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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Film Cooling Performance Prediction for Air and Supercritical CO 2

The current study explores the possibility of cooling the vanes and blades of a direct-fired sCO 2 turbine using film cooling. The operating conditions of a direct-fired sCO 2 cycle and thermophysical properties of the fluid at those conditions can alter the flow field characteristics of the coolant jet and its mixing with the mainstream. Very little information is present in the literature regarding the performance of film cooling geometries employing supercritical CO 2 . The objective of this study is to estimate the resulting film cooling effectiveness while also capturing the effects of the crossflow-to-mainstream velocity ratio on the coolant jet. Herein, a computational fluid dynamic model is used to study the coolant jet exiting a cylindrical hole located on a flat plate, with the coolant fed by an internal channel. Steady-state Reynolds-averaged Navier–Stokes equations were solved along with the (shear-stress transport) SST k–ω model to provide the turbulence closure. The operating conditions for the direct-fired sCO 2 turbine are obtained using an in-house Cooled Turbine Model. Numerical predictions revealed that the crossflow effects and jet lift-off were more pronounced in the case of sCO 2 when compared to air. Spatial distribution of flow field and cooling effectiveness are presented at different operating conditions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Quantifying the reductions in mortality from air-pollution by cancelling new coal power plants

Deep decarbonization paths to the 1.5°C or 2°C temperature stabilization futures require a rapid reduction in conventional coal-fired power plants, but countries are currently building 223 GW of new coal power capacity and plan to build 377 GW more in the next decade. Coal-fired plants are also a major contributor to air pollution related health impacts. Here, we couple an integrated human-earth system model (GCAM) with an air quality model (TM5-FASST) to examine regional health co-benefits from cancelling new coal-fired plants worldwide. Additionally, we find that cancelling all new proposed projects would decrease air pollution related premature mortality between 101,388 - 213,205 deaths (2-5%) in 2030, and 213,414 - 373,054 (5-8%) in 2050, globally, but heavily concentrated in developing Asia. Furthermore, we estimate that strengthening the climate target from 2°C to 1.5°C would avoid 326,351 additional mortalities in 2030, of which 251,011 (75%) are attributable to the incremental coal plant shutdown.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2014

Revised (Mixed-Effects) Estimation for Forest Burning Emissions of Gases and Smoke, Fire/Emission Factor Typology, and Potential Remote Sensing Classification of Types for Ozone and Black-Carbon Simulation

We summarize recent progress (a) in correcting biomass burning emissions factors deduced from airborne sampling of forest fire plumes, (b) in understanding the variability in reactivity of the fresh plumes sampled in ARCTAS (2008), DC3 (2012), and SEAC4RS (2013) airborne missions, and (c) in a consequent search for remotely sensed quantities that help classify forest-fire plumes. Particle properties, chemical speciation, and smoke radiative properties are related and mutually informative, as pictures below suggest (slopes of lines of same color are similar). (a) Mixed-effects (random-effects) statistical modeling provides estimates of both emission factors and a reasonable description of carbon-burned simultaneously. Different fire plumes will have very different contributions to volatile organic carbon reactivity; this may help explain differences of free NOx(both gas- and particle-phase), and also of ozone production, that have been noted for forest-fire plumes in California. Our evaluations check or correct emission factors based on sequential measurements (e.g., the Normalized Ratio Enhancement and similar methods). We stress the dangers of methods relying on emission-ratios to CO. (b) This work confirms and extends many reports of great situational variability in emissions factors. VOCs vary in OH reactivity and NOx-binding. Reasons for variability are not only fuel composition, fuel condition, etc., but are confused somewhat by rapid transformation and mixing of emissions. We use "unmixing" (distinct from mixed-effects) statistics and compare briefly to approaches like neural nets. We focus on one particularly intense fire the notorious Yosemite Rim Fire of 2013. In some samples, NOx activity was not so suppressed by binding into nitrates as in other fires. While our fire-typing is evolving and subject to debate, the carbon-burned delta(CO2+CO) estimates that arise from mixed effects models, free of confusion by background-CO2 variation, should provide a solid base for discussion. (c) We report progress using promising links we find between emissions-related "fire types" and promising features deducible from remote observations of plumes, e.g., single scatter albedo, Angstrom exponent of scattering, Angstrom exponent of absorption, (CO column density)/(aerosol optical depth).

Remote sensing↗
NASA NTRS1996

Microgravity Turbulent Gas-Jet Diffusion Flames

A gas-jet diffusion flame is similar to the flame on a Bunsen burner, where a gaseous fuel (e.g., propane) flows from a nozzle into an oxygen-containing atmosphere (e.g., air). The difference is that a Bunsen burner allows for (partial) premixing of the fuel and the air, whereas a diffusion flame is not premixed and gets its oxygen (principally) by diffusion from the atmosphere around the flame. Simple gas-jet diffusion flames are often used for combustion studies because they embody the mechanisms operating in accidental fires and in practical combustion systems. However, most practical combustion is turbulent (i.e., with random flow vortices), which enhances the fuel/air mixing. These turbulent flames are not well understood because their random and transient nature complicates analysis. Normal gravity studies of turbulence in gas-jet diffusion flames can be impeded by buoyancy-induced instabilities. These gravitycaused instabilities, which are evident in the flickering of a candle flame in normal gravity, interfere with the study of turbulent gas-jet diffusion flames. By conducting experiments in microgravity, where buoyant instabilities are avoided, we at the NASA Lewis Research Center hope to improve our understanding of turbulent combustion. Ultimately, this could lead to improvements in combustor design, yielding higher efficiency and lower pollutant emissions. Gas-jet diffusion flames are often researched as model flames, because they embody mechanisms operating in both accidental fires and practical combustion systems (see the first figure). In normal gravity laboratory research, buoyant air flows, which are often negligible in practical situations, dominate the heat and mass transfer processes. Microgravity research studies, however, are not constrained by buoyant air flows, and new, unique information on the behavior of gas-jet diffusion flames has been obtained.

Source record↗
DOE OSTI2021

A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires

In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.

O'Neill, Susan↗
DOE OSTI2021

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Cirrus cloud spectra and layers observed during the FIRE and GASP projects

A general characterization is developed for cirrus clouds in terms of their spectra, shapes, optical thicknesses, and radiative properties for use in numerical models. Data sets from the Global Atmospheric Sampling Project (GASP) of the upper troposphere and the First ISCCP Regional Experiment (FIRE) are combined and analyzed to study general traits of cirrus clouds. A definition is given for 2D turbulence, and the GASP and FIRE data sets are examined with respect to cirrus layers and entrainment and to dominant turbulent scales. The approach employs conditional sampling in cloudy and clear air, power-spectral analysis, and mixing-line-type diagrams. Evidence is given for a well mixed cloud deck and for the tendency of cirrus to be formed in multilayer structures. The results are of use in mesoscale and global circulation models which predict cirrus, in small-scale cirrus modeling, and in studying the role of gravity waves in the horizontal structure of upper tropospheric clouds.

Flatau, Piotr J.↗
NASA NTRS2021

Colorado Front Range Disasters: Understanding the Impact of Forest Management on the Cameron Peak and CalWood Fire

Along the Colorado Front Range, forest management has gained significant attention due to uncharacteristically large fires that burned late in 2020. The Cameron Peak Fire (largest in Colorado recorded history) and the CalWood Fire collectively burned an estimated 219,019 acres from August through December of 2020. Project partners at the Coalition for the Poudre River Watershed, Colorado State Forest Service, Ben Delatour Scout Ranch, The Nature Conservancy, Colorado Forest Restoration Institute, and Colorado State University were interested in understanding the effectiveness of previous forest treatments in reducing burn severity within the Cameron Peak Fire and the CalWood Fire. We first collated a forest treatment dataset from pre-existing datasets by reclassifying over 29,000 treatments, which occurred across the Northern Colorado Front Range between 1970-2020. Secondly, we mapped three burn severity indices using Landsat 8 OLI and Sentinel-2 MSI Earth observations and compared them to soil burn severity field data. Thirdly, a total of 35 topographic, disturbance, forest structure, and treatment predictor variables were generated across the fires. Finally, we assessed relationships between these predictor variables and burn severity using the random forest algorithm. Model results indicate that the primary drivers of burn severity were elevation and distance to treatment edge for the Cameron Peak Fire and fire area and forest canopy cover for the CalWood Fire. Further analysis of these variables paired with field data is necessary to understand the relationship between burn severity and treatments to guide future restoration efforts, improve forest resiliency, and mitigate fire risks.

Neal Swayze↗
DOE OSTI2017

Seed origin and warming constrain lodgepole pine recruitment, slowing the pace of population range shifts

Understanding how climate warming will affect the demographic rates of different ecotypes is critical to predicting shifts in species distributions. In this study, we present results from a common garden, climate change experiment in which we measured seedling recruitment of lodgepole pine, a widespread North American conifer that is also planted globally. Seeds from a low-elevation provenance had more than three-fold greater recruitment to their third year than seeds from a high-elevation provenance across sites within and above its native elevation range and across climate manipulations. Heating halved recruitment to the third year of both low- and high-elevation seed sources across the elevation gradient, while watering more than doubled recruitment, alleviating some of the negative effects of heating. Demographic models based on recruitment data from the climate manipulations and long-term observations of adult populations revealed that heating could effectively halt modeled upslope range expansion except when combined with watering. Simulating fire and rapid postfire forest recovery at lower elevations accelerated lodgepole pine expansion into the alpine, but did not alter final abundance rankings among climate scenarios. Regardless of climate scenario, greater recruitment of low-elevation seeds compensated for longer dispersal distances to treeline, assuming colonization was allowed to proceed over multiple centuries. In conclusion, our results show that ecotypes from lower elevations within a species’ range could enhance recruitment and facilitate upslope range shifts with climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Satellite Analyses of Cirrus Cloud Properties During the FIRE Phase 2 Cirrus Intensive Field Observations over Kansas

The First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) Phase II Intensive Field Observations (IFO) were taken over southeastern Kansas between November 13 and December 7,1991, to determine cirrus cloud properties. The observations include in situ microphysical data; surface, aircraft, and satellite remote sensing; and measurements of divergence over meso- and smaller-scale areas using wind profilers. Satellite remote sensing of cloud characteristics is an essential aspect for understanding and predicting the role of clouds in climate variations. The objectives of the satellite cloud analysis during FIRE are to validate cloud property retrievals, develop advanced methods for extracting cloud information from satellite-measured radiances, and provide multiscale cloud data for cloud process studies and for verification of cloud generation models. This paper presents the initial results of cloud property analyses during FIRE-II using Geostationary Operational Environmental Satellite (GOES) data and NOAA Advanced Very High Resolution Radiometer (AVHRR) radiances.

Minnis, Patrick↗
NASA NTRS1996

Statistical Models of Landscape Pattern and the Effects of Coarse Resolution of Satellite Imagery on Estimation of Area

Analysis of classified satellite imagery was conducted to characterize errors in estimates of area based on coarse resolution satellite imagery which are due to distortions in sizes of small fragments, and to explore the feasibility of correcting for these errors using a statistical modeling approach. Sizes of bodies of open water on ERS-1 SAR and fire scars on Landsat MSS imagery were measured. Statistical analysis of the smaller scars and ponds as observed with this imagery of relatively fine resolution demonstrated that the distribution of the sizes could be modeled by either of two types of statistical distributions - a power distribution related to fractal processes or a simple exponential distribution. Comparison of the distribution of small bum scars as observed with Landsat to the distribution observed with AVHRR showed distortions due to the coarse spatial resolution of AVHRR caused a net overestimation of bum area. This bias was primarily caused by detection in 2 or 3 AVHRR pixels of bums whose true size was on the order of an AVHRR pixel.

Hlavka, Christine A.↗
DOE OSTI2021

BayoTech Risk and Modeling Support

This white paper describes the work performed by Sandia National Laboratories in the New Mexico Small Business Agreement with BayoTech. BayoTech is a hydrogen generation and distribution company that is located in Albuquerque, NM. Their goal is to distribute hydrogen via their hydrogen systems which utilize the core design that was developed by Sandia. However, because the hydrogen economy is in its nascency, the safety and operation of the generating systems require independent validation. Additionally, in their pursuit of permitting at various locations around the nation, they require fire protection engineering support in discussions with local fire marshals and neighboring industrial entities. Sandia National Laboratories has subject matter expertise in hydrogen risk modeling of consequence (overpressure and dispersion) as well as fire protection engineering. Throughout this project, Sandia has worked with BayoTech to provide our expertise in these subject areas to facilitate the market entry of their hydrogen generation project to address the dire need for decarbonization due to climate change. The general approach of the support by Sandia is outlined in the main body, while the location specific evaluation for the Port of Stockton is contained in Appendix A.

08 HYDROGEN↗
NASA NTRS1977

Research in the use of electrets in measuring effluents from rocket exhaust of the space shuttle (6.4 percent scaled model) and Viking 1 launch

Electrets used to detect the chemical composition of rocket exhaust effluents were investigated. The effectiveness of electrets was assessed while comparisons were made with hydrogen chloride measuring devices from chamber and field tests, and computed results from a multilayer diffusion model. The experimental data used were obtained from 18 static test firings, chamber tests, and the Viking 1 launch to Mars. Results show that electrets have multipollutant measuring capabilities, simplicity of deployment, and speed of assessment. The electrets compared favorably with other hydrogen chloride measuring devices. The summary of the measured data from the electrets and the hydrogen chloride detectors was within the upper and lower bounds of the computed hydrogen chloride concentrations from the multilayer diffusion model.

Susko, M.↗
DOE OSTI2017

Methodology for assessing the safety of Hydrogen Systems: HyRAM 1.1 technical reference manual

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a stan- dardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. De- partment of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.1. HyRAM 1.1 includes generic probabilities for hydrogen equipment fail- ures, probabilistic models for the impact of heat flux on humans and structures, and computa- tionally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.1 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Methodology for assessing the safety of Hydrogen Systems: HyRAM 1.0 technical reference manual

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a standardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. Department of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.0. HyRAM 1.0 includes generic probabilities for hydrogen equipment failures, probabilistic models for the impact of heat flux on humans and structures, and computationally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.0 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.

08 HYDROGEN↗
DOE OSTI2014

Consequence Analysis for Used Fuel Extended Storage

Early identification and evaluation of security issues related to the extended storage of used nuclear fuel is critical. A breach in a dry fuel storage container has the possibility of external gas from the atmosphere interacting with the used fuel rods at high temperatures, resulting in rapid oxidation and possibly the ignition of a zirconium fire. In support of this idea, the current work aims to develop a computational model of heat transfer and fluid flow in and through a breached dry fuel storage cask to determine if the resulting flow conditions are likely to result in a fire.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1976

Simulation of fluidized bed combustors. I - Combustion efficiency and temperature profile

A chemical engineering analysis is made of fluidized-bed combustor (FBC) performance, with FBC models developed to aid estimation of combustion efficiency and axial temperature profiles. The FBC is intended for combustion of pulverized coal and a pressurized FBC version is intended for firing gas turbines by burning coal. Transport phenomena are analyzed at length: circulation, mixing models, drifting, bubble wake lift, heat transfer, division of the FB reactor into idealized mixing cells. Some disadvantages of a coal FBC are pointed out: erosion of immersed heat-transfer tubing, complex feed systems, carryover of unburned coal particles, high particulate emission in off-streams. The low-temperature bed (800-950 C) contains limestone, and flue-gas-entrained SO2 and NOx can be kept within acceptable limits.

Horio, M.↗
DOE OSTI2016

Wildland Fire Induced Heating of Dome 375 Perma-Con®

AET-1 was tasked by ADEM with determining the temperature rise in the drum contents of drums stored in the Dome 375 Perma-Con® at TA-54 given a wildland fire. The wildland fire causes radiative and convective heating on the Perma-Con® exterior. The wildland fire time histories for the radiative and convective heating environment were provided to AET-1 by EES-16. If the calculated temperature rise results in a drum content temperature over 40 °C, then ADEM desires a design solution to ensure the peak temperature remains below 40 °C. An axi-symmetric FE simulation was completed to determine the peak temperature of the contents of a drum stored in the Dome 375 Perma-Con® during a wildland fire event. Three wildland fire time histories for the radiative and convective heat transfer were provided by EES-16 and were inputs for the FE simulation. The maximum drum content temperature reached was found to be 110 °C while using inputs from the SiteG_2ms_4ign_wind_from_west.xlsx time history input and not including the SWB in the model. Including the SWB in the results in a peak drum content temperature of 61 °C for the SiteG_2ms_4ign_wind_from_west.xlsx inputs. EES-16 decided that by using fuel mitigation efforts, such as mowing the grass and shrubs near the Perma-Con® they could reduce the shrub/grass fuel loading near the Perma-Con® from 1.46 kg/m 2 to 0.146 kg/m 2 and by using a less conservative fuel loading for the debris field inside the Dome 375 perimeter, reducing it from 0.58 kg/m2 to 0.058 kg/m 2 in their model. They also greatly increased the resolution of their radiation model and increased the accuracy of their model’s required convergence value. Using this refined input the maximum drum content temperature was found to be 28 °C with no SWB present in the model. Additionally, this refined input model was modified to include worst case emissivity values for the concrete, drum and Perma-Con® interior, along with adding a 91 second long residual radiative heat flux of 2,000 W/m2 to the end of the refined wildland fire input. For this case the maximum drum content temperature was found to be 32 °C. For Rev. 2 of this calculation and additional simulation was run that included a cable fire heat flux on the exterior of the Perma-Con® that was calculated by FP-DO. Including the cable fire heat flux in the model without the SWB resulted in a peak drum content temperature over time of 43 °C. Including the SWB in the simulation with the cable fire heat flux resulted in a peak drum content temperature over time of 35 °C.

42 ENGINEERING↗