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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Stacked-mosaic amplifier and diode delivery concept for kJ-class inertial fusion energy laser driver

We report on a laser amplifier architecture designed as a modular element for scaling to multi-megajoule laser facilities intended for inertial fusion energy (IFE) power plants. This kilojoule-class module features a stacked mosaic of gain media integrated with a diode delivery system for efficient optical pumping of the mosaic-structured gain medium, enabling high-repetition-rate operation at high wall plug efficiencies, necessary for an IFE driver. Details of a diode delivery system capable of pumping the mosaic architecture are presented. Comprehensive numerical modeling demonstrates that the stacked-mosaic approach reduces transverse gain by five orders of magnitude compared to a conventional full-aperture Yb:YAG based amplifier, substantially suppressing transverse amplified spontaneous emission (TASE) and enabling enhanced longitudinal energy extraction. Detailed analysis of thermal management and wavefront distortion in a 4 × 4 mosaic array indicates that temperature gradients and thermally induced aberrations are effectively controlled using gas cooling and commercially available phase-plate and deformable mirror technologies. We further discuss the applicability of a stacked-mosaic architecture to direct-drive IFE schemes, where broad spectral bandwidth is critical, and its compatibility with frequency conversion modules for up-conversion to blue wavelengths. Finally, an example point design for a 10 kJ, 10 Hz Yb:YAG module operating at 175 K with wall-plug efficiency exceeding 10 % is presented, underscoring the feasibility of this approach for next-generation high-energy lasers for IFE drivers. The results establish the stacked-mosaic amplifier as a scalable, robust platform not only for IFE but also for a broad range of advanced scientific and industrial laser applications.

Lasers↗
DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗
NASA NTRS2016

The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.

photosynthesis↗
DOE OSTI2021

Thin Water Films Enable Low-Temperature Magnesite Growth Under Conditions Relevant to Geologic Carbon Sequestration

Injecting supercritical CO2 (scCO2) into basalt formations for long-term storage is a promising strategy for mitigating CO2 emissions. Mineral carbonation can result in permanent entrapment of CO2; however, carbonation kinetics in thin H2O films in humidified scCO2 is not well understood. We investigated forsterite (Mg2SiO4) carbonation to magnesite (MgCO3) via amorphous magnesium carbonate (AMC; MgCO3·xH2O, 0.5 < x < 1), with the goal to establish the fundamental controls on magnesite growth rates at low H2O activity and temperature. Experiments were conducted at 25, 40, and 50 °C in 90 bar CO2 with a H2O film thickness on forsterite that averaged 1.78 ± 0.05 monolayers. In situ infrared spectroscopy was used to monitor forsterite dissolution and the growth of AMC, magnesite, and amorphous SiO2 as a function of time. Geochemical kinetic modeling showed that magnesite was supersaturated by two to three orders of magnitude and grew according to a zero-order rate law. The results indicate that the main drivers for magnesite growth are sustained high supersaturation coupled with low H2O activity, a combination of thermodynamic conditions not attainable in bulk aqueous solution. This improved understanding of reaction kinetics can inform subsurface reactive transport models for better predictions of CO2 fate and transport.

Kerisit, Sebastien N.↗
NASA NTRS2004

Model implementation for dynamic computation of system cost for advanced life support

Life support system designs for long-duration space missions have a multitude of requirements drivers, such as mission objectives, political considerations, cost, crew wellness, inherent mission attributes, as well as many other influences. Evaluation of requirements satisfaction can be difficult, particularly at an early stage of mission design. Because launch cost is a critical factor and relatively easy to quantify, it is a point of focus in early mission design. The method used to determine launch cost influences the accuracy of the estimate. This paper discusses the appropriateness of dynamic mission simulation in estimating the launch cost of a life support system. This paper also provides an abbreviated example of a dynamic simulation life support model and possible ways in which such a model might be utilized for design improvement. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Discipline Life Support Systems↗
DOE OSTI2022

Tropical extreme droughts drive long-term increase in atmospheric CO 2 growth rate variability

The terrestrial carbon sink slows the accumulation of carbon dioxide (CO 2 ) in the atmosphere by absorbing roughly 30% of anthropogenic CO 2 emissions, but varies greatly from year to year. The resulting variations in the atmospheric CO 2 growth rate (CGR) have been related to tropical temperature and water availability. The apparent sensitivity of CGR to tropical temperature ( ${{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}$ ) has changed markedly over the past six decades, however, the drivers of the observation to date remains unidentified. Here, we use atmospheric observations, multiple global vegetation models and machine learning products to analyze the cause of the sensitivity change. We found that a threefold increase in ${{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}$ emerged due to the long-term changes in the magnitude of CGR variability (i.e., indicated by one standard deviation of CGR; STD CGR ), which increased 34.7% from 1960-1979 to 1985-2004 and subsequently decreased 14.4% in 1997-2016. We found a close relationship (r 2 = 0.75, p < 0.01) between STD CGR and the tropical vegetated area (23°S – 23°N) affected by extreme droughts, which influenced 6-9% of the tropical vegetated surface. A 1% increase in the tropical area affected by extreme droughts led to about 0.14 Pg C yr -1 increase in STD CGR . The historical changes in STD CGR were dominated by extreme drought-affected areas in tropical Africa and Asia, and semi-arid ecosystems. The outsized influence of extreme droughts over a small fraction of vegetated surface amplified the interannual variability in CGR and explained the observed long-term dynamics of ${{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}$ .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Key advancements toward eliminating the “drive deficit” in ICF hohlraum simulations

This paper, extended from a Letter [Chen et al., Phys. Rev. E 110, L013201 (2024)] published in Phys. Rev. E in 2024, presents detailed results from a set of experiments studying the x-ray drive produced in laser-heated ignition-scale hohlraums at the National Ignition Facility. The results reveal that deficiencies in non-local thermodynamic equilibrium atomics physics calculations of gold emissivity lead to the overestimation of the “M-band” radiation and are responsible for discrepancies in drive predictions vs measurements in these hohlraums. These experiments were performed on ViewFactor targets with the same gas fill and laser pulse shapes as currently used in ongoing ignition experiments. The common hohlraum model with a magnetohydrodynamics heat transport model and a 10%–20% reduction to the M-band absorption and emission opacity can bring the simulations into agreement with several independent measurements, including the absolute radiation flux, the radiation flux in the 2–4 keV spectral range, and spectroscopic measurements of both K-shell dopant and L-shell gold. This new model is applicable to ICF and high-energy density experiments that use gold hohlraums as drivers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Processes and mechanisms of coastal woody-plant mortality

Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO 2 , and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO 2 , drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.

salinity↗
DOE OSTI2023

Seasonal trends in leaf-level photosynthetic capacity and water use efficiency in a North American Eastern deciduous forest and their impact on canopy-scale gas exchange

Vegetative transpiration (E) and photosynthetic carbon assimilation (A) are known to be seasonally dynamic, with changes in their ratio determining the marginal water use efficiency (WUE). Despite an understanding that stomata play a mechanistic role in regulating WUE, it is still unclear how stomatal and nonstomatal processes influence change in WUE over the course of the growing season. As a result, limited understanding of the primary physiological drivers of seasonal dynamics of canopy WUE remains one of the largest uncertainties in earth system model projections of carbon and water exchange in temperate deciduous forest ecosystems. Here, we investigated seasonal patterns in leaf-level physiological, hydraulic, and anatomical properties, including the seasonal progress of the stomatal slope parameter (g 1 ; inversely proportional to WUE) and the maximum carboxylation rate (V cmax ). V cmax and g 1 were seasonally variable; however, their patterns were not temporally synchronized. g 1 generally showed an increasing trend until late in the season, while V cmax peaked during the midsummer months. Seasonal progression of V cmax was primarily driven by changes in leaf structural, and anatomical characteristics, while seasonal changes in g 1 were most strongly related to changes in V cmax and leaf hydraulics. Using a seasonally variable V cmax and g 1 to parameterize a canopy-scale gas exchange model increased seasonally aggregated A and E by 3% and 16%, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Policy and Regulatory Environment for Utility-Scale Energy Storage: India

This report is part of a series investigating the potential for utility-scale energy storage in South Asia. The first part of the work is focused on opportunities and barriers for energy storage within existing policy and regulatory frameworks in the region. This report applies the previously-developed Energy Storage Readiness Assessment to evaluate the policy and regulatory environment for energy storage in India. Forthcoming reports will expand this analysis to other countries in South Asia. These evaluations are designed to provide insights on the opportunities and barriers for energy storage growth and deployment in each country. The second part of this work consists of techno-economic analysis to understand the drivers of energy storage investments in South Asia. Using NREL’s power system planning and operational models of South Asia, this analysis will identify potential storage applications and growth under various cost, policy, and demand growth scenarios. In addition, the regulatory and policy barriers and incentive mechanisms identified in the first task will be incorporated into the modeling task to understand their impact on energy storage deployment and operation. Together these studies will inform the applications and value of energy storage on South Asia power systems and policy and regulatory pathways to realize this value.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2000

Space Plasma Physics

Dr. James L. Horwitz and R. Hugh Comfort's studies with the high altitude TIDE data have been progressing well. We concluded a study on the relationship of polar cap ion properties observed by TIDE near apogee with solar wind and IMF conditions. We found that in general H+ did not correlate as well as O+ with solar wind and IMF parameters. O+ density correlated(sub IMF), and Kp. At lower solar wind speeds, O+ density decreased with increasing latitude, but this trend was not observed at higher solar wind speeds. By comparing these results with results from other studies of O+ in different parts of the magnetosphere, we concluded that O+ ions often leave the ionosphere near the foot point of the cusp/cleft region, pass through the high-altitude polar cap lobes, and eventually arrive in the plasma sheet. We found that H+ outflows are a persistent feature of the polar cap and are not as dependent on the geophysical conditions; even classical polar wind models show H+ ions readily escaping owing to their low mass. Minor correlations with solar wind drivers were found; specifically, H+ density correlated best with IMF By, V(sub sw)B(sub IMF), and ESW(sub sw).

Wu, S. T.↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. An inverse estimation of the backshell and heatshield surface aeroheating was performed, using the data from the MEDLI2 Instrumented Sensor Plugs, a network of thermocouples embedded within the thermal protection system across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of the surface heat rate, temperature, and heat load to uncertainties in thermocouple depth and material properties. In this paper, a variance decomposition method using Sobol indices was employed to understand the relative contributions of each uncertainty parameter. Performing this analysis using results from the inverse analysis tool FIAT_Opt was found to require incredibly high computation time, and thus machine learning models were trained and evaluated as a surrogate model for FIAT_Opt. This paper demonstrates that machine learning models can be an efficient, accurate alternative to state-of-the-art inverse analysis tools like FIAT_Opt, especially for computationally-expensive processes. Using these models, the sensitivity analysis showed that uncertainties in heat capacity and thermal conductivity were the main drivers for the overall uncertainty in peak reconstructed heating and heat load.

H S Alpert↗
NASA NTRSDate not supplied

Anthropogenic Pathways for Modeling and Managing Future Arctic Fires

Wildland fires, including extreme fire events and seasons, are becoming more common in the boreal and Arctic regions due to climate change. Current climate modeling approaches do not include country- or region-specific socioeconomic pathways that specifically address the drivers of and potential mitigation techniques for wildland fires. Forest management, energy extraction, and tourism, together with firefighting capacity and readiness as well as fuels treatment, can have a significant impact on future wildland fire risks and impacts. To assess the impacts of anthropogenic factors and to align with previous work done on shared socioeconomic pathways (SSPs), climate pathways for future wildfires up to 2050 were created for the states that compose the original Arctic Council countries: Canada, the United States, the Kingdom of Denmark, Iceland, Sweden, Norway, and Finland as well as the Russian Federation (with whom the other seven countries withdrew participation from in May 2022 due to the invasion and ongoing war in Ukraine). High and low fire activity and risk pathways for all states comprising the Arctic were made, with expert ‘best guess’ pathways for each state created separately to represent the middle of road. The low activity and low fire risk pathways, named “We Got This”, assume active fire suppression via citizenry participation and official land management, efficient and extensive fuel treatments, and consistent and active wildland firefighting for each new ignition. The high activity and high fire risk pathways, named “Let It Burn”, assume nearly the opposite, due to lack of government and community response and no action on climate change drivers that increase wildland fire risk. The ‘best guess’ pathway, named “The Fire Will Come”, indicates that some countries are currently on the pathway for less fire compared to other Arctic and Boreal states but not a ‘no-fire’ future. For example, in the Nordic countries, human ignition sources from tourism, timber and energy extraction, summer cottages, and expanding wildland-urban intermix due to exurban growth may increase. In North America, these same risks will apply but also may see an expansion of agriculture that increases the likelihood of open burning in croplands. Drier fuel condition and extreme heat events due to climate change create favorable conditions for extreme wildfires from any ignition source. Throughout the Arctic and boreal lightning is expected to increase, increasing the risk of tundra fires in addition to forest fires in hard-to-reach locations that are more difficult to coordinate and execute wildland firefighting. To move the future Arctic fire SSPs forward, several short-term and long-term actions must be completed. Certain data needs are required, like a harmonized pan-Arctic and pan-boreal fuels geospatial product, while also a need to refine and socialize current definitions of fire seasons and fire management – including developing an open-source system to track and share innovation, mitigation, and adaptation strategies across Arctic states.

Arctic↗
DOE OSTI2023

Molten Salt Reactor Experiment Simulation using Shift/Griffin

The Department of Energy (DOE)’s NEAMS focuses its efforts on the development of advanced modeling and simulation (M&S) tools for light-water reactors (LWRs) and non–LWRs (i.e., molten salt reactors, high-temperature gas reactors, microreactors, and fast reactors). In the previous fiscal year, the Multiphysics Applications Driver Technical Area funded molten salt reactor (MSR) M&S at Oak Ridge National Laboratory (ORNL) to generate multigroup macroscopic cross sections with Shift for a MSRE 2D lattice model in Griffin. In addition, Shift’s capability to calculate gamma dose rates from activated components in the primary exchangers in a molten salt breeder reactor was also demonstrated. In fiscal year 2023, ORNL generated multigroup macroscopic cross sections using Shift for a 3D MSRE core model. MSRE depletion calculations using Griffin were also demonstrated in this fiscal year. For the depletion calculation, one-group microscopic cross sections for the 3D MSRE core were generated using Shift, and the decay transmutation library from ORIGEN was converted to an ISOXML file, which is required as input in Griffin. Several Monte Carlo codes, such as OpenMC and Serpent, were also used to benchmark and supplement multigroup cross sections generated by Shift. Multigroup libraries were generated with 8 and 20 group structures, and the study found the 8-group structure to be more accurate when comparing Griffin results to continuous energy (CE) Monte Carlo results. The average flux from CE Shift calculations is up to ~6% higher than the CE Serpent calculations because of different values applied for the energy released per fission (κ values). The average flux in the fuel salt calculated by Griffin using cross sections generated with Shift agrees well with the reference CE Shift solution; the same is valid for the corresponding Serpent results. The maximum relative error is ~6% and ~2% compared to the CE Shift and Serpent reference solutions, respectively. Meanwhile, the average flux calculated by Griffin in the graphite moderator shows a higher difference in the thermal range when compared to both reference Monte Carlo solutions; this result suggests a need for improvement in cross section generation for the graphite moderator in the thermal range in both Monte Carlo codes. Griffin depletion calculations using cross sections from Shift and Serpent were performed and compared against ORIGEN calculations, and the nuclide densities calculated by Griffin were found to be generally in agreement with those of ORIGEN. Because a different approach was taken to calculate the energy released per fission ( κ values) in Shift and Serpent, a difference in nuclide densities from differences in the average flux was observed between Griffin using Serpent and Shift cross sections. Griffin calculations with Shift cross sections produced higher average flux in the salt than with Serpent cross sections, leading to higher consumption of 235 U and higher production of 135 Xe. For time-dependent depletion calculations, cross sections were generated with Serpent, and Griffin’s results using these cross sections were compared to CE Serpent depletion results, demonstrating good agreement. The average difference in keff between Serpent and Griffin as a function of burnup is about 155 pcm. Similarly, good agreement with small differences up to ~0.5% was also noticed in the nuclide density of 235 U and 135 Xe. More details regarding the methodologies invoked to generate the cross section to make code-to-code comparisons are discussed further in this report. User feedback on Griffin and Shift capabilities that will enhance these calculations is provided in this report for future consideration. The work performed this fiscal year can be extended further for multiphysics coupling of Griffin-Pronghorn/SAM with Mole to study precursor flow and salt chemistry.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2004

Wave-Particle Interactions As a Driving Mechanism for the Solar Wind

Our research has been focusing on a highly experimentally relevant issue: intermittency of the fluctuating fields in outflowing plasmas. We have contributed to both the theoretical and experimental research of the topic. In particular, we have developed a theoretical model and data analyzing programs to examine the issue of intermittency in space plasma outflows, including the solar wind. As fluctuating electric fields in the solar wind are likely to provide a heating and acceleration mechanism for the ions, our studies of the intermittency in turbulence in space plasma outflows help us toward achieving the goal of comparing major physical mechanisms that contribute to the driving of the fast solar wind. Our new theoretical model extends the utilities of our global hybrid model, which has allowed us to follow the kinetic evolution of the particle distributions along an inhomogeneous field line while the particles are subjected to various physical mechanisms. The physical effects that were considered in the global hybrid model included wave-particle interactions, an ambipolar electric field that was consistent with the particle distributions themselves, and Coulomb collisions. With an earlier version of the global hybrid model, we examined the overall impact on the solar wind flow due to the combination of these physical effects. In particular, we studied the combined effects of two major mechanisms that had been proposed as the drivers of the fast solar wind: (1) velocity filtration effect due to suprathermal electrons; (2) ion cyclotron resonance. Since the approval of this research grant, we have updated the model such that the effects due to these two driving mechanisms can be examined separately, thereby allowing us to compare their contributions to the acceleration of the solar wind. In the next section, we shall demonstrate that the velocity filtration effect is rather insignificant in comparison with that due to ion cyclotron resonance.

Wagner, William J.↗
DOE OSTI2022

Moisture‐Budget Drivers of Global Projections of Meteorological Drought From Multiple GCM Large Ensembles

Future projections of global meteorological drought are evaluated in the Multi‐Model Large Ensemble Archive, including an evaluation of the atmospheric moisture budget, conditioned on drought years. Drought is defined as 5‐year running‐mean annual precipitation below some threshold, for example, 10th percentile. Drought increases in frequency over the subtropics, in addition to certain tropical regions, consistent with previous studies. Here, the moisture‐budget decomposition allows drought to be defined as mean‐flow, eddy, or feedback droughts, depending on which term in the equation contributes the largest negative interannual anomaly. In the historical climate, mean‐flow droughts constitute most droughts at low latitudes; eddy droughts are equally common at higher latitudes; feedback droughts (i.e., droughts exacerbated by land–atmosphere feedbacks) constitute almost all droughts in water‐limited subtropical/Mediterranean regions. The future drought increases are predominantly due to increases in feedback droughts in regions where these droughts are common historically but also over the Amazon. However, over most Mediterranean‐type regions mean‐flow droughts are also large contributors, resulting from dynamics. Eddy droughts also contribute to future increases along the equatorward flanks of historical eddy‐driven jets, likely reflecting poleward shifts therein. Model uncertainty is particularly large over the Amazon and Australia, a reflection of model diversity in processes associated with land‐atmosphere interaction. Based on these results, an availability of 3‐D atmospheric data from a wider swath of global climate model large ensembles could help constrain global drought projections based on the representation of drought mechanisms in the historical climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Firm Synthesizer and Supply-chain Simulator (SynthFirm) v2.0

SynthFirm is a national-scale agent-based freight demand model which generates a complete synthetic population of firms in the U.S. and the business-to-business commodity flows between them. Using publicly available data sources as inputs, SynthFirm simulates detailed firm and fleet characteristics, commodity production and consumption, formation of supply chains, and selection of shipping modes, all of which are essential drivers of commodity flow at a disaggregate level. The SynthFirm 2.0 version includes national commercial vehicle fleet generation, international trade simulation and automized model validation pipeline, which allows seemless deployment across the nation and build a comprehensive freight inventories at national scale or for selected region.

Yang, Hung-Chia [Lawrence Berkeley National Labora↗