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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Finding quadruply imaged quasars with machine learning – I. Methods

ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Unraveling the Orbital Physics in a Canonical Orbital System KCuF 3

We explore the existence of the collective orbital excitations, orbitons, in the canonical orbital system KCuF 3 using the Cu L 3 -edge resonant inelastic x-ray scattering. We show that the nondispersive high-energy peaks result from the Cu 2+ dd orbital excitations. These high-energy modes display good agreement with the ab initio quantum chemistry calculation, indicating that the d d excitations are highly localized. At the same time, the low-energy excitations present clear dispersion. They match extremely well with the two-spinon continuum following the comparison with Müller ansatz calculations. The localized d d excitations and the observation of the strongly dispersive magnetic excitations suggest that the orbiton dispersion is below the resolution detection limit. Our results can reconcile with the strong local Jahn-Teller effect in KCuF 3 , which predominantly drives orbital ordering.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Analysis of furnace contamination on superconducting radio frequency niobium using secondary-ion mass spectrometry

Detection of surface contamination on niobium materials used in superconducting radio frequency (SRF) applications is difficult due to quantitative sensitivity and near-atomic depth resolution needed. Inspection of samples known to have experienced surface contamination was found to have inconsistent nitride coverage after high-temperature nitrogen gas exposure (“doping”). Here we compare contaminating species found on samples treated in several different vacuum furnaces, both “exposed” directly in the chamber and “protected” by containment shielding from evaporative sources with “furnace caps.” Typically, furnace caps are used to impede contamination from reaching the interior surface of cavities during the high-temperature vacuum bake that immediately precedes exposure to nitrogen gas. Although, to date, little is known about the effectiveness of these caps, SIMS results showed that they were effective in limiting contamination arising from the furnace environment. Inspection of sample surfaces by SEM showed a lack of nitrides present on contaminated specimens. TEM with energy dispersive spectroscopy performed on these samples revealed that a carbon-rich layer now existed, indicating that a relatively high contaminant load prevents the nucleation and growth of surface nitrides, while thus inhibiting interstitial nitrogen uptake. Except in extreme cases, subsequent removal of the top several micrometers of the surface via electropolishing appears to effectively eliminate any strong influence on the subsequent SRF cavity performance. With the absence of furnace cleaning, carbon contamination was found to be nearly 10× higher for protected nitrogen-doped and electropolished samples, with minimal metallic contamination detected for both processes. SIMS analysis was also performed to compare the cleanliness of samples fully prepared by such nitrogen “doping” with those prepared by a related process, involving the dissolution of niobium surface oxide and diffusion of oxygen into the surface. This oxygen doping or alloying process offers attractive advantages.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. As a result, the successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

42 ENGINEERING↗
DOE OSTI2022

On the provenance of GEMS, a quarter century post discovery

The provenance of GEMS (glass with embedded metal and sulfides) in cometary type interplanetary dust particles is investigated using analytical scanning transmission electron microscopy and secondary ion mass spectrometry. We review the current state of knowledge and closely examine the densities, elemental compositions and distributions, iron oxidation states, and isotopic compositions of a subset of GEMS in chondritic porous interplanetary dust. We find that GEMS are underdense with estimated densities that are 35–65% of compositionally equivalent crystalline aggregates. GEMS low densities result in a lower contribution to the bulk compositions of IDPs than has been assumed based on their volume fraction. We also find that element/Si ratios, assumed to be primary (indigenous), are instead perturbed by contamination and secondary alteration, including pulse heating during atmospheric entry. Fe in pyrrhotite inclusions was oxidized and Mg, S, Ca, and Fe were depleted relative to lithophile Al and Si, resulting in reduction in element/Si ratios. Because they trap outgassing elements, Fe-rich oxide rims that formed on the surface of GEMS are serendipitous “witness plates” to the changes in composition that accompany atmospheric entry. As a result of alteration, GEMS elemental compositions cannot reliably inform about their provenance. Except for highly anomalous oxygen isotope ratios measured in some large GEMS grains that indicate a contribution from circumstellar dust, oxygen isotope compositions are generally poor indicators of provenance. Prior work indicates that most GEMS fall close to the terrestrial oxygen isotope composition, which, however, does not exclude a presolar interstellar origin. Nitrogen isotopic compositions are more diagnostic. Elevated 15 N/ 14 N ratios indicate that GEMS accreted in conjunction with formation of organic matter by ion–molecule reactions in a cold (<50 K) presolar environment like the extreme outer nebula or interstellar medium. Considering all observations, we conclude that GEMS are most likely processed interstellar silicates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A sharp interface Lagrangian-Eulerian method for rigid-body fluid-structure interaction

Herein this paper introduces a sharp interface method to simulate fluid-structure interaction (FSI) involving rigid bodies immersed in viscous incompressible fluids. The capabilities of this methodology are benchmarked using a range of test cases and demonstrated using large-scale models of biomedical FSI. The numerical approach developed herein, which we refer to as an immersed Lagrangian-Eulerian (ILE) method, integrates aspects of partitioned and immersed FSI formulations by solving separate momentum equations for the fluid and solid subdomains, as in a partitioned formulation, while also using non-conforming discretizations of the dynamic fluid and structure regions, as in an immersed formulation. A simple Dirichlet-Neumann coupling scheme is used, in which the motion of the immersed solid is driven by fluid traction forces evaluated along the fluid-structure interface, and the motion of the fluid along that interface is constrained to match the solid velocity and thereby satisfy the no-slip condition. To develop a practical numerical method, we adopt a penalty approach that approximately imposes the no-slip condition along the fluid-structure interface. In the coupling strategy, a separate discretization of the fluid-structure interface is tethered to the volumetric solid mesh via stiff spring-like penalty forces. Our fluid-structure coupling scheme relies on an immersed interface method (IIM) for discrete geometries, which enables the accurate determination of both velocities and stresses along complex internal interfaces. Numerical methods for FSI can suffer from instabilities related to the added mass effect, but computational tests indicate that the methodology introduced here remains stable for selected test cases across a broad range of solid-fluid mass density ratios, including extremely small, nearly equal, equal, and large density ratios. Biomedical FSI demonstration cases include results obtained using this method to simulate the dynamics of a bileaflet mechanical heart valve in a pulse duplicator, and to model transport of blood clots in a patient-averaged anatomical model of the inferior vena cava.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), enabling exceptionally high spatial resolution that benefits both clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field ($B^{+}_{1}$) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate $B^{+}_{1}$ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. Although these approaches show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. In this paper, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000 ​× ​speed-up compared to the traditional MLS method. In the initial phase, we employ random-initialized Adaptive Moment Estimation (Adam) to derive the desired reference shimming weights from multi-channel $B^{+}_{1}$ fields. Next, we train a Residual Network (ResNet) to map $B^{+}_{1}$ fields directly to the ultimate RF shimming outputs, incorporating the confidence parameter into its loss function. Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. Comparative evaluations with standard MLS optimization underscore notable gains in both processing speed and predictive accuracy, which indicates that our technique shows a promising solution for addressing persistent inhomogeneity challenges.

Deep learning↗
DOE OSTI2023

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

One-dimensionality signature in optical conductivity of heavy-fermion CeIr 3 B 2

In low dimensions, the combined effects of interactions and quantum fluctuations can lead to dramatically new physics distinct from that existing in higher dimensions. Here, we investigate the electronic and optical properties of CeIr 3 ⁢B 2 , a quasione-dimensional (1D) Kondo lattice system, using ab initio calculations. The Ce atoms in the hexagonal crystal structure form 1D chains along the c axis, with extremely short Ce-Ce distances. The quasi-1D nature of the crystal structure is well reflected in its electronic structure. Extremely flat bands emerge within the ab plane of the Brillouin zone, yielding sharp optical transitions in the corresponding optical conductivity. Further, our calculations indicate that these prominent peaks in the optical conductivity provide a clear signature of quasi-1D heavy fermion systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Neutrino oscillations in supernovae: Angular moments and fast instabilities

Recent theoretical work indicates that the neutrino radiation in core-collapse supernovae may be susceptible to flavor instabilities that set in far behind the shock, grow extremely rapidly, and have the potential to profoundly affect supernova dynamics and composition. Here we analyze the nonlinear collective oscillations that are prefigured by these instabilities. We demonstrate that a zero crossing in $n_{ν_e}$ – $n_{\overline ν_e}$ as a function of propagation angle is not sufficient to generate instability. Our analysis accounts for this fact and allows us to formulate complementary criteria. Using FORNAX simulation data, we show that fast collective oscillations qualitatively depend on how forward peaked the neutrino angular distributions are.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Historical and Future Windstorms in the Northeastern United States

Large-scale windstorms represent an important atmospheric hazard in the Northeastern US (NE) and are associated with substantial socioeconomic losses. Regional simulations performed with the Weather Research and Forecasting (WRF) model using lateral boundary conditions from three Earth System Models (ESMs: Geophysical Fluid Dynamics Laboratory (GFDL), Hadley Centre Global Environment Model (HadGEM) and Max Planck Institute (MPI)) are used to quantify possible future changes in windstorm characteristics and/or changes in the parent cyclone types responsible for windstorms. WRF nested within MPI ESM best represents important aspects of historical windstorms and the cyclone types responsible for generating windstorms compared with a reference simulation performed with the ERA-Interim reanalysis for the historical climate. The spatial scale and frequency of the largest windstorms in each simulation defined using the greatest extent of exceedance of local 99.9th percentile wind speeds (U > U999) plus 50-year return period wind speeds (U50,RP) do not exhibit secular trends. Projections of extreme wind speeds and windstorm intensity/frequency/geolocation and dominant parent cyclone type associated with windstorms vary markedly across the simulations. Only the MPI nested simulations indicate statistically significant differences in windstorm spatial scale, frequency and intensity over the NE in the future and historical periods. This model chain, which also exhibits the highest fidelity in the historical climate, yields evidence of future increases in 99.9th percentile 10 m height wind speeds, the frequency of simultaneous U > U999 over a substantial fraction (5–25%) of the NE and the frequency of maximum wind speeds above 22.5 ms−1. These geophysical changes, coupled with a projected doubling of population, leads to a projected tripling of a socioeconomic loss index, and hence risk to human systems, from future windstorms.

Pryor, Sara C. (ORCID:0000000348473440)↗
DOE OSTI2020

Atmospheric teleconnection processes linking winter air stagnation and haze extremes in China with regional Arctic sea ice decline

Abstract. Recent studies suggested significant impacts of boreal cryosphere changes on wintertime air stagnation and haze pollution extremes in China. However, the underlying mechanisms of such a teleconnection relationship remains unclear. Here we use the Whole Atmosphere Community Climate Model (WACCM) to investigate dynamic processes leading to atmospheric circulation and air stagnation responses to Arctic sea ice changes. We conduct four climate sensitivity experiments by perturbing sea ice concentrations (SIC) and corresponding sea surface temperature (SST) in autumn and early winter over the whole Arctic and three subregions in the climate model. The results indicate distinct responses in circulation patterns and regional ventilation to the region-specific Arctic changes, with the largest increase of both the probability (by 132 %) and the intensity (by 30 %) of monthly air stagnation extremes being found in the experiment driven by SIC and SST changes over the Pacific sector of the Arctic (the East Siberian and Chukchi seas). The increased air stagnation extremes are mainly driven by an amplified planetary-scale atmospheric teleconnection pattern that resembles the negative phase of the Eurasian (EU) pattern. Dynamical diagnostics suggest that convergence of transient eddy forcing in the vicinity of Scandinavia in winter is largely responsible for the amplification of the teleconnection pattern. Transient eddy vorticity fluxes dominate the transient eddy forcing and produce a barotropic anticyclonic anomaly near Scandinavia and wave train propagation across Eurasia to the downstream regions in East Asia. The piecewise potential vorticity inversion analysis reveals that this long-range atmospheric teleconnection of Arctic origin takes place primarily via the middle and upper troposphere. The anomalous ridge over East Asia in the middle and upper troposphere worsens regional ventilation conditions by weakening monsoon northwesterlies and enhancing temperature inversions near the surface, leading to more and stronger air stagnation and pollution extremes over eastern China in winter. Ensemble projections based on state-of-the-art climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) corroborate this teleconnection relationship between high-latitude environmental changes and midlatitude weather extremes, though the tendency and magnitude vary considerably among each participating model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Magnetotransport and electronic structure of the antiferromagnetic semimetal YbAs

A number of rare-earth monopnictides have topologically nontrivial band structures together with magnetism and strong electronic correlations. In order to examine whether the antiferromagnetic (AFM) semimetal YbAs (TN=0.5 K) exhibits such a scenario, we have grown high-quality single crystals using a flux method, and characterized the magnetic properties and electronic structure using specific heat, magnetotransport, and angle-resolved photoemission spectroscopy (ARPES) measurements, together with density functional theory (DFT) calculations. Both ARPES and DFT calculations find no evidence for band inversions in YbAs, indicating a topologically trivial electronic structure. From low-temperature magnetotransport measurements, we map the field-temperature phase diagram, where we find the presence of a field stabilized phase distinct from the AFM phase at low temperatures. Additionally, an extremely large magnetoresistance (XMR) for both YbAs and the nonmagnetic counterpart LuAs is also observed, which can consistently be accounted for by the presence of electron-hole compensation. Moreover, an angle-dependent study of the Shubnikov-de Haas effect oscillations reveals very similar Fermi surfaces between YbAs and LuAs, with light effective masses down to at least 0.5 K, indicating that the Yb-4f electrons are well localized, and do not contribute to the Fermi surface. However, the influence of the localized Yb-4f electrons on the magnetotransport of YbAs can be discerned from the distinct temperature dependence of the XMR compared to that of LuAs, which we attribute to the influence of short-ranged spin correlations that appear well above TN.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Northern Hemisphere Extratropical Cyclone Activity in the Twentieth Century Reanalysis Version 3 (20CRv3) and Its Relationship with Continental Extreme Temperatures

In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.

20CRv3↗
DOE OSTI2023

Extreme Weather Events and PV Systems Impact

The impact of extreme weather events on PV performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the National Renewable Energy Laboratory's (NREL) PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1 % of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60 % loss, showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events above 90 kilometer/hour, by comparing PLR before and after these events. Similarly, hail events caused higher PLR for hail sizes of and above 25 mm indicating that more stringent hail testing for PV modules is required. In addition, very high snow loads may also cause increased PLRs, but more data are required to better quantify the impact. These findings illustrate the substantial risk extreme weather events pose short-and long-term to fielded PV installations.

extreme weather↗
DOE OSTI2022

Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations

Abstract. Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol–cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the northern slope of Alaska at Oliktok Point (OLI), the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the high Arctic Ocean, and the Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit – Aerosol Cloud Experiment (ICECAPS-ACE) project at the NSF (National Science Foundation) Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LESs) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously – effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LESs are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case's observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary drivers of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener–Bergeron–Findeisen (WBF) process. The non-drizzling case is also more sensitive to ice-nucleating particle (INP) concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Magnetohydrodynamic Control of Interfacial Degradation in Lithium-Ion Batteries for Fast Charging Applications

Interfacial anodic degradation in graphitic materials under fast charging conditions causes severe performance loss and safety hazard in lithium ion batteries. We present a novel method for minimizing the growth of these aging mechanism by application of an external magnetic field. Under magnetic field, paramagnetic lithium ions experience a magnetohydrodynamic force, which rotates the perpendicularly diffusing species and homogenizes the ionic transport. This phenomenon minimizes the overpotential hotspots at the anode/separator interface, consequently reducing SEI growth, lithium plating, and interfacial fracture. In situ electrochemical measurements indicate an improvement in capacity for lithium cobalt oxide/graphite pouch cell (20 mAh) charged from 1–5 C under an applied field of 1.8 kG, with a maximum capacity gain of 22% at 5C. Post-mortem FE-SEM and EDS mapping shows that samples charged with magnetic field have a reduced lithium deposition at 3C and a complete suppression of interfacial fracture at 5C. At 5C, a 24% reduction in the lithium content is observed by performing XPS on the anodic interfacial film. Finally, fast charging performance under variable magnetic field strengths indicate a saturation behavior in capacity at high fields (>2 kG), thereby limiting the field and consequent energy requirements to obtain maximum capacity gain under extreme conditions.

electromagnetism↗