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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A systems approach to a DSMC calculation of a control jet interaction experiment

This study deals with the development of a methodology for numerically simulating the interaction of a reaction control system (RCS) jet with a low-density external flow. A European Space Agency (ESA) experiment was chosen as a test case since it provided experimental data that could validate some of the numerical results. The initial approach was to focus on several subproblems having direct relevance to the full interaction problem. This enabled different numerical methods to be separately investigated and validated for each part of the interaction problem. In this manner, the best methodology for solving the full interaction problem was developed. The subproblems considered in this study included typical RCS nozzle and plume flows, a flat plate at zero incidence, and the flow past the experimental test model without the control jet firing. Once these calculations were completed, a simulation was performed with a control jet operating at a relatively low density. The results from this latter simulation provided qualitative insight into the complex interaction process.

Tartabini, Paul V.↗
NASA NTRS2000

Predicting Fire Susceptibility in the Forests of Amazonia

Although fire is the single greatest threat to the ecological integrity of Amazon forests, our ability to predict the occurrence of Amazon forest fires is rudimentary. Part of the difficulty encountered in making such predictions is the remarkable capacity of Amazon forests to tolerate drought by tapping moisture stored in deep soil. These forests can avoid drought-induced leaf shedding by withdrawing moisture to depths of 8 meters and more. Hence, the absorption of deep soil moisture allows these forests to maintain their leaf canopies following droughts of several months duration, thereby maintaining the deep shade and high relative humidity of the forest interior that prevents these ecosystems from burning. But the drought- and fire-avoidance that is conferred by this deep-rooting phenomenon is not unlimited. During successive years of drought, such as those provoked by El Nino episodes, deep soil moisture can be depleted, and drought-induced leaf shedding begins. The goal of this project was to incorporate this knowledge of Amazon forest fire ecology into a predictive model of forest flammability.

Nepstad, Daniel C.↗
NASA NTRSDate not supplied

Building Capacity to Use Earth Observations for Wildfire Risk Reduction and Resilience Applications: A Summary of NASA’s DEVELOP 2022 Wildfire Projects

NASA’s DEVELOP Program engages individuals and organizations in 10-week feasibility studies to apply NASA Earth observations (EO) to decision makers’ needs. DEVELOP maintains a balance of projects across nine application areas, one of which is Wildfires. DEVELOP's Wildfires application area supports pre-fire (fuel loading, fire risk), during (active fire detection), and post-fire (fire extent, intensity) monitoring and modeling. In 2022 so far, DEVELOP has conducted six projects with Wildfires as a primary or secondary component across a diverse set of study areas. This presentation introduces DEVELOP, its approach to capacity building, key partnerships, and project results highlighting the capabilities of EO for enhanced decision-making regarding wildfires.

Wildfire↗
DOE OSTI2023

Impact of Wildfires on Solar Resource Availability in California in a Changing Climate

Wildfires can emit large amounts of atmospheric particulate matters and influence not only air quality but also availability of photovoltaic (PV) generation due to scattering and absorption of solar radiation. Under anthropogenic changing climate, wildfire activity is projected to increase over western North America due to drier and warmer climate, implying increasing impact on solar resource and larger uncertainty in solar generation especially in regions with faster PV penetration. This study focuses on quantifying the impact of wildfires on aerosol optical depth (AOD) and thus solar resource over California using National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL). This assessment includes historical analysis and estimation of solar resource under wildfire scenarios (2020 wildfire and an enhanced wildfire scenario based on 2020 wildfires). Historical analysis for the period of July-October 2019/2020 (low/high fire activity period) shows that the averaged global horizontal irradiance (GHI) and direct normal irradiance (DNI) are reduced by about 30 and 90 W m-2 (5.6 and 15.3%), respectively, during high fire activity period. To create AOD dataset for enhanced fire scenario, 165% increase in burn area (in around 2050) is selected based on comprehensive literature review, which is further applied to the Fire INventory from NCAR (FINN) fire emission. WRF-Chem model with the enhanced FINN emissions is used to simulate and represent a preserving spatial distribution of burned areas and wildfire-emitted aerosols. Our initial analysis suggests that the"enhanced 2020 wildfire" AOD can increase by a factor of 1.3 - 2, which can significantly reduce solar irradiance and increase uncertainty in generation and reliability of power system in extreme wildfire events under a high solar penetration scenario. Overall, this study provides an estimate of the impacts of wildfires on solar resource to make informed decisions on reserve planning, generation scheduling, and reliability investments.

aerosol optical depth↗
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 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↗
DOE OSTI2025

The Co-Firing of Pine Biomass and Waste Coal in 100 and 600 MW Power Plants: A Sustainable Approach to Reduce GHG Emissions

Climate change is a global issue that has gained much attention recently. Co-firing biomass with coal/waste coal reduces the electricity sector’s GHG emissions sustainably. This study uses commercial software to model waste coal and biomass co-firing in 100 MW and 600 MW power plants. The objective is to assess the effects of fluid types (subcritical and supercritical), plant capacities (100 MW and 600 MW), boiler types (pulverized coal and circulating fluidized bed boilers), biomass and waste coal co-firing ratios (0:100, 20:80, 40:60, 60:40, 80:20, and 100:0), and carbon capture and storage efficiencies (0%, 90%, 95%, and 97%) on performance parameters such as net plant efficiency, heat rate, net plant CO2 and SO2, and particulate matter emissions. The feedstocks selected for this investigation include anthracite waste coal and loblolly pine biomass. As the biomass fraction increases from 0% to 100%, co-fired power plants net efficiency increases by 3–8%. Supercritical plants had a 6% higher net plant efficiency than the subcritical plants. The study found that the biomass’s high heating value decreased the fuel flow rate and reduced plant CO2 emissions by 10–16%. With 100% biomass power plant feed and 90% carbon capture and storage efficiency, CO2 emissions drop by 83% and SO2 and PM emissions drop to zero.

Bhoi, Prakashbhai R. (ORCID:0000000333104888)↗
NASA NTRS2023

Emission Factors for Crop Residue and Prescribed Fires in the Eastern US during FIREX-AQ

Agricultural and prescribed burning activities emit large amounts of trace gases and aerosols on regional to global scales. We present a compilation of emission factors (EFs) and emission ratios (ERs) from the eastern portion of the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign in 2019 in the United States, which sampled burning of crop residues and other prescribed fire fuels. FIREX-AQ provided comprehensive chemical characterization of 53 crop residue and 22 prescribed fires. Crop residues burned at different modified combustion efficiencies (MCE), with corn residue burning at higher MCE than other fuel types. Prescribed fires burned at lower MCE (<0.90) which is typical, while grasslands burned at lower MCE (0.90) than normally observed due to moist, green, growing season fuels. Most non-methane volatile organic compounds (NMVOCs) were significantly anticorrelated with MCE except for ethanol and NMVOCs that were measured with less certainty. We identified 23 species where crop residue fires differed by more than 50% from prescribed fires at the same MCE. Crop residue EFs were greater for species related to agricultural chemical use and fuel composition as well as oxygenated NMVOCs possibly due to the presence of metals such as potassium. Prescribed EFs were greater for monoterpenes (5×). FIREX-AQ crop residue average EFs generally agreed with the previous agricultural fire study in the US but had large disagreements with global compilations. FIREX-AQ observations show the importance of regionally-specific and fuel-specific EFs as first steps to reduce uncertainty in modeling the air quality impacts of fire emissions.

air quality↗
NASA NTRS2020

Southern Colorado Disasters: Using NASA Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Project Summary↗
NASA NTRS2020

Southern Colorado Disasters: Using NASA Earth Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Technical Paper↗
DOE OSTI2020

Absorber Column CFD model validation against PNNL’s device-scale absorber column on the LCFS unit

Absorber column has been widely used for CO 2 capture in the coal-fired power plants. High-fidelity CFD models play an important role in absorber column design and solvents optimization, which help enhance the CO 2 capture efficiency and reduce the operation cost. This report provides a comprehensive description of the development of CFD absorber models at two different levels, the design and implementation of PNNL’s device-scale absorber column experiment, and the methodology to combine the CFD results, experiment data, and Aspen model for a better understanding of the interface area in packed column. A composite model is firstly proposed in the Discrete Element Method (DEM) packing process, which can model the complex geometry of various packing elements. This generates a realistic packing pattern and accurate packing porosity ε and specific area a p compared to the actual values for absorber column used in LCFS. Two level of CFD absorber models were developed, namely the full-size column model (FCM) to simulate the entire packed column with a focus on the wall/entrance effects, and the representative column model (RCM) to simulate a section of column with a focus on the sensitivity study of interface area. A Design of Experiment (DoE) plan was developed to guide the collection of 100 run CFD data and 12 experiment runs. The 100 CFD runs were carried out in the RCM with Pro-Pak packing and cover a wide operation range and solvent properties. Impact of influential factors on the interface area in packed column were investigated in details. The CFD interface area was then combined with the experimental data and Aspen model to infer some information of the effective contact angle in the column. The accuracy and uncertainties in the interface area and contact angle can be quantified.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRSDate not supplied

Vehicle Modeling During the Burning of Thermally Thick Samples in the Saffire V Experiments

In order to design a safer spacecraft in terms of fire prevention, detection and cleanup, the transport of combustion products and heat must be predicted. Large-scale fire data was provided by the Saffire payload within Northrup Grumman’s Cygnus vehicle after a resupply mission to the ISS. Solid materials, including thick thermoplastic samples were ignited to study flame spread and the effect a fire has on a vehicle. Sensors that measured species and temperature were placed within Cygnus. A model using Pyrosim was developed to predict the thick fuel burn events from Saffire V by using sensor data from Cygnus. The heat transfer coefficient between the cabin air and cargo was previously calibrated using the cotton fuel burn event, and provided a good prediction of the temperature response within the vehicle during the two thick fuel experiments. The difference between the Saffire inlet CO 2 sensor, and the forward port standoff CO 2 sensor, which is the closest to the Saffire outlet, was used as an input into the model. This method provided an excellent prediction of the CO 2 response in the other parts of the Cygnus.

fire safety↗
NASA NTRS1992

Smoke and fire characteristics for cerrado and deforestation burns in Brazil - BASE-B experiment

Five test fires were performed during August and September 1990 in the cerrado (savannalike region) in central Brazil (three fires) and tropical moist forest (two fires) in the eastern Amazon. This paper details the gases released, the ratios of the gases to each other and to particulate matter, fuel loads, and the fraction consumed (combustion factors), and the fire behavior associated with biomass consumption. Models are presented for evaluating emission factors for CH4, CO2, CO, H2, and particles less than 2.5 micron diam (PM2.5) as a function of combustion efficiency. The ratio of carbon released as CO2 (combustion efficiency) for the cerrado fires averaged 0.94 and for the deforestation fires it decreased from 0.88 for the flaming phase to less than 0.80 during the smoldering phase of combustion. For tropical ecosystems, emissions of most products of incomplete combustion are projected to be lower than previous estimates for savanna ecosystems and somewhat higher for fires used for deforestation purposes.

Ward, D. E.↗
DOE OSTI2023

Sensitivity of Pyrocumulus Convection to Tree Mortality During the 2020 Creek Fire in California

Abstract This study investigated the sensitivity of pyrocumulonimbus (PyroCb) induced by the California Creek fire of 2020 to the amount and type of surface fuels, within the WRF‐SFIRE modeling system. Satellite data were used to derive fire arrival times to constrain fire progression, and to augment the fuel characterization with better estimates of combustible vegetation accounting for tree mortality. Machine learning was employed to classify standing dead vegetation from aerial imagery, which was then added as a custom fuel class along with the standard Anderson fuel categories. Simulations using this new fuel class produced a larger and more vigorous PyroCb than the control run, however, still under‐predicted the cloud top. Additional augmentation of fuel mass to represent the accumulation of dead vegetation on the forest floor further improved the simulations, demonstrating the efficacy of representing both dead standing and fallen vegetation to produce more realistic PyroCb and smoke simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dissipative Dark Substructure: The Consequences of Atomic Dark Matter on Milky Way Analog Subhalos

Using cosmological hydrodynamical zoom-in simulations, we explore the properties of subhalos in Milky Way analogs that contain a subcomponent of atomic dark matter (ADM). ADM differs from cold dark matter (CDM) due to the presence of self-interactions that lead to energy dissipation, analogous to standard model baryons. This model can arise in dark sectors that are natural and theoretically motivated extensions to the standard model. The simulations used in this work were carried out using GIZMO and utilize the FIRE-2 galaxy formation physics in the standard model baryonic sector. For the parameter points we consider, the ADM gas cools efficiently, allowing it to collapse to the center of subhalos. This increases a subhalo's central density and affects its orbit, with more subhalos surviving small pericentric passages. The subset of subhalos that host satellite galaxies have cuspier density profiles and smaller stellar half-mass radii relative to CDM. The entire population of dwarf galaxies produced in the ADM simulations is more compact than those seen in CDM simulations, unable to reproduce the entire diversity of observed dwarf galaxy structures. Additionally, we also identify a population of highly compact subhalos that consist nearly entirely of ADM and form in the central region of the host, where they can leave distinctive imprints in the baryonic disk. This work presents the first detailed exploration of subhalo properties in a strongly dissipative dark matter scenario, providing intuition for how other regions of ADM parameter space, as well as other dark sector models, would impact galactic-scale observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

QUIC-URB and QUIC-fire extension to complex terrain: Development of a terrain-following coordinate system

Ensemble-based approaches to prescribed fire planning cannot be supported by CFD-based models like FIRETEC and WFDS because they are too computationally expensive and cannot leverage LES approaches like CAWFE and WRF-SFIRE because too coarse of resolution. QUIC-Fire was developed to fill this gap but it cannot currently address complex terrain, typical for instance of the Western United States. In this paper, we describe the extension of the diagnostic wind model QUIC-URB, the wind engine of QUIC-Fire, to a terrain-following coordinate system. In particular, the paper 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 captured the correct phenomenology for the isolated Askervein Hill, with a wind speed up at the top of the hill. We report the model agreed well with measurements on the upwind side of the peak, but overestimated 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 was obtained in 6 s, making the solver appealing for coupled fire–atmosphere simulations. The Socorro Mountain was 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 was addressed in this work). Results highlight that future work should focus on the implementation of parameterizations of wake-eddies, similar to QUIC-URB’s building parameterizations, and on thermodynamic-driven flow.

54 ENVIRONMENTAL SCIENCES↗