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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Criticality Safety: Early Accidents at Los Alamos [Slides]

The results of an experiment can never be considered a failure. Analyses of the results often suggest modifications to theories and procedures to improve the understanding of the underlying principles. In a similar way, observation and analyses of accidents suggest modifications to procedures and behavior to minimize repetition. The lecture covers early accidents at Los Alamos National Laboratory.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

q-nonabelianization for line defects

We consider the q-nonabelianization map, which maps links L in a 3-manifold M to combinations of links $\tilde{L}$ in a branched N -fold cover $\tilde{M}$. In quantum field theory terms, q-nonabelianization is the UV-IR map relating two different sorts of defect: in the UV we have the six-dimensional (2, 0) superconformal field theory of type gl(N ) on M × R 2,1 , and we consider surface defects placed on L × {x 4 = x 5 = 0}; in the IR we have the (2, 0) theory of type gl (1) on $\tilde{M}$ × R 2,1 , and put the defects on $\tilde{L}$ × {x 4 = x 5 = 0}. In the case M = R 3 , q-nonabelianization computes the Jones polynomial of a link, or its analogue associated to the group U(N ). In the case M = C × R, when the projection of L to C is a simple non-contractible loop, q-nonabelianization computes the protected spin character for framed BPS states in 4d N = 2 theories of class S. In the case N = 2 and M = C × R, we give a concrete construction of the q-nonabelianization map. The construction uses the data of the WKB foliations associated to a holomorphic covering $\tilde{C}$ →C.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Enhanced Hanford Low-Activity Waste Glass Property Data Development (Phase 3)

This work was performed for the U.S. Department of Energy (DOE) Office of River Protection (ORP) to provide expert evaluation and experimental work in support of the River Protection Project vitrification technology development1. The long-term objective of this work is to expand the property-composition database for Hanford site low-activity waste (LAW) glasses and property-composition models to cover the balance of the mission for the Hanford Waste Treatment and Immobilization Plant (WTP). When this effort is complete, enhanced LAW glass property-composition models will be developed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Inhibition of Micro-pitting by Tribofilm-Forming ZrO 2 Nanocrystal Lubricant Additives: A Micro-pitting Rig and Transmission Electron Microscope Study

The drive to reduce fuel consumption in transportation has encouraged the emergence of low viscosity lubricants to reduce viscous losses in the engine, drivetrain, and other components. However, viscosity reduction increases the risk of surface damage, thus motivating the development of new anti-wear (AW) additives. Capped ZrO 2 nanocrystals (NCs) in base oils have been shown to form AW tribofilms within microscale sliding contacts. However, the potential of ZrO 2 NCs to protect surfaces subjected to rolling-sliding contact from macroscale damage, such as micro-pitting, remains unexplored. Here, we explore the ability of ZrO 2 NCs to form protective tribofilms under harsh conditions using a micro-pitting rig (MPR), consisting of a three ring-on-roller configuration. The experiments were conducted in polyalphaolephin (PAO) base oil, with and without 5 nm diameter ZrO 2 NCs, at two levels of slide-to-roll ratio (SRR) (30% and 0%) and at variable test durations up to long durations (119 h). MPR results showed the use of ZrO 2 NCs gives rise to the formation of a tribofilm covering the roller surfaces and decreases the initiation and propagation of micro-pits compared to tests in pure PAO base stock. Transmission electron microscopy (TEM) performed on focused ion beam (FIB) milled cross-sectional samples of the roller surfaces revealed the growth of dense and 50–100 nm thick ZrO 2 -based tribofilms independent of (SRR), indicating the potential for robust micro-pitting fatigue protection. Nevertheless, small cracks localized within the near surface region of the roller tested at the most severe conditions (30% SRR and 119 h) were observed. The initiation of these cracks was directly related to the presence of manganese sulphide (MnS) inclusions in the steel, revealed using TEM combined with energy dispersive spectroscopy (EDS). Furthermore, the results highlight the benefits of the protective tribofilms formed by ZrO 2 NCs and suggest approaches for further optimizing their use.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Wildfire‐Induced Losses of Soil Particulate and Mineral‐Associated Organic Carbon Persist for Over 4 Years in a Chaparral Ecosystem

ABSTRACT Wildfires can lower soil carbon (C) stocks directly through combustion, but also indirectly during post‐fire recovery if microbial C demands outpace photosynthetic C inputs. However, how much C is respired by soil microorganisms post‐fire may depend on wildfire effects on particulate organic carbon (POC; mostly plant material accessible to microbes) and/or mineral‐associated organic carbon (MAOC; considered C protected by minerals from decomposers), meaning assessment of wildfire impacts on these pools is necessary to predict microbial decomposition rates and, thus, the fate of soil C. Here, we measured POC, MAOC, pyrogenic organic matter C, plant cover, extracellular enzyme activity (EEA), and microbial community abundance and composition 17 days, and 1, 3, and 4 years after the Holy Fire burned 94 km 2 of fire‐adapted chaparral. The wildfire immediately decreased POC by 50% (from 51 ± 21 to 26 ± 6 g C kg −1 ) and MAOC by 33% (from 9.3 ± 0.9 to 6.3 ± 0.9 g C kg −1 ), consistent with MAOC being less vulnerable to loss than POC. POC decreased by another 38% 1 year post‐fire, consistent with increases in microbial abundance and EEA suggesting increased microbial decomposition. Between 1 and 4 years after the fire, cover of the dominant shrub (Arctostaphylos glandulosa) increased from 3.9% ± 1.6% to 16% ± 5.4% (compared to 58% ± 4.6% in unburned plots), marking the end of net soil C losses. Still, soil C did not increase between 1 and 4 years post‐fire, suggesting plant C inputs did not outpace microbial respiration, a finding consistent with isotopically heavier C from microorganisms raising bulk soil δ 13 C values. As global changes favor increases in wildfire frequency and severity, C losses via combustion and decomposition may outpace plant C inputs during the first 4 years post‐fire in chaparral, slowing the replenishment of soil C stocks.

Biodiversity & Conservation↗
DOE OSTI2020

2019 LANL Radionuclide Air Emissions Report

In June 2020, Los Alamos National Laboratory (LANL) submitted letter ESHQSS: 20-025 to Region 6 of the Unites States Environmental Protection Agency (EPA). This letter transmitted the 2019 LANL Radionuclide Air Emissions Report to the EPA. This report summarizes LANL operations, air emissions of radionuclides, and subsequent off-site dose consequence from these emissions for calendar year 2019. This report is issued to meet requirements of 40 CFR 61 Subpart H, National Emission Standards for Emissions of Radionuclides other than Radon from Department of Energy Facilities. This regulation, called the Radionuclide NESHAP, establishes a limit of 10 millirem per year to the maximally exposed member of the public and requires an annual report to be submitted to the appropriate EPA Region by June 30th of each year. LANL’s off-site dose from airborne emissions of radionuclides in 2019 was 0.43 millirem. The normal practice is to issue this report directly to EPA Region 6 in June of each year, and then after the report is accepted by the EPA, re-issue the report for inclusion in the LANL Electronic Public Reading Room (EPRR). This second part is usually done in July of each year, but this step was overlooked in July 2020 due to other commitments. We regret this error. This omission was noted in late 2020; we are submitting the 2019 LANL Radionuclide Air Emissions Report to the EPRR now in December 2020. Aside from the addition of this cover sheet explaining the delay, the report is unchanged from the version submitted to EPA Region 6 in June.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recent Advances in Synthesis and Application of Lignin Nanoparticles

The ability to engineer material at its nanoscale has shown great potentials in overcoming significant obstacles in numerous fields. Because of its abundance, biodegradability, renewability, and eco-friendliness nature, lignin has become an ideal precursor for producing nanoscale materials from both research and industrial communities. Nanolignin, also known as colloidal lignin particle (CLP) or lignin nanoparticle (LNP), has received significant interest in recent years and its production and application in numerous fields represent an important aspect of lignin valorization. LNPs could be produced via several techniques including sonication, solvent exchange, and precipitation, which could be divided into wet and dry methods. Applications of LNPs in UV protection, cosmetics, food package, coatings, and drug delivery have been reported in the literature. Despite being extensively studied, LNPs as well as their derived materials with controlled shape and architecture are still largely underexploited. This book chapter mainly covers recent advances in the preparation of LNPs and their use in the novel and advanced applications in different areas.

Meng, Xianzhi↗
DOE OSTI2023

Leveraging High-Fidelity Datasets for Machine Learning-based Anomaly Detection in Smart Grids

Data-driven intrusion detection systems are increasingly becoming essential for protecting critical cyber-physical infrastructure, such as the power grid, against the growing number of sophisticated cyber-attacks. The development of such tools is reliant on the availability of high-fidelity cyber-physical datasets that cover a diverse variety of potential cyber events. In this work, a high-fidelity smart grid platform is utilized to develop an extensive dataset, which is used to train and test a machine learning-based intrusion detection system. The evaluation of the developed IDS shows robust performance even when tested with statistically diverse test data not used in training.

Hyder, Burhan↗
DOE OSTI2022

Ligand removal energetics control CO 2 electroreduction selectivity on atomically precise, ligated alloy nanoclusters

Atomically precise, thiolate-protected gold nanoclusters (TPNCs) exhibit remarkable catalytic performance for the electrochemical reduction of carbon dioxide (CO 2 R) to CO. The origin of their high CO 2 R activity and selectivity has been attributed to partial ligand removal from the thiolate-covered surfaces of TPNCs to expose catalytically active sulfur atoms. Recently, heterometal doped (alloy) TPNCs have been shown to exhibit enhanced CO 2 R activity and selectivity compared to their monometallic counterparts. However, systematic studies on the effect of doping (metal type and location on TPNC) on active site exposure and CO 2 R activity are missing in literature. Herein, we apply Density Functional Theory calculations to investigate the effect of heterometal (Pt, Pd, Hg and Cd) doping of Au 25 (SR) 18 TPNC on the active site exposure and CO 2 R activity and selectivity. In this study, we reveal that doping significantly modifies relevant TPNC electronic properties, such as electron affinity, while also altering partial ligand removal and carboxyl (*COOH) intermediate formation energies. Furthermore, we demonstrate that changing the dopant (e.g. Hg) position can change the selectivity of the TPNC towards CO (g) or H 2(g) formation, highlighting the importance of dopant locations in TPNC-based CO 2 R. Most notably, we report a universal (i.e. capturing different dopant types and positions) linear trend between the ligand removal energy and i) the *COOH formation energy, as well as, ii) the hydrogen (*H) formation energy on the different alloy TPNCs. Thus, utilizing the ligand removal energy as a descriptor for CO 2 RR activity and selectivity, our work opens new avenues for accelerated computational screening of different alloy TPNCs for electrocatalytic CO 2 R applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

GDF15: a potential therapeutic target for type 1 diabetes

Type 1 Diabetes (T1D) is a pancreatic disease, which is marked by immune-mediated ? cell destruction causing hyperglycemic phenotype. Recently, our team has demonstrated the beneficial role of GDF15 in preventing T1D-associated insulitis. Therefore, in this review, we discuss the therapeutic potential of GDF15 in treating T1D and map the promising avenues demanding increased focus in this field. Areas covered: Several therapeutic options exist for management of T1D, however, the demand for new and efficient therapies persists, particularly for interventions that may prevent or delay disease onset. Growth differentiation factor 15 (GDF15) is a known diabetes biomarker which has shown impressive tissue-protective actions across a broad range of metabolic disorders. There is a tremendous amount of overlap between the two topics (GDF15 & T1D), however, the connections were uncharted. We discuss all these connections and the possibility of exploiting them to make GDF15 a T1D therapy. Expert Opinion: The newly discovered GDF15 primary receptor and associated signaling pathway has accelerated the excitement by making it a druggable target. However, the there exists a huge gap in knowledge regarding the mechanism by which GDF15 functions in the peripheral tissues. In this context, reports of GDF15 receptor in pancreas, gives a convincing argument of direct GDF15 effect. Further, GDF15’s role in regulating mitochondrial, ER and metabolic stress, makes it an ideal T1D candidate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

EVSE Characterization

NextGen Profiles' EVSE characterization efforts explored performance variability in production EVSE through the use of EV emulation equipment and assessed how different operational conditions influence charging behavior. Data were collected at a frequency of 10 Hz from both the EV emulator and EVSE during each charge session and stored in a time-series database for further analysis. As part of the NextGen Profiles project, characterization of high-power EVSE was performed on both conductive and wireless charging infrastructure; however, only conductive charging data are currently included in this repository. This EVSE characterization was performed over a range of DC output currents and voltages, covering both nominal and off-nominal test conditions. This EVSE characterization dataset includes high-power charging data from two types of 350-kW-capable EVSE using liquid-cooled Combined Charging System-1 (CCS1, North American version) cables and connectors. To protect confidentiality, all EVSE metadata are anonymized, and the publicly released datasets are metered at 10-Hz frequency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A co-opted steroid synthesis gene, maintained in sorghum but not maize, is associated with a divergence in leaf wax chemistry

Significance Virtually all above-ground plant surfaces, such as leaf and stem exteriors, are covered in a cuticle: a wax-infused polyester. This waxy biocomposite is the largest interface between Earth’s biosphere and atmosphere. Its chemical composition is not only highly tuned to mediate nonstomatal water loss, but it also self-assembles to produce superhydrophobic surfaces, protects against UV radiation, and contains bioactive compounds that help resist microbial attack. Developing fundamental knowledge of waxy biocomposites, particularly those on crop species, is a prerequisite for an understanding of their structure–function relationships. Here, we uncover a likely genetic basis for the presence and absence, respectively, of triterpenoids in the leaf waxes of sorghum and maize—compounds previously associated with creating heat-tolerant cuticular water barriers.

09 BIOMASS FUELS↗
DOE OSTI2023

RCT Continuing Training: RCT Job Coverage and CONOPS

RCT Continuing Training covering all of the necessary steps associated with RCT, and how conduct of operations ties in with this process. The terminal objective of this training is: Given the need to perform radiological job coverage, recognize the requirements to maintain radiological work control in accordance with P121, Radiation Protection, RP-PROG-TP-200, Radiation Protection Manual, and P315, Conduct of Operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2025

Total Effective Dose from Radiologic Emissions from INL Facilities for Calculation of Population Dose for the INL 2024 Annual Site Environmental Report

Total effective radiation dose from airborne releases was calculated using air dispersion modeling performed by the National Oceanic and Atmospheric Administration (NOAA) Idaho Falls Office using their HYSPLIT computer model (Stein et al. 2015; Draxler et al. 2013), and the Dose Multi-Media (DOSEMM) dose assessment model (Rood 2019) . The objective of these calculations was to provide a grid of total effective dose across a model domain that encompasses a 50-mile (80-km) radius from any Idaho National Laboratory (INL) Site source. In addition to INL Site sources, releases from the Radiological and Environmental Sciences Laboratory (RESL) (IF-683) and IF-603 located at the INL Research Center (IRC) within the Idaho Falls city limits were also included. Due to tracking limitations, radionuclides released from IF-611 and IF-603 are modeled as released from IF-603. The dose results will be combined with GIS software to compute a total population dose for the calendar year (CY) 2024 and will be reported in the INL Annual Site Environmental Report (ASER). This report does not cover the population dose calculation and only documents generation of the gridded dose file.

42 - ENGINEERING↗
DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Temperature-Time Modeling of Spent Fuel Cladding in Dry Storage Casks

This report uses thermal and decay heat modeling to investigate spent fuel performance and the potential for cladding to anneal in dry storage conditions. Annealing is an important feature to investigate in SNF cladding because it has a direct relationship to cladding response in storage, transportation and disposal conditions. Annealed cladding may have lower strength than unannealed cladding, however its increased ductility would provide better protection against rupture in high strain rate situations such as severe accidents. These consequences are the driver for this work however are outside of the scope of this report. The modeling focused on three representative storage systems, the TN-32B, MAGNASTOR with TSC-37 canister, and NUHOMS AHSM with a 32PTH2 canister. This covers the vertical dual-purpose, vertical ventilated and horizontal ventilated casks respectively. Decay heat modeling using high and low enrichment assemblies was used to bound the decay heat curves that might be expected in dry storage. To bound the temperature relationship, the storage casks were modeled starting at the design basis heat loads with heat decaying through time. Although the results are bounding there is not an attempt to maximize conservatism, rather the intent to form a reasonable upper limit on temperature that will be broadly applicable to the U.S. cask fleet. This will allow materials testing to focus on relevant conditions for annealing that may affect the U.S. spent fuel inventory. The results show a clear dependence on heat load pattern in time in Figure S-1 and Figure S-2. This dependence is due to the different assembly decay heat curves for different assemblies in preferentially loaded casks. It shows the need for careful decay heat modeling when examining in service fuel temperatures that are less than the cask design basis heat load. The body of the report shows percent cladding cutoffs of 300°C and 350 °C as well. These results can be used to inform testing and conclusions about cladding performance through time and the potential for cladding annealing during dry storage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗