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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effect of thickness on degradation of austenitic 347H steel by direct-fired supercritical CO 2 power cycle environment

Austenitic 347H steel of two thicknesses (2.54 and 0.6 mm) was exposed to a simulated direct-fired supercritical CO 2 (sCO 2 ) power cycle environment at 650°C and 1 atm for 1000 h then tensile stressed at room temperature to study the deformation behavior. The thicker 347H formed a protective chromia scale over most of the surface, which minimized carburization of the underlying steel and resulted in no change in mechanical performance. In stark contrast, the thinner 347H formed non-protective Fe-rich oxides over the entire surface, resulting in extensive carburization of the underlying steel. Finally, this led to increased strength but significantly reduced ductility, resulting in partially brittle failure

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stellar feedback-regulated black hole growth: driving factors from nuclear to halo scales

ABSTRACT Several recent simulations of galaxy formation predict two main phases of supermassive black hole (BH) accretion: an early, highly intermittent phase (during which BHs are undermassive relative to local scaling relations), followed by a phase of accelerated growth. We investigate physical factors that drive the transition in BH accretion in cosmological zoom-in simulations from the FIRE project, ranging from dwarf galaxies to galaxies sufficiently massive to host luminous quasars. The simulations model multichannel stellar feedback, but neglect AGN feedback. We show that multiple physical properties, including halo mass, galaxy stellar mass, and depth of the central gravitational potential correlate with accelerated BH fuelling: constant thresholds in these properties are typically crossed within ∼0.1 Hubble time of accelerated BH fuelling. Black hole masses increase sharply when the stellar surface density in the inner 1 kpc crosses a threshold $\Sigma^\star _{1\,\rm kpc}\approx 10^{9.5} \, {\rm M_{\odot }}\,{\rm kpc}^{-2}$, a characteristic value above which gravity prevents stellar feedback from ejecting gas, and similar to the value above which galaxies are observed to quench. We further show that accelerated BH growth correlates with the emergence of long-lived thin gas discs, as well as with virialization of the inner circumgalactic medium. The halo mass Mhalo ∼ 1012 M⊙ and stellar mass M* ∼ 1010.5 M⊙ at which BH growth accelerates correspond to ∼L⋆ galaxies. The fact that stellar feedback becomes inefficient at ejecting gas from the nucleus above this mass scale may play an important role in explaining why AGN feedback appears to be most important in galaxies above L⋆.

Astronomy & Astrophysics↗
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 OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Performance of the Spacecraft Propulsion Research Facility During Altitude Firing Tests of the Delta 3 Upper Stage

The Spacecraft Propulsion Research Facility at the NASA Lewis Research Center's Plum Brook Station was reactivated in order to conduct flight simulation ground tests of the Delta 3 cryogenic upper stage. The tests were a cooperative effort between The Boeing Company, Pratt and Whitney, and NASA. They included demonstration of tanking and detanking of liquid hydrogen, liquid oxygen and helium pressurant gas as well as 12 engine firings simulating first, second, and third burns at altitude conditions. A key to the success of these tests was the performance of the primary facility systems and their interfaces with the vehicle. These systems included the structural support of the vehicle, propellant supplies, data acquisition, facility control systems, and the altitude exhaust system. While the facility connections to the vehicle umbilical panel simulated the performance of the launch pad systems, additional purge and electrical connections were also required which were unique to ground testing of the vehicle. The altitude exhaust system permitted an approximate simulation of the boost-phase pressure profile by rapidly pumping the test chamber from 13 psia to 0.5 psia as well as maintaining altitude conditions during extended steady-state firings. The performance of the steam driven ejector exhaust system has been correlated with variations in cooling water temperature during these tests. This correlation and comparisons to limited data available from Centaur tests conducted in the facility from 1969-1971 provided insight into optimizing the operation of the exhaust system for future tests. Overall, the facility proved to be robust and flexible for vehicle space simulation engine firings and enabled all test objectives to be successfully completed within the planned schedule.

Meyer, Michael L.↗
NASA NTRS1994

MODIS airborne simulator visible and near-infrared calibration, 1991 FIRE-Cirrus field experiment. Calibration version: FIRE King 1.1

Calibration of the visible and near-infrared channels of the MODIS Airborne Simulator (MAS) is derived from observations of a calibrated light source. For the 1991 FIRE-Cirrus field experiment, the calibrated light source was the NASA Goddard 48-inch integrating hemisphere. Laboratory tests during the FIRE Cirrus field experiment were conducted to calibrate the hemisphere and from the hemisphere to the MAS. The purpose of this report is to summarize the FIRE-Cirrus hemisphere calibration, and then describe how the MAS was calibrated from observations of the hemisphere data. All MAS calibration measurements are presented, and determination of the MAS calibration coefficients (raw counts to radiance conversion) is discussed. Thermal sensitivity of the MAS visible and near-infrared calibration is also discussed. Typically, the MAS in-flight is 30 to 60 degrees C colder than the room temperature laboratory calibration. Results from in-flight temperature measurements and tests of the MAS in a cold chamber are given, and from these, equations are derived to adjust the MAS in-flight data to what the value would be at laboratory conditions. For FIRE-Cirrus data, only channels 3 through 6 were found to be temperature sensitive. The final section of this report describes comparisons to an independent MAS (room temperature) calibration by Ames personnel using their 30-inch integrating sphere.

Arnold, G. Thomas↗
DOE OSTI2022

Machine learning-based observation-constrained projections reveal elevated global socioeconomic risks from wildfire

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impact of grassland fire dynamics on particulate emission factors

Understanding particulate emission factors in grassland fires is critical for improving air quality assessments and refining emissions inventories. This study explores the dynamic and heterogeneous nature of fire processes and their influence on particulate emissions. Traditional emission factor inventories often rely on static values, overlooking the spatially and temporally variable dynamics of fire behavior. Through field experiments conducted at Konza Prairie Biological Station measuring emissions with uncrewed aerial vehicles, and computational modeling using HIGRAD/FIRETEC, this research highlights the dependency of emission production on localized fire dynamics, including ignition patterns, fuel properties, and atmospheric conditions. The results demonstrate a need for fire dynamic considerations to accurately capture emissions variability.

09 BIOMASS FUELS↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Heating effects on pyrogenic organic matter mass loss and temperature in a sand matrix in a lab setting from 2022

This dataset contains data associated with the preprint “A laboratory method for simulating the effects of subsequent fires on pyrogenic organic matter at different exposure depths in a sand matrix” (https://doi.org/10.1101/2024.07.30.605814), which has been accepted for publication in the International Journal of Wildland Fire. The dataset was generated to investigate the effects of subsequent fires on preexisting pyrogenic organic matter (PyOM) at different soil depths. Specifically, we designed a laboratory method to simulate soil heating from above, applying realistic heat flux profiles (High, Low, Control) derived from jack pine (Pinus banksiana Lamb.) log burns. Using a cone calorimeter, these profiles were applied to buried jack pine PyOM (produced at 350°C) in a sand matrix pyrocosm to examine PyOM mass loss at different depths (surface, 1 cm, 5 cm). Each treatment (e.g., High heat flux at 1 cm depth) was applied once to five replicates in a pyrocosm.The dataset includes mass loss fraction of PyOM and time-series temperature profiles (measured with type K thermocouples). Additionally, calibration datasets for high and low heat flux profiles are provided, including programmed heat flux profile data and three sets of calibration results. All datasets are in .csv format and were analyzed in R using code found at https://github.com/MengmengLuo/Reburning-pyrogenic-organic-matter-A-laboratory-method-for-dosing-dynamic-heat-fluxes-from-above. This dataset supports research on PyOM transformations during subsequent fires, and methodological advancements in simulating soil heating. It can be used to explore fire-induced PyOM alterations across different fire intensities and soil depths.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

The small motor plume/material impingement test program at MSFC utilizing nine different propellant formulations (April and May 1974), section 2

The relative effects of several candidate SSRM propellant formulations and their plume impingement effects on HRSI and RCC materials were evaluated. Nine solid propellant formulations were tested. The selected propellant matrix allowed an evaluation of propellants with and without metal additives, with and without burning rate catalyst, and low (approximately 1927 C) and high (approximately 2649 C) combustion temperatures. Motors were fired at a simulated SRB staging altitude of 3.96 km (130,000 ft) (nominal). The altitude pressure was predicted to drop approximately 0.6 km (20,000 ft) during a motor firing. All motors were loaded with 1.8 to 2.3 kg (4 to 5 lb) of propellant and burned for approximately 2 s.

Source record↗
DOE OSTI2023

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Regolith Simulant Preparation and Geotechnical Characterization for Plume Surface Interaction Testing

Descent engine plumes interact with the lunar surface and accelerate regolith particles to potentially high velocities. These ejecta create risks to surface assets that have yet to be fully assessed. To better understand these risks, plume surface interactions can be simulated on the ground by firing a test engine plume into a bin of lunar regolith simulant under vacuum conditions. The dynamics of the resultant ejecta can then be recorded. In this technical memorandum we discuss the processes used in preparing a 150 kg bin of lunar regolith simulant for plume surface interaction ground tests under vacuum conditions for the NASA STMD Plume Surface Interaction project. We present our approach to mitigating regolith simulant eruptions during pump-down, the methods used to fill and reset the regolith simulant bin for each test, and the techniques used to characterize the consistency of regolith simulant geotechnical properties before each new firing. The challenges of preparing a regolith simulant test bin below an ambient pressure of one atmosphere, particularly on the large scale, could largely be overcome with a system that could fill the test bin with simulant inside the chamber and under vacuum conditions.

lunar regolith↗
DOE OSTI2021

High-Temperature Oxidation of Steels in Direct-Fired CO 2 Power Cycle Environments

We report future direct-fired supercritical CO 2 power cycles require steels resistant to oxidation/corrosion in high-temperature CO 2 environments containing various impurities. Herein we studied the oxidation behavior of 14 candidate steels in a simulated direct-fired CO 2 power cycle environment consisting of 95% CO 2 , 4% H 2 O, 1% O 2 with/without 0.1% SO 2 at 1 atm and 550 °C, 600 °C, 650 °C for up to 2500 hours. Steels with ≥11.5 wt% Cr exhibited at least partial coverage by Cr-rich oxide scales leading to a significant decrease in the oxidation rates in both gases. While SO 2 had little effect on low-Cr steels that formed Fe-rich oxides, it generally worsened performance of high-Cr (> 11.5 wt%) steels by hindering the establishment of a protective Cr-rich oxide. This effect was most pronounced at the lowest temperature of 550 °C, which was attributed to strong preferential adsorption of sulfur-containing species within the oxide at relatively low temperatures.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Effect of thruster pulse length on thruster-exhaust damage of S13G white thermal control coatings

Rocket exhaust products which strike thermal control surfaces cause changes in solar absorptance (Alpha Sub s) and thermal emittance (Epsilon) of these surfaces. A study was made of the effect of rocket pulse duration on exhaust damage to S13G white coatings. Two pulse lengths were used - 14 msec and 50 msec. An MMH/N204 bipropellant 5-lb thrust rocket was fired into a simulated space environment with a vacuum of 0.00001 torr, a liquid helium temperature enclosure, and solar radiation. The changes in solar absorptance and thermal emittance of S13G white coatings due to rocket exhaust were made in-situ for total firing times of 58 seconds with 14 msec pulses and 223.7 sec with 50 msec pulses. The solar absorptance of S13G increased 25 percent due to 223.7 sec of exposure to 50 msec pulses and the thermal emittance was unaffected. The ratio of Alpha Sub s/Epsilon therefore increased by 25 percent. The short 14 msec pulse exhaust exposure caused between 40 and 70 percent increase in solar absorptance and a decrease of between 13 and 18 percent in thermal emittance. The corresponding increase in Alpha Sub s/Epsilon ratio was between 80 and 100 percent. Ultraviolet radiation was present in the short pulse test and may have contributed to the large damage of that test.

Sommers, R. D.↗