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At least 649 records · Page 36

Proliferated Resilient Economical Half-Meter Aperture Space Telescopes (PREEMPT)

The PREEMPT LDRD was motivated by a major national need for better space-based imaging systems that are both high performing and affordable for the US Government. Current optical payloads for intelligence, surveillance and reconnaissance (ISR) and space domain awareness (SDA) missions can cost hundreds of millions of dollars and take years to develop, which makes it difficult to build the large constellations required for persistent, world-wide coverage. To address this, the team aimed to advance a different kind of large-aperture (>25 cm) telescope, called a monolithic telescope, in which key optical surfaces are built into a single piece of fused silica. This design greatly reduces payload and spacecraft complexity, the need for precision focus actuators, improves mechanical and thermal robustness, and lowers cost when compared with traditional Cassegrain telescopes that rely on many precisely aligned components. The project focused on five primary thrusts. The first thrust was to advance the concept of a V10, 25 cm, monolithic telescope forward from optical design to flight-ready stage. This was accomplished in partnership with Optimax, who delivered the first test unit in the early stages of the LDRD. The team developed several technologies necessary for this optic to be integrated into a flight demonstration. These include carbon fiber housings, highly detailed structural and thermal models and stress-reducing elastic averaging Hirth groove designs. These technologies resulted in a successful maturation of the optic, which is now slated to fly in late 2026/early 2027 for a demonstration mission. The second thrust was to advance the manufacturability of these optics. In collaboration with NIF’s optical manufacturing shop, we reduced polishing time from 480 hours to 65 hours through the implementation of optimized processes and new tools. The NIF team utilized a conceptual V8 (18 cm) optic to demonstrate this optimization, though it can be applied to the rest of the monolithic optic portfolio. Third, the team developed the first conceptual V20 (50 cm) payload, which is slated to be the next generation of LLNL optical payload systems. A set of structural, dynamic and thermal simulations were performed to identify potential challenges in the future development of this payload. Early-stage simulations suggest the payload is feasible, though thermal management will be the key focus area to maintain optimal performance. Fourth, a non-linear model of Viton was developed, to further enhance the reliability of our structural and dynamic models for future payloads. Viton acts as the primary interface material between the optic and its housing. Lastly, the team focused on successfully displaying the feasibility of using additively manufactured metal composites for optical space payloads. The team successfully demonstrated layer by layer deposition of Al-SiC composites, which have highly tunable structural and coefficient of thermal expansion (CTE) properties. These are crucial for optical payloads because CTE mismatch is one of the causes for degraded optical performance for telescopes in orbit. Overall, the work showed that monolithic telescopes could become a practical, lower-cost path to high-resolution space imaging for both national security and scientific missions.

42 ENGINEERING↗

Molecular Insight into Fluorocarbon Adsorption in Pore Expanded Metal–Organic Framework Analogs

The rapid growth in the global energy demand for space cooling requires the development of more efficient environmental chillers for which adsorption-based cooling systems can be utilized. Here, in this contribution, we explore sorbents for chiller use via a pore-engineering concept to construct analogs of the 1-dimensional pore metal–organic framework MOF-74 by using elongated organic linkers and stereochemistry control. The prepared pore-engineered MOFs show remarkable equilibrium adsorption of the selected fluorocarbon refrigerant that is translated to a modeled adsorption-based refrigeration cycle. To probe molecular level interactions at the origin of these unique adsorption properties for this series of Ni-MOFs, we combined in situ synchrotron X-ray powder diffraction, neutron powder diffraction, X-ray absorption spectroscopy, calorimetry, Fourier transform infrared techniques, and molecular simulations. Our results reveal the coordination of fluorine (of CH2F in R134a) to the nickel(II) open metal centers at low pressures for each Ni-MOF analog and provide insight into the pore filling mechanism for the full range of the adsorption isotherms. The newly designed Ni-TPM demonstrates exceptional R134a adsorption uptake compared to its parent microporous Ni-MOF-74 due to larger engineered pore size/volume. The application of this adsorption performance toward established chiller conditions yields a working capacity increase for Ni-TPM of about 400% from that of Ni-MOF-74, which combined with kinetics directly correlates to both a higher coefficient of performance and a higher average cooling capacity generated in a modeled chiller.

Zheng, Jian↗

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗

Forest Soil Carbon Efflux Evaluation Across China: A New Estimate With Machine Learning

Forest soil respiration (Rs) plays an important role in the carbon balance of terrestrial ecosystems. China's forest occupies a large part of the world's forest. However, due to the lack of integrated observation data and appropriate upscaling methodologies, substantial uncertainties exist in the Rs evaluation, which limits our understanding of the carbon balance. Here, we re-evaluated the total soil carbon effluxes across China by combining field observations from 634 published annual Rs with a machine learning technique (i.e., Random Forest (RF)). Our results revealed that the combination of systematic measurements with the RF model allowed a definite estimate. The average annual Rs was 773.6 g C m -2 yr -1 , ranging from 404.6 to 1,772.3 g C m -2 yr -1 . Total forest soil carbon effluxes amounted to 1.17 Pg C yr -1 in China. Geographically, annual Rs showed a clear spatially increasing trend from northeast to southwest. Forest type is an important factor in determining the soil respiration rate. Bamboo and Evergreen broadleaf forests were higher than other types of forests. In conclusion, these results provide a unique insight into the magnitudes and mechanisms of soil CO 2 emissions in China's forest ecosystems.

58 GEOSCIENCES↗

Validating Intra-Plate Power Distributions in the Advanced Test Reactor

We report that the neutronics software, HELIOS, was validated in 2015 for performing core reload design and safety analysis of the Advanced Test Reactor. However, when HELIOS was benchmarked against historic fission-wire measurements (i.e., zero-power full-core measurements) a statistically resolved calculation-to-measurement bias was discovered. The azimuthal power along each fuel plate computed by HELIOS has consistently shown to under-predict measurements made by fission-wires in historic zero-power tests near the fuel element side-plates. It was hypothesized during the HELIOS software validation work, that this bias is attributable to local moderation in coolant vents in the side-plates axially just above and below the fission-wires on the fuel-plate edges. This work used detailed MCNP and MC21 models of the side-plate vents to test this hypothesis. By comparing the average azimuthal biases between HELIOS, and 2D and 3D MCNP models, and a 3D MC21 model, it was found that the HELIOS azimuthal bias is not due to the measurement.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Geminate exciton fusion fluorescence as a probe of triplet exciton transport after singlet fission

The geminate annihilation of two triplet excitons created by singlet exciton fission is affected by the dimensionality of transport as determined by typically anisotropic triplet exciton mobilities in organic molecular crystals. We analyze this process using a random-walk model where the time dynamics of the geminate annihilation probability is determined by the average exciton hopping times along the crystallographic directions. Here, the model is then applied to the geminate fluorescence dynamics in rubrene, where the main channel for triplet-triplet annihilation is via triplet fusion and subsequent photon emission, and we identify the transitions between transport in one, two, and three dimensions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

PHASE: Personalized Head-based Automatic Simulation for Electromagnetic properties in 7T MRI

Accurate and individualized human head models are becoming increasingly important for electromagnetic (EM) simulations. These simulations depend on precise anatomical representations to realistically model electric and magnetic field distributions, particularly when evaluating Specific Absorption Rate (SAR) within safety guidelines. State of the art simulations use the Virtual Population due to limited public resources and the impracticality of manually annotating patient data at scale. Here, this paper introduces Personalized Head-based Automatic Simulation for EM properties (PHASE), an automated open-source toolbox that generates high-resolution, patient-specific head models for EM simulations using paired T1-weighted (T1w) magnetic resonance imaging (MRI) and computed tomography (CT) scans with 14 tissue labels. To evaluate the performance of PHASE models, we conduct semi-automated segmentation and EM simulations on 15 real human patients, serving as the gold standard reference. The PHASE model achieved comparable global SAR and localized SAR averaged over 10 grams of tissue (SAR-10g), demonstrating its potential as a promising tool for generating large-scale human model datasets in the future. The code and models of PHASE toolbox have been made publicly available: https://github.com/hrlblab/PHASE.

Deep learning↗

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Abstract Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

60 APPLIED LIFE SCIENCES↗

Investigation of Best-Practices and Computationally Inexpensive Radiative Exchange Models for Discrete Element Method Modeling of Aluminosilicate Particles in Concentrating Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrating solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrating solar power environments. In this study, coupled experimental and numerical slump testing of aluminosilicate particles was performed and computationally efficient radiative exchange models were evaluated to establish best-practices for discrete element method models for concentrating solar power environments. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Existing modeling limitations and computationally-efficient multi-modal heat transfer models were evaluated using Aspherix®, a commercial discrete element method software. High-temperature (< 1073 K) slump testing of aluminosilicate particles was performed to investigate the deviation between experimentally-observed and numerically-predicted angles of repose introduced by computation-time reduction practices including the relaxation of the particle elastic modulus and coarse-graining. Coarse-graining is used to use a single modeled particle that is representative of a collection of smaller particles, decreasing the computational cost at the expense of geometric accuracy. Additionally, relaxation of the elastic modulus is used to reduce computational time at the expense of an increased, modeled particle overlap. Prior studies have determined that aluminosilicate particles retain a high elastic modulus at high temperatures (< 1073 K), requiring small simulation timesteps to ensure resolved contact forces resemble appropriate solid mechanics. A parametric study was performed to evaluate the influence of computation time improvements on the deviation between experimental and modeled angle of repose across high temperatures < 1073 K. Additionally, numerical case studies were performed on candidate particle systems at varying porosities and temperatures. These studies were performed to investigate the influence of computationally-efficient radiative-exchange modeling methods coupled to Aspherix® on modeled accuracy and computation time. The recently-developed distance-based approximation was evaluated in estimating radiative exchange between particles and participating surfaces located in close proximity. The distance based approximation was developed to use tabulated estimates of the radiative distribution factor between individual particles and surfaces in close proximity (< 40 particle radii). These methods were expanded to the aluminosilicate particles of interest, including the influence of particle size distributions. To capture radiative exchange between particles and surfaces not in close proximity (> 40 particle radii) and to capture the absorption of directional irradiation from concentrating solar resources, a volumetrically-averaged radiative distribution factor was calculated between the modeled granular flow and surfaces using Monte Carlo ray-tracing for participating media. Volume-averaged absorption and scattering coefficients were predicted using a volumetric discretization of the modeled domain with monodisperse approximations based on geometric optics and experimentally-determined scattering phase functions for aluminosilicate particles.

14 SOLAR ENERGY↗

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

Automated and Accelerated Continuum Model Development for Electrochemical Systems (Abbreviated Report)

Despite the availability of computational resources and advancements in numerical computing capabilities, the multiscale models core to understanding, predicting the behaviors of, and designing energy and environmental systems involving porous media are still 1.) developed through by-hand derivations and 2.) limited by many methodological assumptions employed during model derivation. As a result, the advancement of effective media models for engineering DOE mission-critical systems (e.g., batteries, flow batteries, electrolyzers, geothermal systems, subsurface chemical storage systems, etc.) is slow (i.e., it takes years for models to traverse from stages of “development” to “practical utilization”), hindering our ability to effectively optimize such systems and stay at the cutting-edge of the energy frontier. In this work, we aimed to address these limitations by 1.) automating and accelerating multiscale model derivation via symbolic computing and 2.) develop a novel multiscale modeling methodology for flow and transport through porous media that avoids the typical assumptions hindering previous models. As a result of our efforts, we 1.) developed a hybrid symbolic-numeric code called Fouriera for fully-automating the implementation of multiphysical and phase-field models via the Fourier spectral method for materials science research, and 2.) advanced a multiscale modeling methodology called The Method of Finite Averages that rigorously predicts the behaviors of flow and transport through heterogeneous porous media under the influence of non-local effects and strong advection. Ultimately, these deliverables provide strong foundations from which further efforts can advance multiscale modeling tools and capabilities that do not intrinsically rely on 1.) the speed and mathematical capabilities of humans, nor 2.) the methodological assumptions limiting current models.

36 MATERIALS SCIENCE↗

Using Thermodynamic Availability to Predict the Transitional Film Reynolds Number between the Droplet and Jet Modes in Falling Liquid between Horizontal Tubes

The objective of this paper is to propose and validate a model for predicting the transitional film Reynolds number between the droplet and jet modes of falling film flows. The proposed model relies on thermodynamic availability with no fitting to mode transition data. The model directly predicts the numerical values of the transitional film Reynolds numbers at different operating conditions. The results of the model are validated against 43 experimental data points from the literature, which cover 10 working fluids and a wide range of tube geometries. The accuracy of the proposed model in predicting the validation data is -on average- more than 20 % better than that of the widely used Re = a (Ga) b and Re = a (Ga) b (S/ξ) c models. The model also achieves low computation time (less than 30 seconds for the 43 validation data points). The model presented in this paper has the potential of integration with heat transfer, mass transfer, and pressure drop models for optimizing the design and operation of falling film heat and mass exchangers.

42 ENGINEERING↗

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

60 APPLIED LIFE SCIENCES↗

Evaluation of a practical approach for field scale moisture flow modeling in heterogeneous media at a semiarid site

Abstract A practical approach for modeling field‐scale moisture flow in a highly heterogeneous unsaturated medium is described in this study. The validity of this approach is demonstrated through comparison of the numerical simulations with field observations at a semiarid site located in southcentral Washington State. The methodology is based on upscaling the core scale hydraulic properties and combining power‐law and tensorial connectivity‐tortuosity (PA‐TCT) approaches to derive macroscopic anisotropy parameters for each hydrostratigraphic unit (HSU) identified in the field. Each heterogeneous HSU is approximated by an equivalent homogeneous medium (EHM) model for which PA‐TCT parameters are used in the flow simulations. The available field data on moisture content and matric potential are compared with steady‐state flow simulations based on the mean form of Richards' equation. While the homogenization or averaging of heterogeneities, embedded in the EHM modeling approximation, cannot capture all of the field‐scale variability, the simulated steady‐state moisture and matric potential profiles capture well the central tendency of the field data. This approach is deemed practical for assessing the fate and transport of contaminants in highly heterogeneous unsaturated media at the transport scale of hundreds of meters.

Khaleel, Raziuddin↗

First saturation correction in high energy proton-nucleus collisions. Part III. Ensemble averaging

In high energy proton-nucleus collisions, the gluon saturation effects from the nucleus are fully incorporated into the light-like Wilson lines. The gluon saturation effects from the proton, which are anticipated to be important either in the extreme high energy limit or towards the dense-dense (nucleus-nucleus) collision regimes, have been studied perturbatively within the Color Glass Condensate effective theory in previous papers of this series. A configuration-by-configuration expression for the single inclusive semi-hard gluon production including the first saturation correction was obtained. In this paper, we perform ensemble averaging in the McLerran-Venugopalan model and the Dipole Approximation. We find that, in the saturation correction, the effects of the initial state interactions are negligible while the final state interactions play most important role and give a positive-valued contribution to the semi-hard gluon spectrum. Furthermore, we show that the single gluon spectrum scales approximately 1/$k$$^{4}_{⊥}$ at small k ⊥ , suggesting that a resummation of higher order saturation corrections is required to regulate the infrared region of the gluon spectrum.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Economics of electric vehicle corridor fast charging in the United States

Corridor direct-current fast charging (DCFC) stations enable long-distance electric vehicle travel, yet their economics remain uncertain due to high capital costs, low initial utilization, and exposure to utility demand charges. This study evaluates the long-term economics of corridor DCFC across the United States, incorporating capital and operating expenses-including charging equipment and real-world utility tariffs-alongside modeled station utilization, financial incentives, and ancillary retail revenue. In the Baseline scenario, modeled breakeven costs for corridor DCFC average $\$$0.42/kWh over 20 years, yet fewer than half of stations reach cost parity with gasoline on a per-mile basis. Utilization is the primary driver of cost variation, with low-utilization stations costing roughly six times more per kilowatt-hour than the national average. Excluding stations that fail to reach cost parity reduces National Highway System coverage within 50 miles from 94% to 67%, underscoring the trade-off between market-driven deployment and comprehensive network coverage. These results provide guidance for charging providers, utilities, planners, and policymakers seeking to develop and sustain a financially viable national corridor charging network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗