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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluation of Glovebox Fires Involving Flammable Liquids and Standard Glovebox Tools (Final Report)

This research effort was commissioned to examine factors related to loss of containment due to fire within a glovebox (GB). Tests consisted of multiple trials with varying initial conditions. Com bustible levels and types were chosen to reflect realistic operation conditions (including worst-case scenarios), involving flammable liquids, GB tools, and other combustible items commonly found in GBs at Los Alamos National Labs (LANL).

42 ENGINEERING↗
DOE OSTI2023

Project report for year 1

In the first year of the project, we developed a fire event tracking system using active fire detections recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS). We employed this algorithm to track all large fires that occurred in California from 2013 to 2020, and created a retrospective Fire Event Data Suite (FEDS) that contains the vector shapes of fire perimeters, active fire fronts, and the locations and fire radiative powers for each fire pixel at 12-hour temporal resolution for each fire event. Using the 2020 Creek Fire as a test bed, we also delineated hourly fire perimeters by combining the FEDS data with the hourly rates of spread derived from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) fire data. This dataset will form the basis of the near-real-time hourly fire emissions product that we plan to develop at years 2 and 3 of the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Burn Intensity Drives the Alteration of Phenolic Lignin to (Poly) Aromatic Hydrocarbons as Revealed by Pyrolysis Gas Chromatography–Mass Spectrometry (Py-GC/MS)

High-intensity wildfires alter the chemical composition of organic matter, which is expected to be distinctly different from low-intensity prescribed fires. In this study, we used pyrolysis gas chromatography/mass spectrometry (Py-GC/MS), in conjunction with solid-state 13 C nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopy, to assess chemical alterations from three wildfires and a long-term frequent prescribed fire site. Our results showed that black ash formed under moderate intensity burns contained less aromatic (ArH), polyaromatic hydrocarbon (PAH), and nitrogen-containing compounds (Ntg) but more lignin (LgC) and phenol compounds (PhC), compared to white ash formed under high intensity burns. Both 13 C NMR and FT-IR confirmed a higher relative percentage of carboxyl carbon in white ash, indicating the potential for higher water solubility and more mobile carbon, relative to black ash. Compared to wildfires, ash from low-intensity prescribed fire contained less ArH, PAH, and Ntg and more LgC and PhC. Controlled laboratory burning trials indicated that organic matter alteration was sensitive to the burn temperature, but not related to the fuel type (pine vs fir) nor oxygen absence/presence at high burn temperatures. This study concludes that higher burn temperatures resulted in higher (poly)aromatic carbon/nitrogen and lower lignin/phenol compounds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Computational screening of fly ash zeolite sorbents for boric acid removal

In the United States, many impoundments at coal-fired power plants contain elevated contaminants like arsenic, boron, barium, and selenium. Zeolites synthesized from fly ash show promise as sorbents for these contaminants. However, optimizing sorption capacity is challenging due to numerous possible topologies, silicon to aluminum (Si/Al) ratios, and cation types. In this study, molecular simulations are used to design cationic zeolites for boric acid adsorption. Force field models based on quantum mechanical calculations (PBE + D2) for Na-, Ca-, Mn-, and Fe-exchanged chabazite and LTA are presented. The new D2FF force fields reproduce DFT energies with about half the error of UFF. Zeolite performance depends on Si/Al ratio and cation type, with low Si/Al ratio chabazite (CHA) and phillipsite (PHI) zeolite frameworks exchanged with Ca 2+ or Na + /Ca 2+ mixtures showing the highest adsorption. In conclusion, these findings suggest tailored fly ash-derived zeolites could provide effective boron removal from leachate ponds.

CCR impoundment↗
DOE OSTI2023

Effective removal of trace-level toxic metals from flue gas desulfurization wastewater using SiO 2 supported hydrogel sorbent

Flue gas desulfurization (FGD) wastewater generated from coal-fired power plants contain potentially harmful heavy metal pollutants that pose a threat to public health and clean water. In this work, we present a water stable polyethylenimine-n,n’-methylenebisacrylamide (PEI-MBAA) functionalized SiO 2 solid sorbent material (PMS-1.2/1/4) and investigate its metal adsorption kinetics, selectivity, regenerability, and space velocity. The kinetic studies of six of the toxic heavy metals (As, Cd, Cr, Pb, Se, and Hg) prepared with single elements in Milli-Q water showed the effect of chemical bonding and intraparticle mass transfer resistance on the sorption process. The selectivity studies demonstrated the significant adsorption efficiency toward trace-level heavy metals (Se, Cd, U, Al, etc.) from authentic industrial FGD wastewater. Through five consecutive adsorption–desorption cycles with the FGD (uptake)-citrate (release)-based buffer pair, the sorbent showed high heavy metal removal ability and good reusability. The maximum flow rate for the removal of Se from industrial FGD wastewater was determined to be as high as 8 bed volumes/minute of the sorbent bed. Finally, the results demonstrate the PMS-1.2/1.4 sorbent is a promising candidate for the removal of heavy metals from practical aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tropical root traits in response to global changes from 1984 to 2023

This dataset is a compilation of tropical root traits data in response to different global changes in tropical sites, considering 23.50N and S as latitudinal boundaries. The global changes considered are warming, drought, flooding, cyclones, nitrogen addition, CO2 fertilization, and fire. This dataset contains 266 root trait observations from 93 studies across 24 tropical countries. The full citation from where the data was taken from is provided in the dataset, as well as the global change, the ecosystem type, location, coordinates, the root traits measured, and the direction of their response after the global change. Additional information such as the duration of the experiment, the intensity of the global change, the soil layers from where the roots were collected, the root orders, and the type of experiment are also shown. We obtained this dataset by performing a systematic literature review on Web of Science using standardized keywords in English, Spanish, and Portuguese (Yaffar, Lugli et al. in press).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data. The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made. The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library. Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.

Coal ash composition↗
DOE OSTI2020

FINAL TECHNICAL AND ECONOMIC FEASIBILITY STUDY ON THE APPLICATION OF A HEAT INTEGRATED POST-COMBUSTION CO2 CAPTURE SYSTEM WITH HITACHI ADVANCED SOLVENT INTO EXISTING COAL-FIRED POWER PLANT

This report contains the results of a techno-economic assessment (TEA) conducted of a heat integrated post-combustion CO2 capture process with Hitachi advanced solvent for retrofit into an existing coal-fired power plant (but treated as greenfield plant on cost analysis). The process has been developed by the University of Kentucky Center for Applied Energy (UK CAER). EPRI was chiefly responsible for this analysis, with significant input from WorleyParsons, Hitachi Power Systems America (Hitachi) and UK CAER. The project also involves the design, fabrication, installation, testing, and analyses of a slipstream facility located at L&GE-KU’s E.W. Brown Generating Station to demonstrate the UK CAER carbon capture system that could utilize heat integration with the main power plant. The design, start-up, and baseline of the pilot system was performed with a generic 30 wt% MEA solvent to obtain data for direct comparison with the DOE/NETL Reference Case followed by testing Hitachi’s proprietary solvent H3-1. In this techno-economic analysis, two cases utilizing the UK CAER process are compared, using different approach temperatures and solvent, against the DOE/NETL Reference Case (Case 10). The results are shown comparing the energy demand for post-combustion CO2 capture and the net higher heating value (HHV) efficiency of the power plant integrated with the post-combustion capture (PCC) plant. A levelized cost of electricity (LCOE) assessment was performed showing the costs of the options presented in the study. The key factors contributing to the reduction of LCOE were identified as CO2 partial pressure increase at the flue gas inlet, thermal integration of the process, and performance of the Hitachi H3-1 solvent. Recent UK CAER process pilot-scale testing data and process simualtion data showed that the packing heights of absorber and stripper columns were significantly oversized in the prelimanary TEA (Task 2 of this project) and thus updated in this final TEA for the H3-1 case only. In addition, the solvent make-up cost for H3-1 was updated based on lattest test results. Finally, a heat integration with the main power plant was applied in this final TEA to increase overall energy effciency for both the MEA and H3-1 cases. Additonal reductions in capital and operational costs are expected but not taken into account here. Shorter columns result in reduced pressure drops, smaller blower head and pump hydraulic head requirements. An increase in overall energy efficiency resuls in a decreased size of the power plant, the CCS and a reduced parasitic steam requirement to the CCS. The net efficiency of the UK CAER integrated PC power plant with CO2 capture changes from 26.2% for the Reference Case 10 plant in 2010 revised DOE/NETL baseline report to 27.6% for the MEA options considered, and 29.1% for the options utilizing the Hitachi advanced solvent. The UK CAER Process + Hitachi case also produces an extra 30.9 MW of generation compared to the UK CAER Process + MEA case and total 60.9 MW more than DOE Case 10. LCOE ($/MWh) values are $172.08/MWh for the MEA option and $157.65/MWh for the Hitachi H3-1 solvent cases considered in comparison to $189.59/MWh in January 2012 dollar for the Reference Case 10. The UK CAER CCS process with MEA case lowers energy consumption for CO2 capture to 1340 Btu/lb-CO2 captured as compared to 1540 Btu/lb-CO2 in the Reference Case 10. The UK CAER CCS process with H3-1 case further lowers energy consumption for CO2 capture to 973 Btu/lb-CO2 captured, for an advantage of 36.8% less energy consumption than Case 10. The study also shows 38.1% less heat rejection associated with the carbon capture system from 3398 MBtu/hr (Case 10) to 2104 MBtu/hr for the UK CAER + MEA system. Heat rejection is reduced to 2464 MBtu/hr in the UK CAER + H3-1 case, for a 27.5 % decrease compared to Case 10. Modeling outputs show that in the UK CAER process, the cooling water that is 2-5°C cooler than conventional cooling tower water can be achieved for ambient conditions common to the midwest and other regions. The results from the techno-economic assessment show that the proposed technology can be investigated further as a viable alternative to conventional CO2 capture technology. The evaluation also shows the effect of the critical parameters on the LCOE, with the main variables being the approach temperature and CO2 partial pressure increase at the flue gas inlet. A summary of the key advantages of the UK CAER Process + H3-1 case for LCOE and other economic factors compared to the DOE Case 10 is as follows: • A lower variable operating cost by $1.56/MWh ($1.08MWh less than the UK CAER Process + MEA Case), a 11.7% reduction compared to the DOE Case 10 • A lower COE by $25.32MWh ($13.94/MWh lower than the UK CAER Process + MEA Case), a 16.9% reduction compared to the DOE Case 10 • A lower LCOE by $31.94/MWh ($17.51/MWh lower than the UK CAER Process + MEA Case), a 16.9% reduction compared to the DOE Case 10 • A lower cost of CO2 captured by $18.65/tonne CO2 ($9.44/tonne CO2 lower than the UK CAER Process + MEA Case), a 30.4% reduction compared to the DOE Case 10 • A lower cost of CO2 avoided by $34.95/tonne CO2 ($18.53 tonne CO2 lower than the UK CAER Process + MEA Case), a 38.7% reduction compared to the DOE Case 10

Bhown, Abhoyjit S.↗
DOE OSTI2023

Terrain-Influenced Winds and Fire-Fire Interactions in Wildland Fire Simulations [Dissertation]

Ensemble-based approaches to prescribed fire planning cannot be supported by computational fluid dynamics based models like FIRETEC and the Wildland-Urban Interface Fire Dynamics Simulator (WFDS) because they are too computationally expensive and cannot leverage large eddy simulation approaches like CAWFE and WRF-SFIRE because they have too coarse of resolution. QUIC-Fire was developed to fill this gap but it cannot currently address complex terrain, that is typical for instance in the Western United States. This dissertation describes a variety of improvements made to QUIC-Fire and its various incorporated algorithms in an effort to make it a viable tool in simulating wildland and prescribed fires on terrain. The modifications made to QUIC-Fire are described in three chapters. The first chapter describes the extension of the diagnostic wind model QUIC-URB, the wind engine of QUIC-Fire, to a terrain-following coordinate system. The terraininfluenced winds it generates are analyzed and compared. In particular, this chapter presents the mathematical derivation of the wind solver leading to a linear system of equations that are solved through the successive over-relaxation method. The model is validated against a standard test used in previous works (the Askervein Hill) and against a new dataset from measurements in the Socorro Mountains, New Mexico. The terrain-following implementation captures the correct phenomenology for the isolated Askervein Hill, with a wind speed up at the top of the hill. The model agrees well with measurements on the upwind side of the peak, but overestimates speed-up on the downwind side of the hill. This is due to the inability of the model to generate flow separation and wake-eddy dynamics. On a common laptop, the divergence-free wind field is obtained in 6 s, making the solver appealing for coupled fire-atmosphere simulations. The Socorro Mountain is highly complex, with many cliff faces, peaks, and valleys. Although the model captures the magnitude and direction of inlet and outlet areas of the domain, it performs rather poorly in the valley region and in the regions near the steep cliffs. Hence, the model shows good agreement with data in areas of open sloped terrain but lacks in areas where flow separation and thermally driven effects may be present (neither effect is addressed in this work). In the second chapter the implementation of the terrain-following version of QUIC-URB into QUIC-Fire, and the necessary changes needed to include terrain are described. No changes to the underlying fire spread algorithm are made other than what is required to correctly account for the inclusion of terrain. Previously published FIRETEC results that use five different topographies that share the same centerline profile are compared to simulation results from the modified QUIC-Fire that use the same topographies and fuels. QUIC-Fire results show overall similar behaviors in terms of how the topographies affect fire shapes and trends in spread rates. Due to the terrain-following version of QUIC-URB being unable to generate flow separations at the crest of hills, fire spread rates in these regions across all topographies are over-predicted when compared to FIRETEC. Lateral fire growth shows similar trends with FIRETEC between topographies but does not capture the increase in spread due to a diagonal interface between grassland and forested fuel region of the domain. These results suggest that there are three algorithms within QUIC-Fire that could use improvement: how flame tilt angle is accounted for, the incorporation of non-local drag effects, and the inclusion of the wake-eddy parameterizations that are used in QUIC-URB. Lastly, the third chapter describes a modification to the initial guess used for the calculation of the QUIC-URB mass-conserved wind solution during fire simulations. The modification is aimed at improving fire-fire interactions in QUIC-Fire simulations. The modification consists of using the solution from the previous timestep as the starting point for the calculation of the solution for the next timestep. Fire-fire interactions is greatly improved by the change but a new source of error is introduced. Due to how plumes are modelled in QUIC-Fire the new solution contains errors where gaps in the plume structure are present. However, these errors are mostly limited to the upper atmosphere, where they do not affect fire behavior at the surface, and their magnitude isn’t significant enough to discount the amount of new fire phenomenology now captured in QUIC-Fire with the change.

58 GEOSCIENCES↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
DOE OSTI2023

Reference data, predictors, and probability grids for forest degradation classes in three sites in the Brazilian Amazon

Forest degradation by fires and selective logging is widespread in the Amazon region. We implemented a gradient boosted classification modeling framework to classify intact, logged, and burned forests at three Amazonian sites: Feliz Natal Municipality and Xingu Indigenous Territory in Mato Grosso State, and Saracá-Taquera National Forest in Pará State. We used forest degradation history from Landsat time-series as reference data and textural metrics derived from PlanetScope images as predictors. Textural metrics were computed using the Gray-Level Co-Occurrence Matrix (GLCM) textural technique. Included in the attached zip file are ten files: - a shapefile containing the reference data (fire and selective logging polygons and year of event) for each site; - a multiband tif file containing the 8 GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, Correlation) at the original PlanetScope resolution (3.125m) for each site; - a multiband tif file containing the 72 aggregated GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation aggregated using the mean, first quartile, third quartile, maximum, median, minimum, root mean square, standard deviation, and skewness statistics) at 562m resolution for each site; - a multiband tif file containing the 3 probability grids for either intact, logged, or burned forests at the aggregation resolution (562m) for each site.

54 ENVIRONMENTAL SCIENCES↗