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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Verification of Real-Time WSA-ENLIL+Cone Simulations of CME Arrival-Time at the CCMC from 2010 to 2016

The Wang-Sheeley-Arge (WSA)-ENLIL+Cone model is used extensively in space weather operations world-wide to model coronal mass ejection (CME) propagation. As such, it is important to assess its performance. We present validation results of the WSA-ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time by the CCMC space weather team. CCMC uses the WSA-ENLIL+Cone model to predict CME arrivals at NASA missions throughout the inner heliosphere. In this work we compare model predicted CME arrival-times to in situ interplanetary coronal mass ejection leading edge measurements at Solar TErrestrial RElations Observatory-Ahead (STEREO-A), Solar TErrestrial RElations Observatory-Behind (STEREO-B), and Earth (Wind and ACE) for simulations completed between March 2010 and December 2016 (over 1,800 CMEs). We report hit, miss, false alarm, and correct rejection statistics for all three locations. For all predicted CME arrivals, the hit rate is 0.5, and the false alarm rate is 0.1. For the 273 events where the CME was predicted to arrive at Earth, STEREO-A, or STEREO-B, and was actually observed (hit event), the mean absolute arrival-time prediction error was 10.4 +/- 0.9 h, with a tendency to early prediction error of -4.0 h. We show the dependence of the arrival-time error on CME input parameters. We also explore the impact of the multi-spacecraft observations used to initialize the model CME inputs by comparing model verification results before and after the STEREO-B communication loss (since September 2014) and STEREO-A sidelobe operations (August 2014- December 2015). There is an increase of 1.7 h in the CME arrival time error during single, or limited two-viewpoint periods, compared to the three-spacecraft viewpoint period. This trend would apply to a future space weather mission at L5 or L4 as another coronagraph viewpoint to reduce CME arrival time errors compared to a single L1 viewpoint.

MHD / modeling / validation / forecasting / corona↗
DOE OSTI2024

Effects of stratification on overshooting and waves atop the convective core of M ⊙ main-sequence stars

As a massive star evolves along the main sequence, its core contracts, leaving behind a stable stratification in helium. We simulate two-dimensional convection in the core at three different stages of evolution of a $5\,\mathrm{ M}_{\odot }$ star, with three different stratifications in helium atop the core. We study the propagation of internal gravity waves in the stably stratified envelope, along with the overshooting length of convective plumes above the convective boundary. We find that the stratification in helium in evolved stars hinders radial motions and effectively shields the radiative envelope against plume penetration. This prevents convective overshooting from being an efficient mixing process in the radiative envelope. In addition, internal gravity waves are less excited in evolved models compared to the zero-age-main-sequence model, and are also more damped in the stratified region above the core. As a result, the wave power is several orders of magnitude lower in mid- and terminal-main-sequence models compared to zero-age-main-sequence stars.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2006

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Viken, Jeff↗
DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis

The leaf economic spectrum is a widely-studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of PFTs. We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (SLA, N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (R_d, V_{c,max}, J_max) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, these covariance were an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

biodiversity, ecological modeling, ecology, leaf b↗
DOE OSTI2025

Molecular Dynamics Simulations of Supercritical Carbon Dioxide and Water using TraPPE and SWM4-NDP Force Fields

The increased levels of carbon dioxide (CO 2 ) emissions due to the combustion of fossil fuels and the consequential impact on global climate change have made CO 2 capture, storage, and utilization a significant area of focus for current research. In most electrochemical CO 2 applications, water is used as a proton donor due to its high availability and mobility and use as a polar solvent. Additionally, supercritical CO 2 is a promising avenue for electrochemical applications due to its unique chemical and physical properties. Consequently, understanding the interactions between water and supercritical CO 2 is of great importance for future electrochemical applications. Molecular dynamics (MD) simulation is a powerful tool that enables atomistic-resolution dynamics of molecular systems, which can complement and guide future experimental investigations. This study employed atomistic MD to study the cosolubilities, codiffusivities, and structure of supercritical CO 2 and water systems, with a polarizable water model (SWM4-NDP) and a nonpolarizable CO 2 model (TraPPE). Additionally, ab initio MD simulations were used to better understand how atomistic polarizable/nonpolarizable models compare to explicit modeling of electron densities. The polarizable water model exhibited substantial improvement in water-associated properties. In conclusion, we anticipate the development of a compatible polarizable CO 2 model to yield similar improvement, providing a pathway for realizing novel high-pressure electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

36 MATERIALS SCIENCE↗
DOE OSTI2023

IM3 GO WEST Parameter Search Dataset

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It is developed to address weather and water dynamics, and associated vulnerabilities in this bulk power system. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. Therefore, GO WEST can help researchers to strike a balance between model fidelity (i.e. accuracy) and computational complexity (i.e. runtime). This dataset includes model inputs and outputs from 600 model versions for each 2019, 2020, and 2021. The folder naming convention is as follows: Exp{Number of Nodes}_{Mathematical Formulation}_{Transmission Line Limit Scaling Factor in MW}_{Hurdle Rate Scaling Factor in %}_{Year}. Linear programming is designated with "simple" label whereas mixed-integer linear programming is designated with "coal" label. For example, "Exp100_simple_1000_50_2019" folder contains inputs and outputs from 2019 model version with 100 nodes, linear programming, +1000 MW transmission line limit scaling factor, and +50% hurdle rate scaling factor. GO WEST GitHub repository hosts all raw datasets, processing scripts, and model scripts. Please refer to the README file for a detailed description of the included files.

Economics↗
DOE OSTI2024

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗
DOE OSTI2025

IM3 Projected U.S. Western Interconnection Grid Stress Dataset

This dataset provides projected grid stress and reliability results (including all model inputs and outputs from GO WEST and TEP) for Integrated Multisector, Multiscale Modeling (IM3) Phase 2 simulations across eight different scenarios for the U.S. Western Interconnection through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States from a set of Thermodynamic Global Warming (TGW) simulations. These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 GO WEST is an open-source power grid modeling framework for the U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BAs) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of the U.S. Western Interconnection (ACTIVSg10k). Users can select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on the GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within the GO WEST framework. The TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. In order to use TEP model, users need to create scenarios with the GO WEST framework. Please refer to README file for a detailed description of the dataset including individual files and references.

Capacity Expansion Model↗
NASA NTRS1982

Further considerations on modeling the sea breeze with a mixed-layer model

Mixed-layer models have been used to simulate low-level flows under a variety of situations, including flow over complex terrain and in the vicinity of coastal zones. The advantage of mixed-layer models compared to multilevel models is their simplicity and minimal computational requirements. A disadvantage is that the atmosphere above the mixed layer is not modeled explicitly and approximations pertaining to this layer become necessary. This paper examines five approximations for treating this upper layer for a simple sea-breeze circulation. Approximating the flow immediately above the mixed-layer height h by the mixed-layer velocity and using this velocity to advect potential temperature above h gives a better simulation of the sea breeze than the approximation used by Anthes et al. (1980), which neglected horizontal advection at this level.

Anthes, R. A.↗
NASA NTRS1996

Spectra of High-Ionization Seyfert 1 Galaxies: Implications for the Narrow-Line Region

We present line profiles and profile parameters for the Narrow-Line Regions (NLRs) of six Seyfert 1 galaxies with high-ionization lines: MCG 8-11-11, Mrk 79, Mrk 704, Mrk 841, NGC 4151, and NGC 5548. The sample was chosen primarily with the goal of obtaining high-quality [Fe VII] lambda6087 and, when possible, [Fe X] lambda6374 profiles to determine if these lines are more likely formed in a physically distinct 'coronal line region' or are formed throughout the NLR along with lines of lower critical density (n(sub cr)) and/or Ionization Potential (IP). We discuss correlations of velocity shift and width with n(sub cr) and IP. In some objects, lines of high IP and/or n(sub cr) are systematically broader than those of low IP/n(sub cr). Of particular interest, however, are objects that show no correlations of line width with either IP or n(sub cr). In these objects, lines of high and low IP/n(sub cr), are remarkably similar, which is difficult to reconcile with the classical picture of the NLR, in which lines of high and low IP/n(sub cr) are formed in physically distinct regions. We argue for similar spatial extents for the flux in lines with similar profiles. Here, as well as in a modeling-oriented companion paper, we develop further an idea suggested by Moore & Cohen that objects that do and do not show line width correlations with IP/n(sub cr) can both be explained in terms of a single NLR model with only a small difference in the cloud column density distinguishing the two types of object. Overall, our objects do not show correlations between the Full Width at Half-Maximum (FWHM) and IP and/or n(sub cr). The width must be defined by a parameter that is sensitive to extended profile wings in order for the correlations to result. We present models in which FWHM correlations with IP and/or n(sub cr) result only after simulating the lower spectral resolution used in previous observational studies. The models that simulate the higher spectral resolution of our observational study produce line width correlations only if the width is defined by a parameter that is more sensitive to extended profile wings than is the FWHM. Our sample of six objects is in effect augmented by incorporating the larger sample (16 objects) of Veilleux into some of our discussion. This paper focuses on new interpretations of NLR emission-line spectra and line profiles that stem directly from the observations. Paper 2 focuses on modeling and complements this paper by illustrating explicitly the effects that spatial variations in electron density, ionization parameter, and column density have on model profiles. By comparing model profiles with the observed profiles presented here, as well as with those presented by Veilleux, Paper 2 yields insight into how the electron density, ionization parameter, and column density likely vary throughout the NLR.

Moore, David↗
NASA NTRS1998

Measurements of the Downward Longwave Radiation Spectrum over the Antarctic Plateau and Comparisons with a Line-by-Line Radiative Transfer Model for Clear Skies

A 1-year field program was conducted at South Pole Station in 1992 to measure the downward infrared radiance spectrum at a resolution of 1/ cm over the spectral range 550-1667 cm-'. The atmosphere over the Antarctic Plateau is the coldest and driest on Earth, where in winter, surface temperatures average about -60 C, the total column water vapor is as low as 300 pm of precipitable water, and the clear-sky downward longwave flux is usually less than 80 W/sq m. Three clear-sky test cases are selected, one each for summer, winter, and spring, for which high- quality radiance data are available.as well as ancillary data to construct model atmospheres from radiosondes, ozonesondes, and other measurements. The model atmospheres are used in conjunction with the line-by-line radiative transfer model (LBLRTM) to compare model calculations with the spectral radiance measurements. The high-resolution calculations of LBLRTM (approx. = 0.001/ cm) are matched to the lower-resolution measurements (1/ cm) by adjusting their spectral resolution and by applying a correction for the finite field of view of the interferometer. In summer the uncertainties in temperature and water vapor profiles dominate the radiance error in the LBLRTM calculations. In winter the uncertainty in viewing zenith angle becomes important as well as the choice of atmospheric levels in the strong near-surface temperature inversion. The spectral radiance calculated for each of the three test cases generally agrees with that measured, to within twice the total estimated radiance error, thus validating LBLRTM to this level of accuracy for Antarctic conditions. However, the discrepancy exceeds twice the estimated error in the gaps between spectral lines in the region 1250-1500/ cm, where emission is dominated by the foreign-broadened water vapor continuum.

Walden, Von P.↗
NASA NTRS2011

Prominences: The Key to Understanding Solar Activity

Prominences are spectacular manifestations of both quiescent and eruptive solar activity. The largest examples can be seen with the naked eye during eclipses, making prominences among the first solar features to be described and catalogued. Steady improvements in temporal and spatial resolution from both ground- and space-based instruments have led us to recognize how complex and dynamic these majestic structures really are. Their distinguishing characteristics - cool knots and threads suspended in the hot corona, alignment along inversion lines in the photospheric magnetic field within highly sheared filament channels, and a tendency to disappear through eruption - offer vital clues as to their origin and dynamic evolution. Interpreting these clues has proven to be contentious, however, leading to fundamentally different models that address the basic questions: What is the magnetic structure supporting prominences, and how does so much cool, dense plasma appear in the corona? Despite centuries of increasingly detailed observations, the magnetic and plasma structures in prominences are poorly known. Routine measurements of the vector magnetic field in and around prominences have become possible only recently, while long-term monitoring of the underlying filament-channel formation process also remains scarce. The process responsible for prominence mass is equally difficult to establish, although we have long known that the chromosphere is the only plausible source. As I will discuss, however, the motions and locations of prominence material can be used to trace the coronal field, thus defining the magnetic origins of solar eruptions. A combination of observations, theory, and numerical modeling must be used to determine whether any of the competing theories accurately represents the physics of prominences. I will discuss the criteria for a successful prominence model, compare the leading models, and present in detail one promising, comprehensive scenario for prominence formation and evolution that could answer the two questions posed above.

Karpen, Judy T.↗
NASA NTRSDate not supplied

Comparing the Electrical Modeling and Thermal Analysis Toolbox Simulation Data to Electrified Aircraft Propulsion Test Hardware Data

The Electrical Modeling and Thermal Analysis Toolbox (EMTAT), a National Aeronautics and Space Administration (NASA)-developed Simulink™ model block library of electrical components developed with the goal of facilitating system-level control design and analysis, has two levels of fidelity depending on the test needs. The Physics Based model blocks are designed to use physical component characteristics to more accurately model real hardware, including power losses, efficiency, thermal effects, electromagnetic losses, voltage drops and current requirements. These blocks model electrical dynamics that occur at the millisecond turbomachinery time scale which allows for faster-than-real-time electrified turbomachinery simulations. A model was developed to mirror the Hybrid Propulsion Emulation Rig (HyPER) hardware, a laboratory focused on Electrified Aircraft Propulsion (EAP) hardware tests. The present hardware setup supports Turbine Electrified Energy Management (TEEM) testing. The outputs of the model were compared to the results of several tests on the HyPER laboratory motor-generator setup with the goal of matching the steady state simulation data to steady state hardware data within 5% of full scale. The objective of this paper is to present the background, setup, testing and results of this comparison. It will describe some of the adjustments that were necessary to match the system hardware, as well as next steps in verification and validation.

Hyper↗
NASA NTRS2012

The Use of Satellite-Measured Aerosol Optical Depth to Constrain Biomass Burning Emissions Source Strength in a Global Model GOCART

Small particles in the atmosphere, called "atmospheric aerosol" have a direct effect on Earth climate through scattering and absorbing sunlight, and also an indirect effect by changing the properties of clouds, as they interact with solar radiation as well. Aerosol typically stays in the atmosphere for several days, and can be transported long distances, affecting air quality, visibility, and human health not only near the source, but also far downwind. Smoke from vegetation fires is one of the main sources of atmospheric aerosol; other sources include anthropogenic pollution, dust, and sea salt. Chemistry transport models (CTMs) are among the major tools for studying the atmospheric and climate effects of aerosol. Due to the considerable variation of aerosol concentrations and properties on many temporal and spatial scales, and the complexity of the processes involved, the uncertainties in aerosol effects on climate are large, as is featured in the latest report of Intergovernmental Panel on Climate Change (IPCC) in 2007. Reducing this uncertainty in the models is very important both for predicting future climate scenarios and for regional air quality forecasting and mitigation. During vegetation fires, also called biomass burning (BB) events, complex mixture of gases and particles is emitted. The amount of BB emissions is usually estimated taking into account the intensity and size of the fire and the properties of burning vegetation. These estimates are input into CTMs to simulate BB aerosol. Unfortunately, due to large variability of fire and vegetation properties, the quantity of BB emissions is very difficult to estimate and BB emission inventories provide numbers that can differ by up to the order of magnitude in some regions. Larger uncertainties in data input make uncertainties in model output larger as well. A powerful way to narrow the range of possible model estimates is to compare model output to observations. We use satellite observations of aerosol properties, specifically aerosol optical depth, which is directly proportional to the amount of aerosol in the atmosphere, and compare it to the model output. Assuming the model represents aerosol transport and particle properties correctly, the amount of BB emissions determines the simulated aerosol optical depth. In this study, we explore the regional performance of 13 commonly used emission estimates. These are each input to global Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. We then evaluate how well each emission estimate reproduces the smoke aerosol optical depth measured by the MODIS instrument. We compared GOCART-simulate aerosol optical depth with that measured from the satellite for 124 fire cases around the world during 2006 and 2007. We summarize the regional performance of each emission inventory and discuss reasons for their differences by considering the assumptions made during their development. We also show that because stronger wind disperses smoke plumes more readily, in cases with stronger wind, a larger increase in emission amount is needed to increase aerosol optical depth. In quiet, low-wind-speed environments, BB emissions produce a more significant increase in aerosol optical depth, other things being equal. Using the region-specific, quantitative relationships derived in our paper, together with the wind speed obtained from another source for a given fire case, we can constrain the amount of emission required in the model to reproduce the observations. The results of this paper are useful to the developers of BB emission inventories, as they show the strengths and weaknesses of individual emission inventories in different regions of the globe, and also for modelers who use these inventories and wish to improve their model results.

Petrenko, Mariya↗
DOE OSTI2025

Geographic source attribution of honey by strontium isotope analyses: Latvia and India measurements compared to model predictions in a feasibility study

Strontium isotope ratios ( 87 Sr/ 86 Sr) can be used to determine the geographic origin of agricultural products. Here, in this study, we measured the 87 Sr/ 86 Sr of honey samples from Richland, WA, USA, Latvia, and India, and compared the results to published 87 Sr/ 86 Sr from surrounding areas and values predicted based upon a random forest isoscape model (Bataille et al., 2020). While the 87 Sr/ 86 Sr of the honey samples compared well with previously published data, the model did not accurately predict the 87 Sr/ 86 Sr of the honey samples, demonstrating that further refinement of the model would be beneficial. Because Sr cycling in terrestrial ecosystems is complex, the 87 Sr/ 86 Sr of honey is likely to reflect several sources (e.g., nectar, surface water, dust) and may change over time. In order to accurately predict the 87 Sr/ 86 Sr of honey, models must consider local geology, ecology, and bee behavior. Additional research examining how Sr is incorporated into honey is needed to inform models.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1989

Mathematical Models Of Turbulence In Transonic Flow

Predictions of several models compared with measurements of well-documented flow. Report reviews performances of variety of mathematical models of turbulence in transonic flow. Predictions of models compared with measurements of flow in wind tunnel along outside of cylinder having axisymmetric bump of circular-arc cross section in meridional plane. Review part of continuing effort to calibrate and verify computer codes for prediction of transonic flows about airfoils. Johnson-and-King model proved superior in predicting transonic flow over bumpy cylinder.

Rubesin, Morris W.↗