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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Limit Cycle Oscillating Detonation Wave Behavior Analysis Within a Rotating Detonation Engine

Limit cycle oscillation (LCO) detonation wave behaviors are presented and analyzed for test times exceeding 20 s in a water-cooled rotating detonation engine (RDE). LCO detonation waves exhibit cyclic acceleration and deceleration, resulting in oscillating wave spacing at unique process conditions. In previous RDE studies, similar behaviors have been studied as microsecond-scale instabilities leading to ascending or descending modal transitions. In the current work, however, LCO waves are considered a persistent wave mode, occupying unique portions of the operational envelope adjacent to those of their equally spaced counterparts. These occurrences of LCO waves are repeatable, enduring behaviors. A method to generate shifted contour surfaces specifically intended to extract and analyze wave spacing variation through time, termed limit cycle oscillation visualization (LCOV) surfaces, is presented. LCOV surfaces transform data into the reference frame of a primary traveling wave and are used to analyze quasi-steady, short-timescale, and transitional LCO modes. Results are leveraged to understand the relationship between fill height, wave strength, local wave acceleration, and subsequent LCO wave spacing for individual wave sets. In conclusion, quasi-steady LCO waves display wave spacing oscillations between equal spacing values associated with ±1 wave across runs exceeding 18 s.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Comparative Tribo-Mechanical Behavior Analysis of Laser Cladded Nitronic 60 Coating Against Wrought Nitronic 60 Alloy

In this study the tribological and mechanical behavior of a laser cladded Nitronic 60 coating on maraging steel is compared to that of a wrought Nitronic 60 alloy. A multi-layer Nitronic 60 coating was deposited using laser directed energy deposition, and its microstructure, porosity, and microhardness were characterized. The microhardness of the coated region in the laser cladded Nitronic 60 varied between 270-300 HV whereas that for wrought form was 230 HV. Controlled tribological tests under motor oil lubricated conditions were conducted at both room and elevated temperatures. The laser cladded Nitronic coating exhibited poorer wear resistance at room temperature but higher wear resistance at elevated temperature as compared to the wrought Nitronic alloy. Severe plastic deformation and fracture was observed on the laser cladded coating after the wear tests at both temperatures. Wear mechanisms were investigated based on the morphological and compositional characterization of the wear tracks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanical behavior analysis using small-size graphite disc compression testing with digital image correlation methods

The ASTM International Standard Test Method for Tensile Stress-Strain of Carbon and Graphite (ASTM C749) requires dog-bone shaped specimens. Unfortunately, specimen sizes required in ASTM C749 are much too large for irradiation within a test reactor and are incompatible with irradiation capsule volumes, molten salt degradation facilities, or oxidation apparatus. The ASTM International Standard Test Method for Tensile Strength Estimate by Disc Compression of Manufactured Graphite (ASTM D8289) was developed as an alternative that provides a means for testing tensile properties using smaller specimens when geometric constraints are present. However, the important stress–strain relation throughout the test loading history that could be recorded using ASTM C749 is lost by using ASTM D8289 because of the difficulties involved with using traditional strain gauges or extensometers on such a small specimen. In this work, a digital image correlation (DIC) method, which uses a full-field noncontact surface displacement measurement technique, was applied along with the ASTM D8289 splitting tensile test on eight small samples of graphite grade Mersen 2114 to track their deformation during compressive loading. Initial results showed that the DIC technique can measure the surface displacement/strain on these small (Ø6 × 3 mm) graphite specimens with high accuracy and good repeatability, indicating that the DIC technique, combined with the small-disc splitting tensile test in ASTM D8289, can capture the strain evolution in the test. In conclusion, further comparison of measured strains with analytical and numerical simulation results provides additional understanding of the mechanical behaviour of this nuclear graphite material in complex stress states.

36 MATERIALS SCIENCE↗
DOE OSTI2020

BISON Capabilities for LWR Fuel Behavior Analysis During Accident and High-burnup Conditions

The U.S. Department of Energy (DOE)'s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON which is a high-fidelity and high-resolution fuel performance tool at the engineering scale. Increasing recent interest in applications at extended burnups motivated this study to incorporate more physically based models in BISON. This document details integration of newly implemented modeling capabilities into BISON, which includes (1) new thermal conductivity models that are valid up to 100 GWd/t, (2) models for the formation of the high-burnup structure (HBS), (3) two porosity correction methods beingapplied on the thermal conductivity due to the conducting pores during the HBS formation. BISON's results are verified and validated to test the new modeling capabilities

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Think before you throw! An analysis of behavioral interventions targeting PET bottle recycling in the United States

The United States generates 42 Mt of plastic waste each year and is one of the biggest contributors to ocean plastic waste. Consequently, plastic has become synonymous with the linear economy, and many scholars are studying and proposing circular economy solutions to mitigate plastic pollution. Recycling has received much attention from both social sciences and engineering as a circular economy strategy, but no study has yet quantified how behavioral interventions could asymmetrically affect different populations. Here, this study combines agent-based modeling, material flow analysis, system dynamics, and life cycle assessment to assess the effect of four behavioral interventions on the collection rates of polyethylene terephthalate bottle waste, displaced virgin plastic manufacturing, and avoided greenhouse gas (GHG) emissions. Results show that, while behavioral interventions would require about 300–900 GJ of additional energy at end-of-life due to improved collection rates, they would avoid about 500–700 thousand metric tons of GHG emissions. Results also illustrate the importance of habits in disposal behaviors and show that different forms of interventions can be better adapted to particular social contexts than others. While the circular economy and its application to plastic waste should certainly not be restricted to recycling, this study demonstrates that improved collection rates and recycling technologies can contribute to reducing the amount of plastic waste polluting our oceans.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

New Geomechanical Computation Analysis Evaluating Behavior of the Fault nearby BC-4

BC-4 is an abandoned brining cavern situated in the middle of the site. Its presence poses a concern for several reasons: 1) the cavern was leached up into the caprock; 2) it is similar to BC-7, a brining cavern on the northwest corner of the dome that collapsed in 1954 and now is the home to Cavern Lake; 3) a similar collapse of BC-4 would have catastrophic consequences for the future operation of the site. There exists a previously mapped fault feature in the caprock and thought to extend into the salt dome than runs in close proximity to BC-4. There are uncertainties about the true extent of the fault, and no explicit analysis has been performed to predict the effects of the fault on BC-4 stability. Additional knowledge of the fault and its effects is becoming more crucial as an enhanced monitoring program is developed and installed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

Engineering↗
DOE OSTI2022

General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior, without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. Non-negative tensor factorization, on the other hand, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Herein, our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Metallic Fuel Performance Analysis for the European Sodium Fast Reactor (ESFR-SIMPLE): Analysis of metallic fuel performance using SAS4A/SASSYS-1 $-$ MFUEL

The European Sodium Fast Reactor - Safety by Innovative Monitoring, Power Level flexibility and Experimental research (ESFR-SIMPLE) project was initiated in 2022 and includes assessment of a metallic-fueled version of the ESFR concept. Argonne National Laboratory (ANL) has been partnering with the ESFR-SIMPLE project to share its expertise on metallic fueled SFR designs and support some of its analysis. This report focuses on metallic fuel behavior analysis for ESFR-SIMPLE design conditions under base irradiation and transients (ULOF and UTOP).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2023

ASME Irradiation Model

ASME Irradiation Model: How to deal with irradiation data in ASME code rules. Includes discussion on challenges to nuclear graphite, such as lack of sufficient irradiation data to qualify graphite for nuclear application for all grades and temperatures, variety of grades, and time/room in available MTRs to get all the required data. Discussion of Irradiation behavior, Leveraging data generated by GIF countries the past 20 years, Dimensional Change Theory, behavior, analysis, turnaround, and data. Future discussions include Arrhenius approach to predict turnaround behavior for the ASME MDS requirements for all nuclear graphite.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

On the Onset of Plasticity: Determination of Strength and Ductility

The analysis of the work hardening variation with stress reveals insight to operative stress-strain mechanisms in material systems. The onset of plasticity can be assessed and related to ensuing plastic deformation up to the structural instability using one constitutive relationship that incorporates both behaviors of rapid work hardening (Stage 3) and the asymptotic leveling of stress (Stage 4). Results are presented for the mechanical behavior analysis of Ti-6Al-4V wherein the work hardening variation of Stages 3 and 4 are found to: be dependent through a constitutive relationship; be useful in a Hall-Petch formulation of yield strength; and provide the basis for a two point-slope fit method to model the experimental work hardening and stress-strain behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physical properties of the layered $\mathcal{f}$-electron van der Waals magnet Ce 2 Te 5

Here. we report a detailed study of the magnetic, transport, and thermodynamic properties of Ce 2 Te 5 single crystals, a layered $\mathcal{f}$-electron van der Waals magnet. Four consecutive transitions at ~5.2, 2.1, 0.9, and 0.4 K were observed in the ac-plane electrical resistivity $\rho(T)$, which were further confirmed in specific heat $C_\text{p}(T)$ measurements. Analysis of the magnetic susceptibility $\chi(T)$, the magnetic-field variation of $\rho(T)$, and the increase of the first transition temperature ($T_c$ ~ 5.2 K) with applied magnetic field indicates ferromagnetic order, while the decrease of the other transitions with field suggests different states with dominant antiferromagnetic interactions below $T_2$ ~ 2.1 K, $T_3$ ~ 0.9 K, and $T_4$ = 0.4 K. Critical behavior analysis around Tc that gives critical exponents $\beta$ = 0.31(2), $\gamma$ = 0.99(2), $\delta$ = 4.46(1), and $T_c$ = 5.32(1) K indicates that Ce 2 Te 5 shows a three-dimensional magnetic critical behavior. Moreover, the Hall resistivity ρxy indicates that Ce2Te5 is a multiband system with a relatively high electron mobility ~2900 cm 2 V –1 s –1 near $T_c$, providing further opportunities for future device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Feeding state-dependent neuropeptidergic modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila

Abstract An animal’s feeding state changes its behavioral priorities and thus influences even nonfeeding-related decisions. How the feeding state information is transmitted to nonfeeding-related circuits and what circuit mechanisms are involved in biasing nonfeeding-related decisions remain open questions. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by changes in the feeding state. We found that this effect is achieved by the differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and, less frequently, a protective type of response if larvae are fed sugar than when they are fed a balanced diet. Information about the internal state is conveyed to inhibitory neurons through homologs of the vertebrate neuropeptide Y, which is known to be involved in regulating feeding behavior.

Science & Technology - Other Topics↗
DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor

The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗