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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Monitoring the long-term performance of organic redox flow battery by a distribution of relaxation time analysis

Organic redox flow batteries hold great promise as an energy storage technology, but their intricate chemistry makes them vulnerable to various degradation mechanisms. Monitoring this degradation is essential for identifying the limiting processes within the cells. Electrochemical impedance spectroscopy (EIS) offers a straightforward, in-situ method for measuring the total resistance of an operating cell. However, to pinpoint the limiting processes during long-term cycling, EIS data must be complemented by other techniques. Distribution of relaxation time (DRT) analysis is particularly effective for differentiating resistance components. Here, in this study, we perform a comprehensive analysis of resistance evolution and the separation of anode and cathode contributions during long-term cycling of a full cell employing 7,8-dihydroxyphenazine-2-sulfonic acid (DHPS) as the anolyte. Separate analyses of the DHPS anolyte and ferri-/ferrocyanide catholyte were conducted using a symmetric cell setup. The relaxation times derived from symmetric cells facilitate the identification of peaks in the DRT profiles from the full cell. Importantly, the DRT profiles indicate a correlation between the evolution of charge transfer resistance and the chemical degradation of DHPS. The methodologies and results outlined in this study offer significant insights for developing diagnostic tools applicable to other types of redox flow batteries.

Distribution of relaxation time↗
DOE OSTI2020

Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. Therefore, for the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Long term durability test and post mortem for metal-supported solid oxide electrolysis cells

Hydrogen is a renewable energy carrier, and electrolysis to split water is the most environmentally friendly method to produce hydrogen. This work reports long-term durability and degradation mode analysis for metal-supported solid oxide electrolysis cells (MS-SOECs). Catalyst screening showed that MS-SOECs with composite electrode catalysts (samarium-doped ceria-nickel [SDC-Ni] serving as a fuel electrode catalyst, and praseodymium oxide [PrO x ]-SDC or La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 [LSCF]-SDC serving as an air electrode catalyst) exhibit the highest electrochemical performance at 700 °C. The degradation rate of cells with LSCF-SDC as the air electrode catalyst was as low as 1.3%/100 h in long term durability tests at a current density of 0.33 A cm -2 , in contrast to rapid degradation observed for a cell with a PrO x -SDC air electrode. Furthermore, post-mortem analysis reveals the degradation is dependent on the primary modes of fuel electrode catalyst coarsening and Cr poisoning on the air electrode catalyst, as well as secondary modes of oxidation of the metal support and local elemental accumulation of Ni. Other degradation modes reported in conventional anode-supported SOECs, such as Ni migration, foreign element contamination, delamination of the cell, and nano-voids on the electrolyte, are not observed in the present MS-SOECs.

25 ENERGY STORAGE↗
DOE OSTI2024

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

58 GEOSCIENCES↗
DOE OSTI2024

Degradation of a Cr-Mo steel by carbide precipitation over long-term service

Microstructure of the 40-year service-exposed 1Cr-0.5Mo steel extracted from pressure vessels through the boat sampling technique was characterized using x-ray diffraction, field-emission scanning electron microscopy, energy dispersive spectrometry, electron backscatter diffraction, focused ion beam, and transmission electron microscopy. The microstructure degradation during long-term service was identified. Statistical crystal plastic ity models were developed to correlate the microstructure and mechanical properties quantitatively. The steel is found significantly strengthened but slightly embrittled. Nanoscale Mo 2 C carbides have nucleated within the ferrite matrix over the long-term service, resulting in significant strengthening of the matrix. The coarser grain boundary Cr 23 C 6 carbides, on the other hand, act as the damage accumulation sites and lead to the degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ALPHANSO: Open-source modeling of (α, n) neutron source terms

Applications ranging from nuclear safeguards to dark matter detection require accurate predictions of neutron yields and energy spectra produced by (α, n) reactions. Legacy tools like SOURCES-4C remain widely used despite significant limitations, including outdated nuclear data, missing target nuclides, and restricted accessibility. Here, we present ALPHANSO, an open-source Python package for calculating (α, n) neutron source terms. ALPHANSO incorporates modern nuclear data libraries and formats covering all naturally occurring target nuclides and provides a transparent, modular framework for updating or extending the data as new evaluations are released. Comparison with an updated version of SOURCES-4A, NeuCBOT, and experimental measurements across a range of elements and materials shows that ALPHANSO reproduces neutron yields and spectra in good agreement with experimental data and state-of-the-art (α, n) calculations. These results demonstrate that ALPHANSO is a reliable, accessible, and modern alternative to legacy (α, n) source term codes such as SOURCES-4C. Its open-source design and modular data handling make it readily extensible to future evaluated nuclear data and low-background applications.

(α, n) reactions↗
DOE OSTI2021

Initial calculations for source term of Molten Salt Reactors

This paper provides an overview of the current MSR design space and lists unique features of the various designs under consideration. Some general considerations for source terms calculation for Molten Salt Reactors (MSRs) are explained. Applicability and limitations of terminology currently defined for legacy light water reactor (LWR) systems are discussed in the view of MSRs and the need for updated terminology is discussed. Calculations carried out for the Molten Salt Reactor Experiment (MSRE) are discussed with a qualitative comparison to the designs presented. The nature of the fission products (FPs) and actinides for Low enriched uranium, thorium and fast U/Pu fuel cycles employed in representative molten salt reactor systems are discussed. Computational results are obtained from a code (Serpent 2) with online reprocessing. Divergence in source terms when fission product bubbling is demonstrated. The source release for each molten salt reactor during postulated accidents is also presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Seasonal and long-term variations in leaf area of Congolese rainforest

It is important to understand temporal and spatial variations in the structure and photosynthetic capacity of tropical rainforests in a world of changing climate, increased disturbances and human appropriation. The equatorial rainforests of Central Africa are the second largest and least disturbed of the biodiversly-rich and highly productive rainforests on Earth. Currently, there is a dearth of knowledge about the phenological behavior and long-term changes that these forests are experiencing. Here, this study reports on leaf area seasonality and its time trend over the past two decades as assessed from multiple remotely sensed datasets. Seasonal variations of leaf area in Congolese forests derived from MODIS data co-vary with the bimodal precipitation pattern in this region, with higher values during the wet season. Independent observational evidence derived from MISR and EPIC sensors in the form of angular reflectance signatures further corroborate this seasonal behavior of leaf area. The bimodal patterns vary latitudinally within this large region. Two sub-seasonal cycles, each consisting of a dry and wet season, could be discerned clearly. These exhibit different sensitivities to changes in precipitation. Contrary to a previous published report, no widespread decline in leaf area was detected across the entire extent of the Congolese rainforests over the past two decades with the latest MODIS Collection 6 dataset. Long-term precipitation decline did occur in some localized areas, but these had minimal impacts on leaf area, as inferred from MODIS and MISR multi-angle observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Toward Long-Term Accurate and Continuous Monitoring of Nitrate in Wastewater Using Poly(tetrafluoroethylene) (PTFE)–Solid-State Ion-Selective Electrodes (S-ISEs)

Long-term accurate and continuous monitoring of nitrate (NO 3 – ) concentration in wastewater and groundwater is critical for determining treatment efficiency and tracking contaminant transport. Current nitrate monitoring technologies, including colorimetric, chromatographic, biometric, and electrochemical sensors, are not feasible for continuous monitoring. In this work, we addressed this challenge by modifying NO 3 – solid-state ion-selective electrodes (S-ISEs) with poly(tetrafluoroethylene) (PTFE, (C 2 F 4 ) n ). The PTFE-loaded S-ISE membrane polymer matrix reduces water layer formation between the membrane and electrode/solid contact, while paradoxically, the even more hydrophobic PTFE-loaded S-ISE membrane prevents bacterial attachment despite the opposite approach of hydrophilic modifications in other antifouling sensor designs. Specifically, an optimal ratio of 5% PTFE in the S-ISE polymer matrix was determined by a series of characterization tests in real wastewater. Five percent of PTFE alleviated biofouling to the sensor surface by enhancing the negative charge (–4.5 to –45.8 mV) and lowering surface roughness (Ra: 0.56 ± 0.02 nm). It simultaneously mitigated water layer formation between the membrane and electrode by increasing hydrophobicity (contact angle: 104°) and membrane adhesion and thus minimized the reading (mV) drift in the baseline sensitivity (“data drifting”). Long-term accuracy and durability of 5% PTFE-loaded NO 3 – S-ISEs were well demonstrated in real wastewater over 20 days, an improvement over commercial sensor longevity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Bayesian Deep Learning Approach to Near-Term Climate Prediction

Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre-industrial control simulation of the Community Earth System Model. While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir computing networks in this and other similar problem settings, we currently focus on the use of feedforward convolutional networks. In particular, we find that a feedforward convolutional network with a Densenet architecture is able to outperform a convolutional LSTM in terms of predictive skill. Next, we go on to consider a probabilistic formulation of the same network based on Stein variational gradient descent and find that in addition to providing useful measures of predictive uncertainty, the probabilistic (Bayesian) version improves on its deterministic counterpart in terms of predictive skill. Finally, we characterize the reliability of the ensemble of machine learning models obtained in the probabilistic setting by using analysis tools developed in the context of ensemble numerical weather prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor-corrector type nudging approaches are still attractive due to their simplicity of implementation when more complex methods need to be avoided. However, optimal estimate of nudging gain matrix might be cumbersome. In this paper, we put forth a fully nonintrusive recurrent neural network approach based on a long short-term memory (LSTM) embedding architecture to estimate the nudging term, which plays a role not only to force the state trajectories to the observations but also acts as a stabilizer. Furthermore, our approach relies on the power of archival data and the trained model can be retrained effectively due to power of transfer learning in any neural network applications. In order to verify the feasibility of the proposed approach, we perform twin experiments using Lorenz 96 system. Our results demonstrate that the proposed LSTM nudging approach yields more accurate estimates than both extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) when only sparse observations are available. With the availability of emerging AI-friendly and modular hardware technologies and heterogeneous computing platforms, we articulate that our simplistic nudging framework turns out to be computationally more efficient than either the EKF or EnKF approaches.

42 ENGINEERING↗
DOE OSTI2022

Evidence for long-term potentiation in phospholipid membranes

Biological supramolecular assemblies, such as phospholipid bilayer membranes, have been used to demonstrate signal processing via short-term synaptic plasticity (STP) in the form of paired pulse facilitation and depression, emulating the brain’s efficiency and flexible cognitive capabilities. However, STP memory in lipid bilayers is volatile and cannot be stored or accessed over relevant periods of time, a key requirement for learning. Using droplet interface bilayers (DIBs) composed of lipids, water and hexadecane, and an electrical stimulation training protocol featuring repetitive sinusoidal voltage cycling, we show that DIBs displaying memcapacitive properties can also exhibit persistent synaptic plasticity in the form of long-term potentiation (LTP) associated with capacitive energy storage in the phospholipid bilayer. The time scales for the physical changes associated with the LTP range between minutes and hours, and are substantially longer than previous STP studies, where stored energy dissipated after only a few seconds. STP behavior is the result of reversible changes in bilayer area and thickness. On the other hand, LTP is the result of additional molecular and structural changes to the zwitterionic lipid headgroups and the dielectric properties of the lipid bilayer that result from the buildup of an increasingly asymmetric charge distribution at the bilayer interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Investigating the Role of Accident Tolerant Cladding on Source Term Reduction for High-Burnup PWRs Using MELCOR

The use of accident tolerant fuel (ATF) cladding can increase coping times during and beyond design basis accidents. While such gains may be incremental, they provide a margin that can potentially be recovered to enable high-burnup (HBU) operation. Realizing such a margin requires demonstrating that the combination of HBU and ATF has not led to an overall increase in source term. This study investigates the influence of cladding technology (Zr-based, Cr-coated Zr, and FeCrAl) and fuel cycle length (18 and 24 months) on radiological dose at the boundary of the exclusion zone for a four-loop pressurized water reactor to investigate whether ATF claddings can provide such benefits. We analyze a recovered large break loss-of-coolant accident scenario to investigate the impact of transient timescale on the benefits of such coping time increases. The simulations have been performed using the MELCOR and MELCOR Accident Consequence Code System codes. For the cases analyzed, increased fuel cycle length did not necessarily increase radionuclide release and hydrogen generation, as these were found to be sensitive to the core power distribution. Similarly, off-site dose consequence is dominated by short-lived radionuclides that tend to saturate earlier in the burnup, so higher burnup operation did not necessarily increase the source term for the phenomena and transients analyzed here. Delays in recovery of the lowpressure safety injection system increase hydrogen production and radionuclide release, especially between 780 s and 1620 s, due to the nonlinear oxidation and core degradation behavior. Results show that Cr-coated Zr enhances safety by delaying heatup and gap release. Here, when uncertainty propagation on oxidation properties is considered, FeCrAl exhibits the lowest overall radionuclide release and off-site dose throughout the spectrum. However, while the considered “base model” performance is superior under delayed injection scenarios, upper-bound cases display hydrogen generation risk comparable to the Zr-based cladding.

Accident Tolerant Fuel↗
DOE OSTI2022

Tri-Sectional Approximation of the Shortest Path to Long-Term Voltage Stability Boundary with Distributed Energy Resources

Ensuring long-term voltage stability is critical for reliable operations of power grids. High share of distributed energy resources (DERs) can create complicated system operation modes that may invalidate the traditional long-term voltage stability analysis based on typical operation modes. To address this challenge, this paper investigates how to compute the shortest path to the voltage stability boundary in the DER aggregated load space with large dispersion. Instead of working in the Euclidean space, we establish the analysis and computations on the algebraic power flow manifold to better capture the curvature change of the shortest path along the direction of losing stability. A modified optimal control framework is presented for obtaining the ground-truth of the smooth shortest path on the manifold. To efficiently and accurately solve for the shortest path, we further leverage the geometric features of the power flow manifold and propose a tri-sectional approximation model that is scalable for large-scale systems. Several numerical examples, up to the 1354-bus system, with different DER penetration levels and high dimensional renewable power injection variations are evaluated. The simulation results demonstrate that the tri-sectional approximation achieves high accuracy and efficiency to approximate the shortest path to the voltage stability boundary.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Machine Learning Assisted Reservoir Operation Model for Long–Term Water Management Simulation

This study explores strategies for long-term reservoir simulations by combining generic rule-based reservoir management model (RMM) and machine learning (ML) models for two major multipurpose reservoirs — Allatoona Lake and Lake Sidney Lanier in the southeastern United States. First, a standalone RMM is developed to simulate daily release and storage during Water Year 1981–2015. Next, using Long-Short Term Memory (LSTM) as the ML technique, a standalone LSTM model is trained based on reservoir inflow and meteorological observations to simulate reservoir release and estimate reservoir storage through water balance calculation. Three hybrid modeling strategies are developed, one using RMM output as an additional LSTM input (H1), another using LSTM as the initial release estimate in RMM (H2), and the third combining the first two strategies (H3). The Nash–Sutcliffe efficiency (NSE) for release (NSE-r), storage (NSE-s), and their mean (NSE-avg) are used for model evaluation. Overall, H1 improves NSE-r to 0.65 and 0.54 for Allatoona and Lanier, respectively, compared to standalone RMM (0.44 and 0.21); however, its storage trajectory did not produce a physically feasible solution, similar to LSTM. H2 and especially H3 show that they can retain the best features from RMM and LSTM, with H3 NSE-avg being 0.695 and 0.55 for Allatoona and Lanier outperforming RMM (0.615 and 0.29). In conclusion, the findings suggest a robust simulation capacity for large-scale water management in future studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Constellation's best estimate alternate source term methodology overview

Safety analyses for a nuclear power plants need to consider postulated accidents that results in at risk of accidental release of radiation. The regulations require plant specific safety analysis reports to include an evaluation of the requirements of 10CFR50.67. Such a safety analysis report mandates limits such that calculated radiological consequences relative to certain dose locations do not exceed a total effective dose equivalent (TEDE) limits following a postulated release of radioactivity. Calculations are performed to estimate the radiological consequences, in terms of dose, to people and equipment to ensure the estimated doses are within the prescribed limits. Analysis should demonstrate, with reasonable assurance, that these prescribed limits are complied with. Conventional methodologies utilize conservative approaches to address lack of uncertainty quantification in the utilized approaches, methods, and/or inputs. These built-in excess conservatisms often result in compounding effects and hence overly conservative results in the estimated radiological consequences, which leads to inaccurate margin evaluation for operation and accident mitigation. Therefore, evaluating accurate dose consequences is needed for both operational and safety reasons. The research documented in this paper provides an overview of Constellation's Best Estimate Alternate Source Term (BEAST) Methodology. BEAST methodology relies upon the use of realistic yet bounding input distributions for key analysis parameters, which replaces use of conservative deterministic singular inputs that bound overall analysis domain. This approach enables evaluating a more accurate accident analysis response, while enabling a bounding licensing basis envelope via more accurate quantification of uncertainty in the application. Therefore, built-in margin for a given scenario is more accurately evaluated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Improving the Estimation of the Atmospheric Water Vapor Pressure Using Interpretable Long Short-Term Memory Networks: Dataset, Python code, and trained models

Atmospheric water vapor pressure is an essential meteorological control on land surface and hydrologic processes. It is not as frequently observed as other meteorologic conditions, but often inferred through the August–Roche–Magnus formula by simply assuming dew point and daily minimum temperatures are equivalent or by empirically correlating the two temperatures using an aridity correction. The performance of both methods varies considerably across different regions and during different time periods; obtaining consistently accurate estimates across space and time remains a great challenge. We applied an interpretable Long Short-Term Memory (iLSTM) network conditioned on static, location specific attributes to estimate daily vapor pressure for 83 FLUXNET sites in the United States and Canada. This data package includes all raw data of the 83 FLUXNET sites, input data for model training/validation/test, trained models and results, and python codes for the manuscript "Improving the Estimation of the Atmospheric Water Vapor Pressure Using an Interpretable Long Short-term Memory Network". Specifically, it consists of five parts. - First, "1_Daymet_data_83sites.zip" includes raw data downloaded from Daymet for the 83 sites used in the paper according to their longitude and latitude, in which vapor pressure is used. It also includes a pre-processed CSV data file combining all data from the 83 sites which is specifically used for the paper. - Second, "2_Fluxnet2015_data_83sites.zip" includes raw half hourly data of the 83 sites downloaded from FLUXNET2015 data portal, pre-processed daily data of the 83 sites, a CSV file including combined pre-processed daily data of the 83 sites, and a CSV file including the information (site ID, site name, latitude, longitude, data available period) of the 83 sites. - Third, "3_MODIS_LAI_data_83sites_raw.zip" includes raw leaf area index (LAI) data downloaded from the AppEEARs data portal. - Fourth, "4_Scripts.zip" includes all scripts related to model training and post-processing of a trained model, and a jupyter notebook showing an example for model post-processing. Two typo errors in files titled "run2get_args.py" and "postprocess.py" were corrected on March 27, 2024 to avoid confusions. - Finally, "Trained_models_and_results.zip" includes three folders and three files with suffix ".npy", and each folder corresponds to one file with suffix ".npy" with the same title. Each of the three folders include all trained models associated with one iLSTM model configuration (35 models for each configuration, details are described in the paper). Each file with suffix ".npy" includes the post-processed results of the corresponding 35 models under one iLSTM model configuration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Functional Requirements for the Modeling and Simulation of Advanced (Non-LWR) Reactor Mechanistic Source Term

A number of industry vendors are leading a resurgence in advanced reactor development. As the assurance of public safety from the accidental release of radionuclides to the environment is central to regulatory licensing and the reactor design process, the development of a mechanistic source term (MST) assessment has been identified by the advanced reactor industry as a foremost priority. In contrast to historic light-water reactor (LWR) source term analysis efforts, an MST attempts to realistically model the release and transport of radionuclides from the source to the environment for specific scenarios, while accounting for retention or transmutation phenomena and associated uncertainties. This work seeks to aid in the development of MST modeling and simulation tools by performing a preliminary identification of functional requirements for the major advanced reactor types.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗