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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Irradiation Testing Plan for Long Term Accident Tolerant Fuels

Many potential Accident Tolerant Fuel (ATF) technologies with the potential for making substantive improvements to the performance of Light Water Reactors (LWRs) in accident scenarios will require development and qualification timelines longer than 4 years. These technologies are named “Long Term” ATF. The attached report summarizes the irradiation testing capabilities available for use in the development and qualification of these technologies and presents a nominal test sequence.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Effects of White Sturgeon (Acipenser transmontanus) Passage Through a Novel Hydropower Turbine and Implications for Long-Term Survival

Researchers from UC Davis and Natel Energy compared a novel "Restoration Hydropower Turbine" against a conventional turbine design to evaluate their physical and physiological effects on juvenile White Sturgeon. The study found that the new design achieved over 99% survival across all test speeds, whereas the conventional turbine caused significant mortality and injury due to blade strikes. Furthermore, long-term monitoring showed that fish passing through the new turbine experienced no meaningful or persistant negative impacts on their growth, behavior, or stress levels.

13 HYDRO ENERGY↗
DOE OSTI2026

Performance and long-term stability of the ICARUS-T600 scintillation light detection system

The ICARUS-T600 Liquid Argon (LAr) Time Projection Chamber (TPC) is presently used as a far detector of the Short Baseline Neutrino (SBN) program at Fermilab (USA) to search for a possible LSND-like sterile neutrino signal at $\Delta m^2 \sim 1 eV^2$ with the Booster Neutrino Beam (BNB). A light detection system, based on 360 large area Photo-Multiplier Tubes (PMTs), has been realized for ICARUS-T600 to detect VUV photons produced after the passage of ionizing particles in LAr. This system is fundamental for the TPC operation, providing an efficient trigger and contributing to the 3D reconstruction of events. Moreover, since the detector is exposed to a huge flux of cosmic rays due to its shallow depths operations, the light detection system allows for the time reconstruction of events, contributing to the identification and to the selection of neutrino interactions within the BNB spill gate. Long-term behavior of PMT gains and timing resolution, demonstrate the robustness and reliability of the ICARUS scintillation light detection system and provide valuable input for the design and operation of future large-scale liquid argon detectors.

Raselli, Gian Luca [INFN, Perugia] (ORCID:00000002↗
DOE OSTI2023

Nucleon form factors and the pion-nucleon sigma term

This talk summarizes the progress made since Lattice 2021 in understanding and controlling the contributions of towers of multihadron excited states with mass gaps starting lower than of radial excitations, and in increasing our confidence in the extraction of ground state nucleon matrix elements. The most clear evidence for multihadron excited state contributions (ESC) is in axial/pseudoscalar form factors that are required to satisfy the PCAC relation between them. The talk examines the broader question--which and how many of the theoretically allowed positive parity states N(p)π(−p), N(0)π(0)π(0), N(p)π(0), N(0)π(p), … make significant contributions to a given matrix element? New data for the axial, electric and magnetic form factors are presented. They continue to show trends observed in Ref.[1]. The N2LO χPT analysis of the ESC to the pion-nucleon sigma term, σπN, has been extended to include the Δ as an explicit degree of freedom [2]. The conclusion reached in Ref.[3] that Nπ and Nππ states each contribute about 10 MeV to σπN, and the consistency between the lattice result with Nπ state included and the phenomenological estimate is not changed with this improvement.

Gupta, Rajan↗
DOE OSTI2025

Agricultural crop and residue production by county - near-term scenario

This dataset contains data on agricultural crop and residue production by county in 2030. The agricultural crops in this dataset include barley, corn, cotton, grain sorghum, hay, oats, rice, soybeans, and wheat. The agricultural residues include barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al.,2024) for the near-term scenario with biomass market prices of up to $70 per dry ton.

agricultural crop↗
DOE OSTI2023

SPRUCE Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog: Supporting data

This data set contains measurements of anaerobic oxidation of methane (AOM), net methane (CH4) production, carbon dioxide (CO2) production, and gross CH4 production measurements from the SPRUCE experiment (Hanson et al. 2017). CH4 is greenhouse gas with 45 times the sustained global warming potential of CO2 over 100 years, which may have significant implications on global climate change. The process of AOM plays a large role in the extent of CH4 emissions from wetland environments like those at the SPRUCE experiment site. This study conducted two on different dates. In experiment 1, net CH4 and CO2 production were measured with a gas chromatograph through the peat profile at in situ temperatures of the SPRUCE plots four times from May to October, 2016 (2016-05 to 2016-10). Two treatments were examined, a Porewater Treatment that was a 1:1 volumetric peat:porewater slurry and a No Porewater Treatment that was field-moist peat with no porewater added. Additionally, net CH4 production were measured at frequent intervals (from 4 hr to 9 d) in peat from selected SPRUCE plots from soil collected on October 16, 2016 (2016-10-16). Samples were collected from the SPRUCE plots, immediately made anaerobic, and transported to the University of Oregon for rate measurements within several days of collection. In experiment 2, AOM, net CH4 production, CO2 production, and gross CH4 production were measured through the peat profile at in situ field temperatures once from samples collected in July, 2021 (2021-07). The same two porewater treatments were examined. AOM was measured with a 13CH4 tracer method using a Picarro G2201-i analyzer for isotopic CO2/CH4 quantification with a small stable isotope module attached. This data set contains three data files in comma separate (*.csv) format. This data set is associated with the following publication: Barney, M, AM Hopple, LL Gregory, JK Keller, and SD Bridgham. 2024. Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog. Soil Biology and Biochemistry, 190, 109316. https://doi.org/10.1016/j.soilbio.2024.109316.

SPRUCE Experiment, Marcell Experimental Forest, Me↗
DOE OSTI2020

Ecogenomics reveals community interactions in a long-term methanogenic bioreactor and a rapid switch to sulfate-reducing conditions

This Narrative is part of a collection used in a single publication. The paper took a metagnomic and metatranscriptomic approach to examine a bioreactor's response to the presence of sulfate, which reduces the production of methane, a valuable product, while producing hydrogen sulfide, a corrosive contaminant. This Narrative collection has been created to highlight the types of analyses done for this publication. They are as follows (in order): SJ1 Metagenomes: a metagenomic analysis of sample SJ1, the initial bioreactor. SJ1 Metatranscriptomes: QC-filtered raw RNA reads were aligned to individual MAGs and assembled into transcripts. Metagenome-discovered food webs in a long-term methanogenic bioreactor and rapid switch to sulfate reducing conditions (this Narrative): the public Narrative referenced in the paper, which contains several key MAGs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Long-Term Stability Metrics of Photoelectrochemical Water Splitting

Photoelectrochemical (PEC) water splitting, one of the most promising technologies for clean hydrogen generation, has drawn considerable attention over the past few decades. Achieving simultaneous highly efficient and stable unassisted PEC water splitting has been the “holy grail” in clean and renewable fuel generation. State-of-the-art photoelectrodes have shown relatively high efficiencies (∼10–20%). Still, their stability is limited due to photoelectrode chemical instability, electrolyte resistance, mass transfer issues, and an often unoptimized experimental setup. In this work, we present a framework and a set of protocols for conducting long-term stability experiments and further provide details on several critical factors such as light source calibration, choosing the right counter electrode, the configuration of the PEC cell, and photoelectrode sample preparation.

08 HYDROGEN↗
DOE OSTI2023

Coastal inundation regime moderates the short-term effects of sediment and soil additions on seawater oxygen and greenhouse gas dynamics: a microcosm experiment

The frequency and persistence of tidal inundation varies along the coastal terrestrial-aquatic interface, from frequently inundated wetlands to rarely inundated upland forests. This inundation gradient controls soil and sediment biogeochemistry and influence the exchange of soils and sediments from terrestrial to aquatic domains. Although a rich literature exist on studies of the influence of tidal waters on the biogeochemistry of coastal ecosystem soils, few studies have experimentally addressed the reverse question: How do soils (or sediments) from different coastal ecosystems influence the biogeochemistry of the tidal waters that inundate them? To better understand initial responses of coastal waters that flood coastal wetlands and uplands, we conducted short-term laboratory experiments where seawater was amended with sediments and soils collected across regional gradients of inundation exposure (i.e., frequently to rarely inundated) for 14 sites across the Mid-Atlantic, USA. Measured changes in dissolved oxygen and greenhouse gas concentrations were used to calculate gas consumption or production rates occurring during seawater exposure to terrestrial materials. We also measured soil and water physical and chemical properties to explore potential drivers. We observed higher oxygen consumption rates for seawater incubated with soils/sediments from frequently inundated locations and higher carbon dioxide production for seawater incubated with soils from rarely inundated transect locations. Incubations with soil from rarely inundated sites produced the highest global warming potential, primarily driven by carbon dioxide and secondarily by nitrous oxide. We also found environmental drivers of gas rates varied notably between transect locations. Our findings indicate that seawater responses to soil and sediment inputs across coastal terrestrial-aquatic interfaces exhibit some consistent patterns and high intra- and inter-site variability, suggesting potential biogeochemical feedback loops as inundation regimes shift inland.

coastal inundation↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗
DOE OSTI2024

Effectiveness and Long-Term Effects of SER+ FELIZ(mente): A School-Based Mindfulness Program for Portuguese Elementary Students

School-based mindfulness programs (SBMPs) have gained global popularity. Yet, there is a need for more rigorous procedures to develop and assess them. This study aimed to address these limitations by examining the effectiveness of a Portuguese SBMP, called SER + FELIZ(mente). The final sample included 190 third and fourth-graders: 99 in the SBMP group and 91 in a wait-list group. Effects on attentional control, emotional regulation and wellbeing were examined in the short term (T2) and 6 months later (T3). We also tested the moderating role of age, gender, and baseline scores. Using a multilevel modeling approach, results showed a clear benefit of SBMP on emotional wellbeing at T2 but not T3. Despite SBMP students surpassing wait-list students in attentional control, cognitive reappraisal, and social wellbeing at T2, these effects were due to a levelling off among SBMP students, coupled with a decline among wait-list students from T1 to T2. These effects were moderated by age at T3. While universal improvements were limited, our SBMP seemed to have acted as a shield against school stressors (i.e., likely, test anxiety at T2). This opens a new avenue for research concerning the role of SBMPs in preventing (rather than repairing) mental-health issues in elementary students.

Magalhães, Sofia↗
DOE OSTI2025

Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors.

LSTM↗
DOE OSTI2020

Predicting Emission Source Terms in a Reduced-Order Fire Spread Model—Part 1: Particulate Emissions

A simple, easy-to-evaluate, surrogate model was developed for predicting the particle emission source term in wildfire simulations. In creating this model, we conceptualized wildfire as a series of flamelets, and using this concept of flamelets, we developed a one-dimensional model to represent the structure of these flamelets which then could be used to simulate the evolution of a single flamelet. A previously developed soot model was executed within this flamelet simulation which could produce a particle size distribution. Executing this flamelet simulation 1200 times with varying conditions created a data set of emitted particle size distributions to which simple rational equations could be tuned to predict a particle emission factor, mean particle size, and standard deviation of particle sizes. These surrogate models (the rational equation) were implemented into a reduced-order fire spread model, QUIC-Fire. Using QUIC-Fire, an ensemble of simulations were executed for grassland fires, southeast U.S. conifer forests, and western mountain conifer forests. Resulting emission factors from this ensemble were compared against field data for these fire classes with promising results. Also shown is a predicted averaged resulting particle size distribution with the bulk of particles produced to be on the order of 1 μm in size.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Highly Insulated Wall Systems with Exterior Insulation of Polyisocyanurate under Different Facer Materials: Material Characterization and Long-Term Hygrothermal Performance Assessment

The application of exterior insulation in both new construction and retrofits is a common practice to enhance the energy efficiency of buildings. In addition to increased thermal performance, the rigid insulation can serve to keep the sheathing board warm and serve as a water-resistive barrier to keep moisture-related problems due to condensation and wind-driven rain. Polyisocyanurate (PIR) rigid boards have a higher thermal resistance in comparison to other commonly used exterior insulation boards. However, because of its perceived lower permeance, its use as exterior insulation is not very common. In this study, the hygrothermal property of PIR boards with different facer types and thicknesses is characterized. The material data obtained through experimental test and extrapolation is used in a long term hygrothermal performance assessment of a wood frame wall with PIR boards as exterior insulation. Results show that PIR with no facer has the smallest accumulated moisture on the sheathing board in comparison to other insulation boards. Walls with a bigger thickness of exterior insulation perform better when no vapor barrier is used. The PIR exterior insulation supports the moisture control strategy well in colder climates in perfect wall scenarios, where there is no air leakage and moisture intrusion. In cases where there is trapped moisture, the sheathing board has a higher moisture content with PIR boards with both aluminum or fiberglass type facers. An innovative facer material development for PIR boards can help efforts targeting improved energy-efficient and durable wall systems.

36 MATERIALS SCIENCE↗