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DOE OSTI2025

DELVE-DEEP Survey: The Faint Satellite System of NGC 55

We report the first comprehensive census of the satellite dwarf galaxies around NGC 55 (2.1 Mpc) as a part of the DECam Local Volume Exploration DEEP (DELVE-DEEP) survey. NGC 55 is one of four isolated, Magellanic analogs in the Local Volume around which DELVE-DEEP aims to identify faint dwarfs and other substructures. We employ two complementary detection methods: one targets fully resolved dwarf galaxies by identifying them as stellar overdensities, while the other focuses on semiresolved dwarf galaxies, detecting them through shredded unresolved light components. As shown through extensive tests with injected galaxies, our search is sensitive to candidates down to M V ≲ −6.6 and surface brightness μ ≲ 28.5 mag arcsec 2 , and ∼80% complete down to M V ≲ −7.8. We do not report any new confirmed satellites beyond two previously known systems, ESO 294–010 and NGC 55-dw1. We construct the satellite luminosity function of NGC 55 and find it to be consistent with the predictions from cosmological simulations. As one of the first complete luminosity functions for a Magellanic analog, our results provide a glimpse of the constraints on low-mass-host satellite populations that will be further explored by upcoming surveys, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time.

Medoff, Jonah [Univ. of Chicago, IL (United States↗

DOE OSTI2026

The SPT-deep Cluster Catalog: Sunyaev–Zel’dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees

We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$^{2}$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 μK-arcmin at 95, 150, and 220 GHz, respectively. Candidates are selected via the thermal Sunyaev–Zel’dovich (SZ) effect with a minimum significance of ξ = 4.0, resulting in a catalog of purity ∼89%. Optical data from the Dark Energy Survey and infrared data from the Spitzer Space Telescope are used to confirm 442 cluster candidates. The clusters span 0.12 < z ≲ 1.8 and 1.0 × 10$^{14}$M$_{⊙}$/h$_{70}$ < M$_{500c}$ < 8.7 × 10$^{14}$M$_{⊙}$/h$_{70}$. The sample’s median redshift is 0.74, and the median mass is 1.7 × 10$^{14}$M$_{⊙}$/h$_{70}$; these are the lowest median mass and highest median redshift of any SZ-selected sample to date. We assess the effect of infrared emission from cluster member galaxies on cluster selection by performing a joint fit to the infrared dust and tSZ signals by combining measurements from SPT and overlapping submillimeter data from Herschel/SPIRE. We find that at high redshift (z > 1), the tSZ signal is reduced by $17.9_{−3.2}^{+3.8}$%$(3.8_{−0.7}^{+0.9}$%$)$ at 150 GHz (95 GHz) due to dust contamination. We repeat our cluster finding method on dust-nulled SPT maps and find the resulting catalog is consistent with the nominal SPT-Deep catalog, suggesting dust contamination does not significantly impact the SPT-Deep selection function; we attribute this lack of bias to the inclusion of the SPT 220 GHz band.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Shallow- and deep-convection characteristics in the greater Houston, Texas, area using cell tracking methodology

The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Weather radars have been traditionally used to provide various convective-cloud characteristics. Here, we analyzed climatological convective-cell radar characteristics to obtain and assess the diurnal cycles of three convective-cell types – shallow, modest deep, and vigorous deep convective cells – that formed in the greater Houston area, using the National Weather Service radar from Houston, Texas, and a multi-cell identification and tracking algorithm. The examined dataset spans 4 years (2018–2021) and covers the warm-season months (June to September) in those years. The analysis showed clear diurnal cycles in cell initiation (CI) consistent with the sea breeze circulation and showed diurnal and normalized lifetime relationships in cell evolution parameters (e.g., maximum reflectivity, echo-top height, Geostationary Operational Environmental Satellite-16 (GOES-16) channel 13 brightness temperature, and the height of maximum reflectivity). The cell evolution is well represented by relationships between (1) the height and value of the maximum radar reflectivity, (2) the minimum GOES-16 channel 13 brightness temperature and the maximum vertically integrated liquid, (3) the maximum reflectivity and columnar-average reflectivity, and (4) the echo-top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. GOES-16 Aerosol Optical Depth values are also used as a proxy for cell initiation aerosol concentrations to investigate any potential relationships between initiation location and aerosol concentration. Overall, no significant relationships between initiation location and aerosol concentration were found for the three cell types investigated, but there are some minor differences in the pre-CI aerosol optical depth for vigorous deep convective cells.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Legacy Survey of Space and Time Data Preview 2: deep_coadd dataset type

We present Rubin Data Preview 2 (DP2), the second data preview from the NDF-DOE Vera C. Rubin Observatory. Data Preview 2 (DP2) comprises coadds, detection catalogs, and ancillary data products; and when fully released will also include single-epoch images and difference images. DP2 is derived from observations acquired by the LSST Science Camera (LSSTCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile, primarily during the on-sky commissioning campaign between 2025-04-16 and 2025-09-21, supplemented by observations taken between 2025-10-25 and 2026-01-06 that overlap the commissioning footprint. The DP2 footprint comprises the Science Validation wide-area survey, five Deep Drilling Fields, and a number of targeted small-field regions, including Trifid and Lagoon, Prawn, M49, and New Horizons, all observed as part of the Rubin First Look campaign. Each field was imaged in up to six broad photometric bands, ugrizy, and coadded to produce deep imaging covering an estimated 3,000 deg2. The addition of single-visit-only areas expands the total DP2 footprint to an estimated 15,000 deg2, with coverage in at least one filter. The median per-visit PSF FWHM across the wide-area survey ranges from 1.17 arcsec in the z band to 1.26 arcsec in g and r bands. The deepest field, reaches estimated coadded 5σ depths of u=26 mag, g=26.8 mag, r=26.3 mag, i=26.1 mag, z=25.3 mag, y=23.9 mag. Based on a roughly five-month primary observing baseline and covering only part of the eventual LSST footprint, DP2's area, depth, and multiband coverage nonetheless support a broad range of early science investigations ahead of LSST Data Release This dataset is a subset of the full data release consisting of the deep_coadd dataset type. These are the combination of multiple processed, calibrated, and background- subtracted images, for a patch of sky, for each of the six filters. This release contains 925,460 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

EVs@Scale High-Power Charging (HPC) Pillar Deep-Dive Technical Meeting

EVs@Scale Lab Consortium is addressing challenges, developing solutions, and enabling technologies for transportation electrification ecosystem. The consortium has five research pillars one of which focuses on high power charging (HPC). HPC pillar brings together hardware and software expertise, capabilities, and facilities related to high power EV charging, charge management, and grid integration. Deep-dive technical meetings are organized twice a year and provides an opportunity for more industry engagement and technical feedback for the national labs throughout the project lifecycle. High-Power Charging pillar has two active projects: (i) Next-Gen Profiles (NGP) and (ii) High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP). This presentation summarizes the progress made in both projects during the past six months focusing on specific topics. There will be two deep-dive technical meetings twice a year. Every deep-dive meeting will focus on different aspects of the project progress.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods

Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method focuses on both zero-dimensional (0D) and two-dimensional (2D) data, to explore how the UQ methods function for different data dimensionalities. We investigate uncertainty injected on the input and output variables and include a method to propagate uncertainty in the case of input uncertainty so that we can compare the predicted aleatoric uncertainty to the known values. We experiment with three levels of noise. The aleatoric uncertainty predicted across all models and experiments scales with the injected noise level. However, the predicted uncertainty is miscalibrated to $\rm{std}(\sigma_{\rm al})$ with the true uncertainty for half of the DE experiments and almost all of the DER experiments. The predicted uncertainty is the least accurate for both UQ methods for the 2D input uncertainty experiment and the high-noise level. While these results do not apply to more complex data, they highlight that further research on post-facto calibration for these methods would be beneficial, particularly for high-noise and high-dimensional settings.

Nevin, Rebecca↗

DOE OSTI2024

Nov. 2024 EVs@Scale High-Power Charging Deep Dive Technical Meeting

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗

DOE OSTI2024

Breakthrough Conductivity Enhancement in Deep Eutectic Solvents via Grotthuss–Type Proton Transport

There is an increasing demand for the development of ion-conducting electrolytes for energy storage systems. Much attention is directed toward deep eutectic solvents as potential candidates. In the search for highly conductive systems, the possibility of designing deep eutectic solvents with Grotthuss-type proton transport is widely overlooked. Herein, ethaline, a mixture of choline chloride and ethylene glycol is used in a 1:2 molar ratio, to induce a significant conductivity increase with the addition of water and sulfuric acid (H 2 SO 4 ). The achieved breakthrough conductivity is analyzed experimentally and simulated with ab initio molecular dynamics (AIMD). At sufficient water content, an H-bonding network is formed that leads to a significant breakthrough conductivity based on H 2 SO 4 -derived proton transfer following the long-established Grotthuss proton transport mechanism. This result is substantiated by the positive deviation from the ideal KCl line in the Walden plot. Specifically, the data series positioned above the reference line indicates a Grotthuss mechanism in action. The AIMD simulations demonstrate proton transfer between water and ethylene glycol, supported by simulation frames captured at various times.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux (CHF) data used for the 2006 Groeneveld lookup table. To compare with traditional methods, a fine-tuned deep neural network (DNN) regression model was evaluated on the same dataset. Both models achieved small mean absolute relative errors, with the CVAE showing more favorable results. Uncertainty quantification (UQ) was performed using repeated CVAE sampling and DNN ensembling. The DNN ensemble improved performance over the baseline, while the CVAE maintained consistent results with less variability and higher confidence. Both models achieved small errors inside and outside the training domain, with slightly larger errors outside. Altogether, the CVAE performed better than the DNN in predicting CHF and exhibited better uncertainty behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗

DOE OSTI2025

Geometry-aware framework for deep energy method: An application to structural mechanics with hyperelastic materials

Here, in this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Impact of carbon dioxide removal technologies on deep decarbonization: EMF37 MARKAL–NETL modeling results

Here this paper examines the MARKAL-NETL modeling results for the Energy Modeling Forum Study on Deep Decarbonization & High Electrification Scenarios for North America (EMF 37) with specific focus on carbon dioxide removal (CDR) technologies and opportunities under different scenarios guidelines, policies, and technological advancements. The results demonstrate that CDR, such as, bioenergy with carbon capture and storage (BECCS), direct air capture (DAC) and afforestation are key negative emission technologies in deep decarbonization scenarios in the U.S. are accounted for about 70% of annually avoided carbon dioxide emissions (CO 2 ) by 2050, or more than 2 billion tons of CO 2 (GtCO 2 ). The potential scale of CDR and its impact on the energy system depends on energy supply and demand technologies advancement and their costs, the level of end-use sectors electrification, availability and costs of CDR. Results show that the carbon prices are substantially lower if the advanced technologies available, particularly, in carbon management scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Experimental investigation of flow distribution in enhanced geothermal systems with deep eutectic solvent

Geothermal energy has been recognized as a valuable alternative to fossil fuels and nuclear power, as it is renewable and reliable. Enhanced Geothermal Systems (EGSs) have the potential to expand geothermal energy production by enabling access to previously untapped geothermal resources. Geothermal short-circuiting poses a significant challenge to EGS development, leading to reduced heat extraction. Deep Eutectic Solvent (DES) exhibits favorable thermal and rheological properties, making it a candidate for geothermal applications. Here, this paper examines Choline Chloride-Based Deep Eutectic Solvent (DES) as a working fluid in geothermal applications and its potential to mitigate geothermal short-circuiting. Hydraulic experiments using a dual fracture flow loop were conducted at high temperatures. The results showed that DES exhibited higher differential pressure behavior compared to water. Flow distribution results revealed that DES enhances flow allocation within the small fracture, particularly when a temperature difference exists between fractures. Specifically, DES increased flow distribution by an average of 11% when the temperature difference was 85°C, and by 13% when the difference was 45°C, relative to water. These findings suggest that DES responds to thermal fracture differences, making it a potential remedy to address geothermal short-circuiting.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗

DOE OSTI2025

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks

Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work, we demonstrate that the failure of plain physics-informed neural networks arises from the significant discrepancy in the convergence rate of residuals at different training points, where the slowest convergence rate dominates the overall solution convergence. Based on these observations, we propose a pointwise adaptive weighting method that balances the residual decay rate across different training points. The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics-informed neural networks and physics-informed deep operator networks. In conclusion, through extensive numerical results we demonstrate that our proposed approach of balanced residual decay rates offers several advantages, including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.

Balanced convergence rate↗

DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗

DOE OSTI2024

Probing the potential of type V Deep eutectic solvents as sustainable electrolytes

The increasing interest within the scientific community in environmentally friendly solvents has led to a focus on Deep Eutectic Solvents (DES), which have natural components. DES are viewed as alternatives to traditional organic solvents and have the potential to be used as electrolytes. For the first time, transport properties of four Type V Deep Eutectic Salt Solutions (DESS) were accessed to investigate the potential of this technology, selecting precursors ranked as excellent in Eco-Scale metrics. The DESS were composed of terpene and trioctylphosphine oxide (TOPO), and varying concentrations of lithium bis(trifluoromethane)sulfonimide (LiTFSI), and their properties were assessed through self-diffusion, viscosity, density, and conductivity measurements. While Type V DESS are capable of dissolving significant amounts of LiTFSI (up to 30 % molar), their ionic conductivity is low, with values ranging from 3.6·10 –3 to 9.3·10 –2 mS·cm –1 at 25 °C, thus limiting their suitability as electrolytes, for instance, for lithium-ions batteries applications. Similar diffusion coefficients for Li + and TFSI – ions suggest the formation of long-lived ion pairs moving as a neutral species. As a result, future research aims to introduce additives to disrupt contact ion pairs and enhance transport properties, leveraging the sustainable appeal of DES and their use in advanced energy storage technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Intense formation of secondary ultrafine particles from Amazonian vegetation fires and their invigoration of deep clouds and precipitation

New particle formation (NPF) in fire smoke is thought to be unlikely due to large condensation and coagulation sinks that scavenge molecular clusters. We analyze aircraft measurements over the Amazon and find that fires significantly enhance NPF and ultrafine particle (UFP < 50 nm diameter) numbers compared to background conditions, contrary to previous understanding. We identify that the nucleation of dimethylamine with sulfuric acid, which is aided by the formation of extremely low volatility organics in biomass-burning smoke, can overcome the large condensation and coagulation sinks and explain aircraft observations. We show that freshly formed clusters rapidly grow to UFP sizes through biomass-burning secondary organic aerosol formation, leading to a 10-fold increase in UFP number concentrations. Here, we find a contrasting effect of UFPs on deep convective clouds compared to the larger particles from primary emissions for the case investigated here. UFPs intensify the deep convective clouds and precipitation due to increased condensational heating, while larger particles delay and reduce precipitation.

54 ENVIRONMENTAL SCIENCES↗