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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Global Total Ozone Recovery Trends Attributed to Ozone-Depleting Substance (ODS) Changes Derived From Five Merged Ozone Datasets

We report on updated trends using different merged zonal mean total ozone datasets from satellite and ground-based observations for the period from 1979 to 2020. This work is an update of the trends reported in Weber et al. (2018) using the same datasets up to 2016. Merged datasets used in this study include NASA MOD v8.7 and NOAA Cohesive Data (COH) v8.6, both based on data from the series of Solar Backscatter Ultraviolet (SBUV), SBUV-2, and Ozone Mapping and Profiler Suite (OMPS) satellite instruments (1978–present), as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone – Essential Climate Variable (GTO-ECV) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (both 1995–present), mainly comprising satellite data from GOME, SCIAMACHY, OMI, GOME-2A, GOME-2B, and TROPOMI. The fifth dataset consists of the annual mean zonal mean data from ground-based measurements collected at the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. The addition of 4 more years consolidated the fact that total ozone is indeed slowly recovering in both hemispheres as a result of phasing out ozone-depleting substances (ODSs) as mandated by the Montreal Protocol. The near-global (60° S–60° N) ODS-related ozone trend of the median of all datasets after 1995 was 0.4 ± 0.2 (2σ) %/decade, which is roughly a third of the decreasing rate of 1.5 ± 0.6 %/decade from 1978 until 1995. The ratio of decline and increase is nearly identical to that of the EESC (equivalent effective stratospheric chlorine or stratospheric halogen) change rates before and after 1995, confirming the success of the Montreal Protocol. The observed total ozone time series are also in very good agreement with the median of 17 chemistry climate models from CCMI-1 (Chemistry-Climate Model Initiative Phase 1) with current ODS and GHG (greenhouse gas) scenarios (REF-C2 scenario). The positive ODS-related trends in the Northern Hemisphere (NH) after 1995 are only obtained with a sufficient number of terms in the MLR accounting properly for dynamical ozone changes (Brewer–Dobson circulation, Arctic Oscillation (AO), and Antarctic Oscillation (AAO)). A standard MLR (limited to solar, Quasi-Biennial Oscillation (QBO), volcanic, and El Niño–Southern Oscillation (ENSO)) leads to zero trends, showing that the small positive ODS-related trends have been balanced by negative trend contributions from atmospheric dynamics, resulting in nearly constant total ozone levels since 2000.

Total Column Ozone Trends↗
DOE OSTI2022

Performance Before and After Irradiation of Pixelated 3D Silicon Sensors for the HL-LHC CMS Tracker

The Large Hadron Collider (LHC) particle accelerator at European Center for Nuclear Research (CERN) will be shut down starting in 2026 to achieve the High Luminosity Large Hadron Collider (HL-LHC) upgrade. The upgrade will allow for higher fluences, in order to increase the probability of detecting increasingly rare particles, and to obtain higher precision measurements of known particles. To accommodate the new accelerator conditions, many aspects of the Compact Muon Solenoid (CMS) detector will be upgraded; of particular interest for this thesis are the silicon pixel detectors located in the inner tracker. These will be replaced and upgraded to accommodate the higher fluences of the HL-LHC upgrade, as well as to replace existing sensors which have sustained radiation damage. In order for the new sensors to operate under high luminosity conditions, they must be increasingly radiation hard, and in order to detect rare particles, they must be increasingly more precise. The performance of one Centro Nacional de Microelectronica (CNM) 3D silicon sensor before and after undergoing irradiation at fluences similar to those which will be observed at the HL-LHC was investigated to determine radiation hardness and precision. Data was collected at Fermi National Laboratory (Fermilab), in the Fermi National Laboratory Test Beam Facility (FTBF) silicon tracker telescope, which can be used to determine the number of particles, and tracks made by high energy protons passing through. The sensor was also irradiated at Fermilab in the Irradiation Test Area (ITA). Prior to data collection a tuning procedure is carried out to determine ideal bias voltage operating conditions, mask noisy and dead pixels, adjust to the ideal threshold, and map sensor gain. Data is then collected at the FTBF, where the sensor is installed in the center of the FTBF silicon telescope. Variables, including angle and bias voltage, are varied throughout data collection. Data is then processed using an alignment software to determine the exact telescope geometry, along with the tracks which were observed passing through the sensor and telescope. Sensor performance was found to be comparable before and after irradiation, with irradiated results showing slightly lower efficiencies and cluster sizes. Position resolution is comparable both before and after irradiation, and similar distributions of cluster shape are observed. After irradiation, the sensor shows increasing collected charge with bias, an indication of increased width of the depletion region. Peak charge pre-irradiation is higher than post-irradiation peak charge, indicating the irradiated results are not taken under fully-depleted conditions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation

Abstract Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation ( Z * MHW ), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z * MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z * MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z * MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z * MHW and Z * MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z * MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z * MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.

Holmquist, James R. (ORCID:0000000325466766)↗
DOE OSTI2025

Pyrogenic Organic Matter Laboratory Experiment: Aerobic Respiration and Geochemistry from Variably Inundated Stream Sediments (v3)

This dataset supports a broader study examining the effects of variable inundation and pyrogenic organic matter on ecosystem respiration. The dataset provides data generated from a laboratory batch experiment investigating the interaction between variable inundation conditions (wet and dry sediment) and pyrogenic organic matter (burned and unburned treatments). The contents include time series dissolved oxygen, sediment geochemistry data, and field metadata (including qualitative information on instream and river corridor characteristics). This data package was originally published in November 2025. It was updated in April 2026 (v2; new and modified files) and May 2026 (v3; modified files). See the change history section in the readme for more details For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) international generic sample number (IGSN) mapping file; (5) readme; (6) field protocol; (7) sample name metadata; (8) an environmental context picture for the dry and inundated sampling locations; and (9) a subfolder with sample data from the sediment incubation experiment. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC); (2) total nitrogen (TN); (3) gravimetric moisture; (4) partial pressure and production rates of carbon dioxide, methane, and nitrous oxide; (5) field wet sediment mass, dry sediment mass, water mass, and field wet sediment volume in incubation and sediment NPOC/TN vials; (6) methods codes; (7) respiration rates, pH, and temperature from after the incubation, raw time series dissolved oxygen and temperature, and a subfolder containing associated plots and scripts; (8) ions; (9) FTICR-MS methods; and (10) a subfolder of 12 Tesla (12T) FTICR-MS data. This folder contains the CoreMS processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .html, .Rmd, .py, .cal, .json, or .jpg.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Velocity Measurements in the Wake of the Swept Wing Flow Test Model at the National Transonic Facility

Femtosecond laser electronic excitation tagging (FLEET) was applied to obtain flowfield velocity data for the Swept Wing Flow Test (SWiFT) at the NASA Langley Research Center National Transonic Facility (NTF). Despite numerous challenges associated with performing flow velocimetry measurements within a large-scale cryogenic wind tunnel facility, the experimental campaign was conducted under various conditions, including dry air at 320 K, cool nitrogen at 240 K, and Mach numbers of 0.2 and 0.8. FLEET velocimetry measurements were performed in the downstream wake of the SWiFT model, providing a quantitative dataset. This dataset includes one-dimensional velocity profiles and two-dimensional velocity maps acquired at different angles of attack and Reynolds numbers. The two-dimensional single component velocity maps indicate relatively uniform flow across the 120-mm wide wake flow survey suggesting that the main flow features can be represented by single-position linear velocity profiles. The measured instantaneous velocity profiles at variable angles of attack are compared to model lift coefficient information obtained at Mach 0.2. At high Reynolds numbers, a sudden velocity decrease was observed in the FLEET measurements at the same time as stall in lift coefficient was observed. At a low Reynolds number, both the velocity profiles and the lift coefficient show a smoother transition, without a sudden stall. Furthermore, the two-dimensional, one component velocity map reveals a velocity deficit region at Mach 0.8 at various angles of attack. Both single shot and mean velocity measurements were acquired allowing assessment of flowfield fluctuations and measurement precisions. The uncertainties are within 4 m/s in mean measurements based on repeatability data and about 5 m/s in instantaneous single-shot measurements. Measurements are reported with ~4.8 mm spatial resolution with 32-pixel averaging used to reduce measurement errors.

femtosecond↗
NASA NTRSDate not supplied

Velocity Measurements in the Wake of the Swept Wing Flow Test (SWIFT) Model at the National Transonic Facility

Femtosecond laser electronic excitation tagging (FLEET) was applied to obtain flowfield velocity data for the Swept Wing Flow Test (SWiFT) at the NASA Langley Research Center National Transonic Facility (NTF). Despite numerous challenges associated with performing flow velocimetry measurements within a large-scale cryogenic wind tunnel facility, the experimental campaign was conducted under various conditions, including dry air at 320 K, cool nitrogen at 240 K, and Mach numbers of 0.2 and 0.8. FLEET velocimetry measurements were performed in the downstream wake of the SWiFT model, providing a quantitative dataset. This dataset includes one-dimensional velocity profiles and two-dimensional velocity maps acquired at different angles of attack and Reynolds numbers. The two-dimensional single component velocity maps indicate relatively uniform flow across the 120-mm wide wake flow survey suggesting that the main flow features can be represented by single-position linear velocity profiles. The measured instantaneous velocity profiles at variable angles of attack are compared to model lift coefficient information obtained at Mach 0.2. At high Reynolds numbers, a sudden velocity decrease was observed in the FLEET measurements at the same time as stall in lift coefficient was observed. At a low Reynolds number, both the velocity profiles and the lift coefficient show a smoother transition, without a sudden stall. Furthermore, the two-dimensional, one component velocity map reveals a velocity deficit region at Mach 0.8 at various angles of attack. Both single shot and mean velocity measurements were acquired allowing assessment of flowfield fluctuations and measurement precisions. The uncertainties are within 4 m/s in mean measurements based on repeatability data and about 5 m/s in instantaneous single-shot measurements. Measurements are reported with ~4.8 mm spatial resolution with 32-pixel averaging used to reduce measurement errors.

Transonic↗
NASA NTRS2012

TRMM Precipitation Application Examples Using Data Services at NASA GES DISC

Data services to support precipitation applications are important for maximizing the NASA TRMM (Tropical Rainfall Measuring Mission) and the future GPM (Global Precipitation Mission) mission's societal benefits. TRMM Application examples using data services at the NASA GES DISC, including samples from users around the world will be presented in this poster. Precipitation applications often require near-real-time support. The GES DISC provides such support through: 1) Providing near-real-time precipitation products through TOVAS; 2) Maps of current conditions for monitoring precipitation and its anomaly around the world; 3) A user friendly tool (TOVAS) to analyze and visualize near-real-time and historical precipitation products; and 4) The GES DISC Hurricane Portal that provides near-real-time monitoring services for the Atlantic basin. Since the launch of TRMM, the GES DISC has developed data services to support precipitation applications around the world. In addition to the near-real-time services, other services include: 1) User friendly TRMM Online Visualization and Analysis System (TOVAS; URL: http://disc2.nascom.nasa.gov/Giovanni/tovas/); 2) Mirador (http://mirador.gsfc.nasa.gov/), a simplified interface for searching, browsing, and ordering Earth science data at GES DISC. Mirador is designed to be fast and easy to learn; 3) Data via OPeNDAP (http://disc.sci.gsfc.nasa.gov/services/opendap/). The OPeNDAP provides remote access to individual variables within datasets in a form usable by many tools, such as IDV, McIDAS-V, Panoply, Ferret and GrADS; and 4) The Open Geospatial Consortium (OGC) Web Map Service (WMS) (http://disc.sci.gsfc.nasa.gov/services/wxs_ogc.shtml). The WMS is an interface that allows the use of data and enables clients to build customized maps with data coming from a different network.

Liu, Zhong↗
DOE OSTI2026

Spring Dust in Colorado Plateau: Transport Pathways and Interannual Variability Derived From Two Decades of MERRA‐2 Reanalysis

Spring dust storms in the U.S. Southwest significantly impact environmental and human systems, yet their climatological patterns and driving mechanisms remain poorly understood. Using two decades of MERRA-2 reanalysis data and self-organizing map clustering, we identified four distinct dust transport pathways from surrounding and remote deserts for the top 10% of spring dust events impacting the Colorado Plateau: (a) intense southwesterly near-surface transport where eastward upward transport is blocked by topography, (b) substantial remote transport plus weaker surface emissions, (c) moderate southwesterly transport with stronger upper-level transport, and (d) rare northwesterly pattern linked to the Great Basin sources, which accounts for 38%, 14%, 37%, and 11% of total events, respectively. The frequency and intensity of these events strongly correlate with large-scale climate variability, with increased dust events during La Niña. Our results provide a robust framework for improving seasonal dust forecasts and understanding future dust dynamics under climate change.

aerosol transport↗
NASA NTRS1995

A comparison of design variables for control theory based airfoil optimization

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work in the area it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using either the potential flow or the Euler equations with either a conformal mapping or a general coordinate system. We have also explored three-dimensional extensions of these formulations recently. The goal of our present work is to demonstrate the versatility of the control theory approach by designing airfoils using both Hicks-Henne functions and B-spline control points as design variables. The research also demonstrates that the parameterization of the design space is an open question in aerodynamic design.

Reuther, James↗
NASA NTRS1993

Comparison of dynamic height variations in the tropical Atlantic during 1987-1989 as viewed in sections hydrography and Geosat altimetry

During the 1980s the Soviet Union conducted a series of 25 hydrographic cruises in the tropical Atlantic under the Sections (acronym for Energetically Active Zones of the Ocean and Climate Variability) program. As one of its objectives, Sections has produced the most extensive hydrographic data set ever collected in the tropical Atlantic. Here we examine dynamic topography from seven cruises during the years 1987 through 1989 and compare them to corresponding maps of sea level from the Geosat satellite mission. The two independent estimates of sea level variation are well correlated, with the sea level fields explaining approximately half of the variance of dynamic height fields. There is a seasonal variation to the agreement, so that the correlation increases somewhat in summer and fall, as the variability of the height field increases. Wavenumber spectra of both dynamic height and sea level are quite red for wavelengths greater than 400-600 km. Smaller wavelengths cannot be resolved by the hydrographic sampling program. A comparison of the dynamic height and sea level spectra indicates that sea level has somewhat less variability than dynamic height at all wavenumbers.

Carton, James A.↗
NASA NTRS2021

Compact Augmented Spark Igniters for Liquid Rocket Engines

The objective of this dissertation is to characterize augmented spark impinging (torch) igniters. Torch igniters inject propellants into a small prechamber where a spark igniter imparts the activation energy to initiate combustion. The flame stabilizes within the prechamber and exits into the main combustion chamber where it ignites the main flow directly or by mixing with a coaxial flow to create a secondary flame. The underlying phenomena that promote reliable ignition are multifaceted and required a broad scope of investigations. Initial efforts focused on spark discharges across an annular geometry. Gaseous propellent projects the electrical arc toward the prechamber, where the arc and the thermal energy is imparted to the surrounding fluid then meets with a combustible mixture. Discharges are parametrically examined against pressure, spark-gap size, and exciter types to emulate transient engine startup. The energy imparted to the flow, approximate velocity, and distance traveled by the exhaust plume are determined. Spark quenching (no energy deposition) and its effects on perceived versus actual measurements are shown. Results outline the prechamber target zone where a combustible mixture is necessary for ignition. Next, three-dimensional, time-accurate, and nonreacting computational fluid dynamics simulations assess effects of geometric and mass flow differences on the size, location, and composition of a combustible mixture. Fluid dynamic phenomena are elucidated. Correlations between nondimensional variables are found. Results are coupled with a conservation-law analysis to produce novel objective functions. Lastly, a full-scale, modular torch igniter is tested. Injector sizes, mixture ratios, and injector momentum ratios are systematically varied to map ignition probability and ignition delay. Results show the importance of local mixture ratios, provide injector momentum ratios guidelines, and demonstrate reliable ignition of core mixture ratios typically outside flammability limits. The deliverable of this project is not a torch igniter or derivative thereof. Instead, this dissertation produces novel objective-functions for igniter design. These equations are functions of variables that may be constrained by engine-level requirements, physics, and experimental results from this work. They provide a foundation for future igniter design and will minimize iterative processes from the design cycle for next-generation ignition hardware.

Torch Igniter↗
NASA NTRS2013

Saturns Thermal Emission at 2.2-cm Wavelength as Imaged by the Cassini RADAR Radiometer

We present well-calibrated, high-resolution maps of Saturn's thermal emission at 2.2-cm wavelength obtained by the Cassini RADAR radiometer through the Prime and Equinox Cassini missions, a period covering approximately 6 years. The absolute brightness temperature calibration of 2% achieved is more than twice better than for all previous microwave observations reported for Saturn, and the spatial resolution and sensitivity achieved each represent nearly an order of magnitude improvement. The brightness temperature of Saturn in the microwave region depends on the distribution of ammonia, which our radiative transfer modeling shows is the only significant source of absorption in Saturn's atmosphere at 2.2-cm wavelength. At this wavelength the thermal emission comes from just below and within the ammonia cloud-forming region, and yields information about atmospheric circulations and ammonia cloud-forming processes. The maps are presented as residuals compared to a fully saturated model atmosphere in hydrostatic equilibrium. Bright regions in these maps are readily interpreted as due to depletion of ammonia vapor in, and, for very bright regions, below the ammonia saturation region. Features seen include the following: a narrow equatorial band near full saturation surrounded by bands out to about 10deg planetographic latitude that demonstrate highly variable ammonia depletion in longitude; narrow bands of depletion at -35deg latitude; occasional large oval features with depleted ammonia around -45deg latitude; and the 2010-2011 storm, with extensive saturated and depleted areas as it stretched halfway around the planet in the northern hemisphere. Comparison of the maps over time indicates a high degree of stability outside a few latitudes that contain active regions.

composition↗
NASA NTRS1993

Molecular catastrophes and circumstellar SiO masers

Understanding the complex SiO maser regions of highly evolved stars can be improved through multiwavelength studies of 'premaser' stars, such as M0-M4 giants and semiregular variables, which can be placed on normal H-R diagrams unlike most of the OH-IR stars. I argue that SiO masers are a key part of the transformation of hot stellar plasma into cold circumstellar silicate dust, in the outflows from evolved, oxygen rich stars. Evidence for this statement rests on the following: (1) red giant mass loss originates in a stochastic, amsotropic manner; (2) SiO maser maps of Miras and red supergiants show numerous maser spots within a few stellar radii; (3) molecules and dust naturally form in a cooling outflow; (4) the IRAS Low Resolution Spectrometer provided evidence for diverse and variable 10 micron silicate features in Miras, and these shapes correlate well with the proposed maser chronology, suggesting a formation and annealing sequence. The theory for the occurrence of SiO masers involving thermal instability, related 'new' physics, recent calculations and a prediction are discussed.

Stencel, Robert E.↗
NASA NTRS1975

Hydrazine engine plume contamination mapping

Instrumentation for the measurement of plume exhaust specie deposition rates were developed and demonstrated. The instruments, two sets of quartz crystal microbalances, were designed for low temperature operation in the back flow and variable temperature operation in the core flow regions of an exhaust plume. These quartz crystal microbalances performed nominally, and measurements of exhaust specie deposition rates for 8400 number of pulses for a 0.1-lb monopropellant thruster are reported.

Chirivella, J. E.↗
DOE OSTI2023

Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator

Here, in the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial differential equations (PDEs), including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice. Instead, inspired by the idea of operator learning, this work leverages the Fourier neural operator (FNO) to effectively learn the frequency domain seismic wavefields under the context of variable velocity models. We also propose a new framework paralleled FNO (PFNO) for efficiently training the FNO-based solver given multiple source locations and frequencies. Numerical experiments demonstrate the high accuracy of both FNO and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore, the cross-dataset generalization test verifies that PFNO adapts to out-of-distribution velocity models. Finally, PFNO admits higher computational efficiency on large-scale testing datasets than the traditional finite-difference method. The aforementioned advantages endow the FNO-based solver with the potential to build powerful models for research on seismic waves.

58 GEOSCIENCES↗
DOE OSTI2024

Large Ensemble Exploration of Global Energy Transitions Under National Emissions Pledges

Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long-term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO 2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO 2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Grad–Shafranov equilibria via data-free physics informed neural networks

A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. In this paper, we explore a method for solving the Grad–Shafranov equation by using physics-informed neural networks (PINNs). For PINNs, we optimize neural networks by directly minimizing the residual of the partial differential equation as a loss function. We show that PINNs can accurately and effectively solve the Grad–Shafranov equation with several different boundary conditions, making it more flexible than traditional solvers. This method is flexible as it does not require any mesh and basis choice, thereby streamlining the computational process. We also explore the parameter space by varying the size of the model, the learning rate, and boundary conditions to map various tradeoffs such as between reconstruction error and computational speed. Additionally, we introduce a parameterized PINN framework, expanding the input space to include variables such as pressure, aspect ratio, elongation, and triangularity in order to handle a broader range of plasma scenarios within a single network. Parameterized PINNs could be used in future work to solve inverse problems such as shape optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗