations impacting data architecture design, as well as provide these stakeholders insight into the data and data systems necessary to ensure storage can meet growing expectations in a safe and cost-efficient manner. Understanding data needs, the systems required, relevant standards, and user needs early in a project conception aids greatly in ensuring that a project ultimately performs to expectations.

25 ENERGY STORAGE↗
DOE OSTI2019

Interatomic Potential Model Development: Finite-Temperature Dynamics Machine Learning

Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. Here, for developing an unbiased potential, a finite-temperature dynamics machine learning (FTD-ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD-ML exhibits three distinguished features: 1) FTD-ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD-ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first-principles data; 3) FTD-ML is much more computationally cost effective than first-principles simulations, especially when the system size increases over 10 3 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD-ML approach exhibits good performance for general simulation purposes. Thus, the FTD-ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental-level accuracy.

42 ENGINEERING↗
DOE OSTI2024

Application of a Prize Mechanism to Address Data Utilization Challenges at Utilities

The electric industry sector is facing an “explosion” of data from a variety of sources. Electric sector stakeholders need to define how to capitalize on large datasets, both those they create and those from other sources (like data on weather, buildings, electric vehicles, etc.), to improve reliability and resilience and meet the changing system dynamics from renewable integration. For the electricity sector to fully utilize these vast new datasets, it must undergo a transformation in how it manages data quality, storage, and processing. The U.S. Department of Energy (DOE) Office of Electricity (OE) is committed to accelerating research, development, and demonstration of new technologies and tools within the electricity sector to advance reliability, resilience, and affordable operation of the power system. Through the prize mechanism, OE identified two widespread data-related challenges for utilities—load modeling and data analysis automation—and offered an opportunity for utilities and teams of software engineers to identify additional challenges faced by utilities. After completing one round of the American-Made Digitizing Utilities Prize, OE, the National Renewable Energy Laboratory (NREL) as the prize administrator, and Pacific Northwest National Laboratory (PNNL) as the domain experts have compiled the results and lessons learned to feed into the second round of the prize.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗
DOE OSTI2022

Statistical analysis of S—N type environmental fatigue data of Ni-base alloy welds using weibull distribution

In this study, the probabilistic fatigue life model for Ni-base alloys was developed based on the Weibull distribution using statistical analysis of fatigue data reported in NUREG/CR-6909 and the new fatigue data of Alloy 52M/152 and 82/182. The developed Weibull model can consider right-censored data (i.e., non-failed data) and quantify the improved safety (or reliability) based on the level of failure probability. The overall margin in the current fatigue design limit model (ASME design curve + NUREG/CR-6909 F en model) is similar to that of the Weibull model with a cumulative failure probability of approximately 2.5%. The margin in the current fatigue design limit model demonstrated inconsistencies for the Ni-base alloy weld data, whereas the Weibull model showed a consistent margin. Therefore, the Weibull model can systematically mitigate the excessive safety margin.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On the inference of viscoelastic constants from stress relaxation experiments

Several constitutive theories have been proposed in the literature to model the viscoelastic response of materials, including widely used rheological constitutive models. These models are characterized by certain parameters ("time constants") that define the time scales over which the material relaxes. These parameters are primarily obtained from stress relaxation experiments using curve-fitting techniques. However, the question of how best to estimate these time constants remains open. As a step towards answering this question, we propose an optimal experimental design approach based on ideas from information geometry, namely Fisher information and Kullback-Leibler divergence. The material is modeled as a spring element in parallel with multiple Maxwell elements and described using a one- or two-term Prony series. Treating the time constants as unknowns, we develop expressions for the Fisher information and Kullback-Leibler divergence that allow us to maximize information gain from experimental data. Based on the results of this study, we propose that the largest time constant estimated from a stress relaxation experiment for a linear viscoelastic material should be at most one-fifth of the total time of the experiment in order to maximize information gain. Our results also provide confirmation that the equilibrium modulus of the material cannot be reliably determined from curve-fitting to data from a stress relaxation experiment.

Fisher information↗