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DOE OSTI2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗

DOE OSTI2023

Biofuels Information Center

The purpose of the Biofuels Information Center (BIC) task is to provide relevant data, information, reports, and web-based tools to all bioenergy stakeholders. The BIC task began in FY08 to meet the requirement under Title II, Sec. 229 of the Energy Independence and Security Act of 2007 (EISA) requires DOE to develop a "Biofuels and Biorefinery Information Center". The BIC task supports biofuels pages content on the EERE's most visited website - the Alternative Fuels Data Center (AFDC http://www.afdc.energy.gov) and the Bioenergy Atlas tools (currently archived) (previous address https://maps.nrel.gov). This task results in more than 1.7 million web pageviews (an instance of an internet user visiting a webpage) per year. In FY22, the task completed the final year of the 5 year USDA Biofuels Infrastructure Partnership (BIP). The USDA BIP expanded infrastructure for E15 and/or E85 to approximately 850 stations and NREL received and reviewed data for quality analyzed all infrastructure and sales data collected by USDA. Stations are privately held and previously it was difficult to ascertain infrastructure and sales data. This unique dataset allows insight into infrastructure data (number of pumps and tanks, costs to install new equipment) and sales data (price and volume for E10, E15, E85, and diesel by month). The 2021 USDA BIP National Summary Report is with DOE for review prior to publication. Future work will include the biannual Bioenergy Industry Status Report (4 previous versions have been published) . The task also supports the principal investigator's time to engage stakeholders on infrastructure and deployment of biofuels. This includes leading, membership, and participation in the following roles: member Board of Advisors at the Fuels Institute, voting member for multiple UL standards committees, Co-Chair of the Infrastructure team at Agriculture/Auto/Ethanol, Member of Coordination Research Council's ULSD Corrosion Committee. The Principal investigator routinely responds to industry inquires to assist in deployment of biofuels regularly.

biofuels↗

DOE OSTI2026

Understanding spectral dependance of laser-induced damage precursors in dielectric materials (Abbreviated report_23-ERD-006)

High peak and average power laser systems are typically limited by the handling fluence of the optical components. In particular, the multilayer dielectric coatings are known to be much lower operational fluence than the more ideal bulk materials. In this project we proposed and succeeded in probing different established classes of damage prone precursors at different wavelengths to understand their fundamental laser damage response as a function of wavelength. This study helped to shed light on the fundamental physics of the non-linear precursors that govern laser damage phenomena for ns-regime pulsed laser damage. In this study, we utilized the onsite coating capabilities (VPL, IBS coating lab) to purposefully generate laser damage-prone precursors in hafnia-based coatings (both single and multi-layer coatings). Specifically, we engineered coatings with craze lines initiated by nodules, generated coatings with our xenon-based coating process to suppress nanobubble formation and generated hafnia coatings under controlled oxygen flow conditions to study hafnia sub-oxides and oxygen flow dependance. In these studies we found that craze lines are rife with precursors that are sensitive to ultra-violet light but not to infrared light; we found that the removal of nanobubbles helps with all wavelengths tested, but is most impactful for ultra-violet light; we also found that ultra-violet laser damage performance of hafnia is closely matched to oxygen flow rate, while the infrared performance may be slightly better at lower flow rates. During this LDRD we also successfully stood up a new laser damage testing capability, namely a wavelength agile damage test station to study the spectral response of known laser-induced damage precursors. This is a unique and important capability for Lawrence Livermore National Lab, allowing us to understand the spectral response of materials under high intensity irradiation and damage. This is a vital tool to understand non-linear optical response at wavelengths that we have previously been unable to test at.

36 MATERIALS SCIENCE↗

DOE OSTI2023

National Center for Airborne Laser Mapping (NCALM) LiDAR and DEM data from two NGEE Arctic Sites, Seward Peninsula, Alaska, Winter 2022

From April 3 through April 6 of 2022, airborne remote sensing data was collected by the National Center for Airborne Laser Mapping (NCALM) in collaboration with NGEE Arctic scientists. Snow-on data was collected around two NGEE Arctic study sites on the Seward Peninsula of Alaska: Teller mm 27 and Kougarok mm 64. A Robinson R44 II helicopter with a RIEGL VQ-580 II airborne laser scanner was used to collect the LiDAR point cloud data for each study site. This survey was conducted during expected peak snow cover at the end of the winter on the Seward Peninsula, and can be paired with data collected in 2021 during the snow-off campaign "National Center for Airborne Laser Mapping (NCALM) LiDAR, Imagery, and DEM data from five NGEE Arctic Sites, Seward Peninsula, Alaska, August 2021" (Singhania et al., 2023) (NGA270). This data package contains LiDAR point clouds (.las), Digital Elevation Models (.tif), and shapefiles of the .las tiling system (.shp). A project report detailing data collection details, GNSS stations and GNSS corrections, and processing steps is also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Comparative evaluation and selection of heat exchangers using multicriteria decision-making

Here, this study presents a well-structured method for comparing and selecting Heat Exchanger (HE) technologies for Integrated Energy Systems (IES). The decision to select a HE for a particular IES configuration can vary greatly depending not only on engineering requirements but also on customer’s specific demand. In other words, the HE selection for IES requires a multicriteria decision-making approach, taking into account diverse technical, economic, and safety aspects, as well as the relative priorities considered by energy users. This study employs a HE evaluation approach combining multicriteria decision-making techniques widely used in various industries: quality function deployment (QFD) and analytic hierarchy process (AHP) techniques. Of particular interest is the use of the proposed method to select a high-temperature HEs that couples advanced nuclear reactors and industrial processes. To build a practical basis for comparing HEs within the proposed framework, efforts were made to identify the various HEs requirements for IES purposes. In addition, leveraging the insights obtained from the literature review and the market survey of commercial HE suppliers, a knowledge base was built to facilitate the comparison of each requirement across various HE designs. Also, evaluation metrics were identified for HE requirements with robust rational to enhance the quality of decisions made throughout the proposed evaluation process. The evaluation procedure and knowledge base described in this study can provide a useful basis for those interested in screening the appropriate HE designs for various IES scenarios.

Analytic Hierarchy Process (AHP)↗

DOE OSTI2025

Shallow Geothermal Resources for Cooling Applications at the University of Hawai'i

Drilling activities account for 30% to 57% of the cost to develop and install a geothermal plant. Therefore, an accurate representation of the cost to drill a well is paramount in techno-economic analysis to determine the feasibility of a geothermal power project. In 2022, the National Renewable Energy Laboratory (NREL) endeavored to revise the U.S. Department of Energy (DOE) GeoVision baseline drilling cost curves due to extensive improvement in drilling rates at the Utah Frontier Observatory Research in Geothermal Energy (FORGE) demonstration site. That effort did not culminate in the recommendation of new curves because the actual project costs did not match the reported performance improvements and were at or above the GeoVision baseline. The need for another iteration of this analysis has arisen from industry record drilling performance reported by recent commercial field-scale and demonstration projects, including Fervo Energy's Cape Station, the Utah FORGE 16B(78)-32 demonstration and the Geysers Power Company's GDC-36 demonstration. Therefore, in this work, we have estimated the resulting industry average rate of penetration (ROP) and bit life and applied these parameters as inputs to the Well Cost Simplified model used in the GeoVision analysis. The resulting revised cost curves show a significant decline from the GeoVision baseline. For vertical wells, the magnitude of cost decline ranges between 12% and 24% while for deviated wells, cost reductions between 18% and 26% are estimated. The revised cost curves are in good agreement with actual cost data and therefore, quantify the economic impact of the utilization of (and advances in) polycrystalline diamond compact (PDC) bit technology and the application of physics-based methodologies that optimize mechanical specific energy.

building cooling↗

DOE OSTI2025

2025 Geothermal Drilling Cost Curves Update: Preprint

Drilling activities account for 30% to 57% of the cost to develop and install a geothermal plant. Therefore, an accurate representation of the cost to drill a well is paramount in techno-economic analysis to determine the feasibility of a geothermal power project. In 2022, the National Renewable Energy Laboratory (NREL) endeavored to revise the U.S. Department of Energy (DOE) GeoVision baseline drilling cost curves due to extensive improvement in drilling rates at the Utah Frontier Observatory Research in Geothermal Energy (FORGE) demonstration site. That effort did not culminate in the recommendation of new curves because the actual project costs did not match the reported performance improvements and were at or above the GeoVision baseline. The need for another iteration of this analysis has arisen from industry record drilling performance reported by recent commercial field-scale and demonstration projects, including Fervo Energy’s Cape Station, the Utah FORGE 16B(78)-32 demonstration and the Geysers Power Company’s GDC-36 demonstration. Therefore, in this work, we have estimated the resulting industry average rate of penetration (ROP) and bit life and applied these parameters as inputs to the Well Cost Simplified model used in the GeoVision analysis. The resulting revised cost curves show a significant decline from the GeoVision baseline. For vertical wells, the magnitude of cost decline ranges between 12% and 24% while for deviated wells, cost reductions between 18% and 26% are estimated. The revised cost curves are in good agreement with actual cost data and therefore, quantify the economic impact of the utilization of (and advances in) polycrystalline diamond compact (PDC) bit technology and the application of physics-based methodologies that optimize mechanical specific energy.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Comparing cross-platform performance via node-to-node scaling studies

Due to the increasing diversity of high-performance computing architectures, researchers and practitioners are increasingly interested in comparing a code’s performance and scalability across different platforms. However, there is a lack of available guidance on how to actually set up and analyze such cross-platform studies. In this paper, we contend that the natural base unit of computing for such studies is a single compute node on each platform and offer guidance in setting up, running, and analyzing node-to-node scaling studies. In conclusion, we propose templates for presenting scaling results of these studies and provide several case studies highlighting the benefits of this approach.

cross-platform studies↗

DOE OSTI2025

Framework for Assessing Impact of Wave-Powered Desalination on Resilience of Coastal Communities

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Combined TREAT-LOC & SATS Integral LOCA Experiment Plan

The Transient Reactor Test Facility (TREAT) loss-of-coolant (LOC) and highburnup (HBu) experiment series, along with the Severe Accident Test Station (SATS) HBu experiment series, are integral LOC accident (LOCA) experiments planned under the Department of Energy (DOE) Advanced Fuels Campaign (AFC) program, which aims to support burnup extension needs by addressing identified R&D priorities in order to achieve an improved understanding of fuel fragmentation, relocation, and dispersal (FFRD) of HBu fuel during LOCA events. Priorities have been identified by the Electric Power Research Institute (EPRI)’s Collaborative Research on Advanced Fuel Technologies (CRAFT) Fuel Performance and Testing Technical Experts Group (FPTTEG). The data produced under this plan will be used to further validate and confirm existing models and inform future R&D and model development. The experimental program was specifically developed to address data gaps and opportunities identified via detailed review of the existing public knowledge base on LOCA FFRD, as well as reviewing specific experimental development activities regarding prototypic LOCA conditions for light-water reactor (LWR) systems. The test program relies on a unique combination of in- and out-of-pile experimental approaches to provide a clear tieback to the existing integral and semi-integral LOCA experiment database, using state-of-the-art facilities. More importantly, the program will systematically investigate the impacts of prototypic HBu fuel/cladding thermomechanical behaviors under postulated LWR LOCA conditions not yet fully investigated. These conditions correspond with prototypic decay-energy heatup (DEH) and stored-energy heatup (SEH) conditions. First, TREAT’s unique capability will enable the first evaluation of the impact of SEH conditions on HBu fuels. The test program will emphasize the development of an improved mechanistic understanding of key phenomena through independent experimental systems, development of a database to support fuel performance modeling tools, world-leading advanced materials characterization, and the most advanced approach to in situ diagnostics ever deployed to evaluate FFRD. The results will represent a significant leap forward in evaluating prototypic conditions and novel data to support modeling development and validation, as well as to inform the technical basis for LOCA-induced FFRD.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Mid Infra-Red Laser Sensor for Continuous Sulfur Trioxide Monitoring to Improve Coal-Fired Power Plant Performance during Flexible Operations

During the course of this project, we performed exhaustive research and development of SO3/H2SO4 sensing technology for coal-fired power plant applications (Figure 1). The development culminated in a successful field campaign of a prototype continuous real-time H2SO4 monitor at a coal-fired power plant (TRL 6) accomplishing the primary goal of the project. The developed sensors utilize tunable laser absorption spectroscopy (TLAS) operating in the mid-infrared (Mid-IR) wavelength region, which is the so-called “molecular fingerprint” region. Systems operating in the Mid-IR have orders of magnitude more sensitivity than systems operating at shorter wavelengths, such as near-infrared (NIR). However, NIR systems are more widespread due to more mature supporting technology (e.g., fiber optics, optical components, etc.). In this project, we not only produced a specific Mid-IR sensor, we also advanced Mid-IR sensor technology in general through the development and demonstration of such supporting technology. In this project, we also developed proprietary broad tuning lasers enabling the ability to effectively measure SO3, H2SO4, H2O, and SO2. Different molecular species have unique spectral signatures that can be probed with lasers operating at different wavelengths. Standard TLAS uses relatively narrow wavelength tuning distributed feedback (DFB) lasers, which can typically only target a single species with narrow features, and are not appropriate for species with broad features, such as SO3 or H2SO4. In contrast, by developing unique, broad-tuning laser technology, we were able to measure these species, as well as SO2 and H2O simultaneously. Furthermore, to enable real-time analysis at a power plant, we modified a commercially available heated gas cell to operate in the Mid-IR wavelength range and fiber coupled the lasers to enable remote delivery of the beams. To generate reference data (library spectra), our collaborators at the University of California Irvine (UCI) developed a catalytic SO3 generation facility. It is worth mentioning that representative H2SO4 and SO3 Mid-IR spectra are not a part of any publicly available database and the data generated under this project is a valuable resource in and of itself. In addition, based on the UCI study we determined that detection of SO3 is complicated by the very strong SO2 absorption. For that reason, we concentrated on H2SO4 detection. Since SO3 and H2SO4 exist in a flue gas in a state of equilibrium, which depends on temperature and humidity, by measuring water concentration and controlling the temperature of the gas cell, we developed an approach to determine SO3 concentration from the H2SO4 measurement. During the development phase of the project, we performed three testing campaigns at our collaborator’s FERCo flue gas facility with conditions representative of the coal-fired power plant (~ 40ppm SO3, 1700ppm to 2800 ppm SO2, 10% water) with the exception of particulate matter. After three test campaigns at FERCo we performed field testing at Harrison Power Station. The final system was mounted on a duct and measured H2SO4, SO2 and water. The tests were highly successful with a demonstrated real-time H2SO4 precision of 1 ppm with a 1 second update. Our collaborators at EPRI conducted an industry survey and determined that there is a very high interest for the SO3/H2SO4 monitoring in the power generation industry as well as in heavy industries in general. Furthermore, work performed by OptoKnowledge beyond the scope of this project under a synergistic DOE SBIR determined another approach to SO3 detection. We applied for Phase II on this SBIR for development a of versatile SO3/H2SO4 sensor but were not selected. We are currently looking for another opportunity to leverage all the technological advancements produced by this project including but not limited to the flue gas facility at UCI, the hardware and software developed, and relationships with FERCo, EPRI CEMTEK, and Harrison Station.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗

DOE OSTI2022

β decay of neutron-rich 76 Cu and the structure of 76 Zn

In this work, the β decay of 76 Cu to the levels of 76 Zn was studied at the Holifield Radioactive Ion Beam Facility (HRIBF) at Oak Ridge National Lab (ORNL). A purified 76 Cu beam was developed and data were recorded for the decay of the A=76 decay chain using four high-purity germanium (HPGe) clover detectors at the Low-energy Radioactive Ion Beam Spectroscopy Station (LeRIBSS). In this measurement, data on γ-ray emission following β decay, including βγ and γγ coincidences, were collected and γγ spectra were analyzed to identify the statistically significant coincidences. From this analysis, we propose a level scheme for 76 Zn which contains a total of 59 energy levels up to 6.0 MeV containing 105 γ rays. We have identified an additional 53 γ rays associated with this decay which could not be place in the decay scheme due to insufficient coincidence information or no energy match to identified levels. No γ rays from states in 75 Zn fed in the delayed-neutron branch were observed even though other γ rays in the A=75 decay chain were observed. Spin and parity assignments are proposed for some levels based on comparison to systematics and shell model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

How does Mercury Methylation Respond to Intensive Forest Management and the Creation of Anoxia in Floodplain Soils

This research endeavor, entitled “How does Mercury Methylation Respond to Intensive Forest Management and the Creation of Anoxia in Floodplain Soils?” represents a collaborative effort undertaken by the University of North Carolina at Greensboro, led by Principal Investigator Dr. James S. Coleman, the United States Forest Service Santee Experimental Forest (SEF) Station Director Dr. Carl C. Trettin as Co-Principal Investigator, and The Chinese University of Hong Kong Dr. Martin Tsz-Ki Tsui and Dr. Alex T. Chow serving as Co-Principal Investigators. The overarching objective of this proposed project was to capture and elucidate the immediate and short-term effects of forest disturbances, namely clear-cutting and thinning, on the biogeochemical cycling of mercury (Hg) within the SEF in Huger, South Carolina. To accomplish this objective, a two-year exploratory study was proposed to conduct soil sampling and measurements of Hg and toxic methylmercury (MeHg), and soil physiochemical data within the experimental watershed (WS77) at SEF, following the clear-cutting process initiated for the preparation of the native longleaf pine restoration project by the USFS. Based on our mercury and hydrological data assessment, we concluded that the implementation of forest management practices, particularly mechanical thinning and clear-cutting, appears to enhance the potential for Hg methylation. Furthermore, these forest management practices may disrupt and alter the storage, mobility, and transformations of atmospherically deposited Hg in these lowland forests. We advocate for further research to comprehensively evaluate the impact of forest management on Hg cycling within forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Structural Templation of MOF-Derived Zirconia Nanoparticles

Templated synthesis is an important avenue for the development and synthesis of porous materials, as it provides a high level of control over the resulting structure. However, this control is difficult to achieve at the atomic level for poorly crystalline or noncrystalline materials, such as metal–organic framework (MOF)-derived carbons. We report the carbonization of three zirconium-based MOFs with different framework and inorganic building unit structures to form zirconia nanoparticles in a carbon matrix. Using a combination of X-ray diffraction, X-ray total scattering, and transmission electron microscopy, we found that the extended Zr-oxo chains of MIL-140C-bpy facilitate the formation of larger and more ordered zirconia nanoparticles. In contrast, the discrete Zr 6 -oxo clusters of UiO-67-bpy and Zr-ABTC result in smaller and differently structured nanoparticles.

metal-organic framework↗

DOE OSTI2022

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m –2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Final Report)

The Phase 1 Final Technical Report describes the results of the work completed during the “Advanced Modular Sub-Atmospheric Hybrid Heat Engine” project. A key part of the Phase 1 work was the completion of a thermodynamic cycle analysis for the MHHE at the selected module size. The hybrid heat engine has been developed as a modular unit (sized in the range of 500kW – 60MW) that can be used with modular coal or biomass gasifiers, with distributed power generation systems, with large power plants comprised of multiple generating units, and with natural gas compression stations. The MHHE will provide cleaner, more efficient, and lower cost generation with better load following capabilities than existing competing technologies with a singular generating source such as solar farm, gas turbine, or combustion engine. The drivers of the MHHE technology are: benefits of modular power generation (reduced equipment cost, construction cost and implementation time, connection ready on delivery, flexible scalability, serviceability), fuel flexibility, lowest cost power generation and reduced emissions.A logical progression of work and a clear path forward toward meeting the FOA goals and objectives have been established. Namely, a preliminary market analysis and primary fuel identification was completed first and then the modularity of the system was defined. The benefits of the proposed hybrid and modular heat engine were described when applied to modular coal gasifiers, distributed power generators, and larger power plants. Based on the market analysis, modularity, and chosen primary fuel, a conceptual design and layout of the hybrid heat engine was developed, analyzed, and characterized. The technology gaps already identified have been reviewed and expanded upon, and a test plan to address these gaps through bench scale testing in Phase 2 has been developed. Cost estimate methodology and considerations in support of a potential Phase 2 project have been described.

08 HYDROGEN↗

DOE OSTI2026

Tussock tundra surface temperatures, ambient air and incoming photosynthetically active radiation measured at the NGEE Arctic Council site, 2021 - 2023

This dataset contains temperature measurements carried out along two fiber optics cables/lines (150 m each) laid out along the ground at the Next Generation Ecosystem Experiment (NGEE) Arctic site near Council, Alaska. The lines traverse an heterogeneous part of the tussock tundra site including thermokarst features and lichen dominated sections of the tundra. Measurements were done using a Sensornet Oryx DTS, a Distributed Temperature sensor that was installed in September 2021 and taken down in August 2023. The sensor was powered by solar power with data being collected every 30 minutes at 1 m resolution. In addition to these measurements air temperature and incoming photosynthetically active radiation (PAR) are provided. These measurements are co-located with the NGEE Arctic Council eddy flux and meteorological station (AmeriFlux ID US-NGC). Included are six *.csv files (four data files and two reporting format files) and two *.kml files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Air temperature↗