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DOE OSTI2022

Modeling Power Plant Siting Opportunities and Constraints in the Eastern Interconnection

The electrical transmission grid is a critical part of the U.S. national infrastructure, and its modernization is a U.S. Department of Energy (DOE) priority. We describe the Energy Zones Mapping Tool (EZMT), a unique, powerful, and public web-based system with multi-criteria decision analysis (MCDA) models for more than 20 power plant technologies and many other capabilities. Through a case study on natural gas combined cycle (NGCC) power plants in the Eastern Interconnection (EI), we provide an example of incorporating an EZMT MCDA model into a larger planning context, with projections of interconnection-level capacity expansion, thermoelectric power plant retirements, and water availability. Our results provide insights on candidate NGCC site distributions and the criteria influencing them. The case study provides both an efficient methodology for performing similar analyses and hundreds of candidate NGCC power plant sites that can be studied in more detail as potential project sites.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Adoption of Plug-in Electric Vehicles: Local Fuel Use and Greenhouse Gas Emissions Reductions Across the U.S.

The dependence on gasoline-powered light-duty automobiles has made U.S. households vulnerable to the burden of fuel costs. Tailpipe emissions from these vehicles constitute 58% of greenhouse gas (GHG) emissions in the U.S., which are damaging to the environment (EPA, 2023). The adoption of plug-in electric vehicles (PEVs) has been shown to effectively reduce fuel costs and GHG emissions. However, local effects on these benefits are not well understood by American consumers, potentially limiting adoption and therefore the realization of PEV benefits at scale (MacInnis & Krosnick, 2020; EY Americas, 2023). To fill this research gap, this study estimates the fuel cost savings and GHG emission reductions at the state and ZIP code levels by considering local fuel prices, vehicle class preference, average vehicle model year, fuel efficiencies, and driving intensities. The study's findings reveal that the adoption of PEVs can yield substantial benefits in terms of fuel cost savings and GHG emission reductions nationwide. Specifically, driving a battery electric vehicle (BEV) is estimated to result in annual savings of up to $\$2,200$, while driving a plug-in hybrid electric vehicle (PHEV) can lead to savings up to $\$1,500$, when compared to an internal combustion engine vehicle (ICEV) of equivalent size. Moreover, using population-weighted averages by ZIP code, BEVs and PHEVs show the potential to save 400 and 200 grams of carbon dioxide equivalent per mile, respectively, compared to a representative ICEV of the same class. The magnitude of fuel cost savings and emissions reduction vary by region due to various factors. Generally, regions with high gasoline prices, low electricity prices, preferences for larger vehicles, and high driving intensities tend to see relatively large fuel savings. The emissions reductions are more pronounced in areas with clean grids where consumer preferences lie with large vehicles. This regional variability underscores the importance of considering local contextual factors when assessing the potential benefits of PEV adoption. In more than 99% of U.S. ZIP codes, PEVs result in overall savings in fuel use (and subsequent costs) and GHG emissions. While not a central focus of this analysis, reductions in GHG tailpipe emissions from PEV adoption would also come with reductions in criteria pollutant emissions, contributing to improved local air quality depending on the PEV penetration, population density, and electricity generation infrastructure in the locality.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Ensemble-Based Characterization of Historical Storm Types in ERA5 over CONUS

We present Storm-Type Labeled Precipitation, a gridded dataset that classifies ERA5 precipitation over CONUS (20–50° N, 125–66° W) by storm type at 6-hourly resolution. Each grid cell/time step is assigned to one of five classes—mesoscale convective system (MCS), extratropical cyclone (ETC), hurricane (HUR), atmospheric river (AR), or other convective, with an “unidentified” code reserved for cases with no detected type. Two products are provided: (i) a TempestExtremes (TE)–only version, and (ii) a hybrid TRACK–TE version that uses TRACK for ETCs and HURs and TE for MCSs and ARs. Outputs are integer masks aligned to ERA5 precipitation, supporting storm-type attribution of rainfall, event compositing, trend analysis, and model evaluation. Future work will extend detection to high-resolution downscaled Earth system model projections to support evaluations of projected changes in hydrologic hazards that may threaten critical water and energy infrastructures.

Rastogi, Deeksha [ORNL] (ORCID:0000000204624027)↗

NASA NTRS2009

Developing a Model-based Capability to Analyze Requirements for the Climate Observing System

Models are foundational for estimating states of the earth's climate system, both as tools to extrapolate information in time and space, and as observation 'operators' used to relate what is analyzed and predicted to what is observed. Expanding the simulation approach further, observing system simulation experiments (OSSEs) are designed to mimic the complete process of analyzing the climate state by replacing real observations with entirely simulated ones determined from a model-based depiction of nature. OSSEs provide a framework to 'fly' simulated satellite instruments through a synthetic atmosphere and investigate the trade-spaces of measurements for various satellite configurations and sampling strategies, and assess their measurement impact on modeling and forecasting capabilities. Such a tool is a crucial but as yet unfulfilled need for future mission selection and design. The components of a state-of-the-art OSSE system are being assembled at the Global Modeling and Assimilation Office (GMAO, Code 610.1) at NASA/GSFC, leveraging on the GMAO's existing modeling and data assimilation infrastructure for numerical weather prediction (NWP). The OSSE framework is based on the GMAO's Goddard Earth Observing System atmospheric general circulation model, version 5 (GEOS-5) and the Gridpoint Statistical Interpolation (GSI) observational analysis scheme, combined with the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) model developed by the Atmospheric Chemistry and Dynamics Branch (Code 613.3). This system is an evolving, key component of Goddard's planned development of an Integrated Earth System Analysis (IESA) capability, which will bring together into a single, fully interactive system Goddard's modeling and assimilation efforts in atmosphere, ocean and chemistry and aerosols to provide a comprehensive analysis and prediction system for weather and climate In addition to providing a state-of-the-art capability for assimilating current observation types, GEOS-5, and the future IESA, provide the capability to identify the need for, and assess the potential impact of, future observing systems under consideration for improving weather and climate prediction.

Gelaro, Ronald↗

DOE OSTI2020

Recycling and Life Cycle Issues for Lightweight Vehicles

This chapter addresses recycling and life cycle considerations related to the growing use of lightweight materials in vehicles. This chapter first addresses the benefit of a life cycle perspective in materials choice, and the role that recycling plays in reducing energy inputs and environmental impacts in a vehicle’s life cycle. Some limitations of life cycle analysis and results of several vehicle- and fleet-level assessments are drawn from published studies. With emphasis on lightweight materials such as aluminum, magnesium, and polymer composites, the status of the existing recycling infrastructure and technological challenges being faced by the industry also are discussed.

Das, Sujit↗

DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗

DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

DOE OSTI2024

Framework for custom event sample augmentations for ATLAS analysis data

For HEP event processing, data is typically stored in column-wise synchronized containers, such as most prominently ROOT’s TTree, which have been used for several decades to store by now over 1 exabyte. These containers can combine row-wise association capabilities needed by most HEP event processing frameworks (e.g. Athena for ATLAS) with column-wise storage, which typically results in better compression and more efficient support for many analysis use-cases. One disadvantage is that these containers, TTree in the HEP use-case, require to contain the same attributes for each entry/row (representing events), which can make extending the list of attributes very costly in storage, even if those are only required for a small subsample of events. Since the initial design, the ATLAS software framework features powerful navigational infrastructure to allow storing custom data extensions for subsamples of events in separate, but synchronized containers. This allows adding event augmentations to ATLAS standard data products (such as DAOD-PHYS or PHYSLITE) avoiding duplication of those core data products, while limiting their size increase. For this functionality, the framework does not rely on any associations made by the I/O technology (i.e. ROOT), however it supports TTree friends and builds the associated index to allow for analysis outside of the ATLAS framework. A prototype based on the Long-Lived Particle search is implemented and preliminary results with this prototype will be presented. At this point, augmented data are stored within the same file as the core data. Storing them in separate files will be investigated in future, as this could provide more flexibility, e.g. certain sites may only want a subset of several augmentations or augmentations can be archived to tape once their analysis is complete.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Transportation Energy Futures Series: Alternative Fuel Infrastructure Expansion: Costs, Resources, Production Capacity, and Retail Availability for Low-Carbon Scenarios

The petroleum-based transportation fuel system is complex and highly developed, in contrast to the nascent low-petroleum, low-carbon alternative fuel system. This report examines how expansion of the low-carbon transportation fuel infrastructure could contribute to deep reductions in petroleum use and greenhouse gas (GHG) emissions across the U.S. transportation sector. Three low-carbon scenarios, each using a different combination of low-carbon fuels, were developed to explore infrastructure expansion trends consistent with a study goal of reducing transportation sector GHG emissions to 80% less than 2005 levels by 2050.These scenarios were compared to a business-as-usual (BAU) scenario and were evaluated with respect to four criteria: fuel cost estimates, resource availability, fuel production capacity expansion, and retail infrastructure expansion.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2012

Modernization of B-2 Data, Video, and Control Systems Infrastructure

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Spacecraft Propulsion Research Facility, commonly referred to as B-2, is NASA s third largest thermal-vacuum facility with propellant systems capability. B-2 has completed a modernization effort of its facility legacy data, video and control systems infrastructure to accommodate modern integrated testing and Information Technology (IT) Security requirements. Integrated systems tests have been conducted to demonstrate the new data, video and control systems functionality and capability. Discrete analog signal conditioners have been replaced by new programmable, signal processing hardware that is integrated with the data system. This integration supports automated calibration and verification of the analog subsystem. Modern measurement systems analysis (MSA) tools are being developed to help verify system health and measurement integrity. Legacy hard wired digital data systems have been replaced by distributed Fibre Channel (FC) network connected digitizers where high speed sampling rates have increased to 256,000 samples per second. Several analog video cameras have been replaced by digital image and storage systems. Hard-wired analog control systems have been replaced by Programmable Logic Controllers (PLC), fiber optic networks (FON) infrastructure and human machine interface (HMI) operator screens. New modern IT Security procedures and schemes have been employed to control data access and process control flows. Due to the nature of testing possible at B-2, flexibility and configurability of systems has been central to the architecture during modernization.

Cmar, Mark D.↗

NASA NTRS2012

Modernization of B-2 Data, Video, and Control Systems Infrastructure

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Spacecraft Propulsion Research Facility, commonly referred to as B-2, is NASA's third largest thermal-vacuum facility with propellant systems capability. B-2 has completed a modernization effort of its facility legacy data, video and control systems infrastructure to accommodate modern integrated testing and Information Technology (IT) Security requirements. Integrated systems tests have been conducted to demonstrate the new data, video and control systems functionality and capability. Discrete analog signal conditioners have been replaced by new programmable, signal processing hardware that is integrated with the data system. This integration supports automated calibration and verification of the analog subsystem. Modern measurement systems analysis (MSA) tools are being developed to help verify system health and measurement integrity. Legacy hard wired digital data systems have been replaced by distributed Fibre Channel (FC) network connected digitizers where high speed sampling rates have increased to 256,000 samples per second. Several analog video cameras have been replaced by digital image and storage systems. Hard-wired analog control systems have been replaced by Programmable Logic Controllers (PLC), fiber optic networks (FON) infrastructure and human machine interface (HMI) operator screens. New modern IT Security procedures and schemes have been employed to control data access and process control flows. Due to the nature of testing possible at B-2, flexibility and configurability of systems has been central to the architecture during modernization.

Cmar, Mark D.↗

DOE OSTI2019

Wireless charging and shared autonomous battery electric vehicles (W+SABEV): synergies that accelerate sustainable mobility and greenhouse gas emission reduction

Emerging technologies play important roles in shaping future mobility systems and impacting sustainability performance of the transportation sector in major economies, such as the United States of America (USA) and China. This study applies a life cycle framework to demonstrate and evaluate the synergies of the following four emerging transportation system technologies both qualitatively and quantitatively: (1) wireless charging; (2) shared mobility services; (3) autonomous driving; and (4) battery electric vehicles (BEV). The new concept of a wireless charging and shared autonomous battery electric vehicle (W+SABEV) system is introduced and modeled. First, an analytical framework is presented to assess the pros and cons of the W+SABEV system vs. a conventional plug-in charging BEV system, adhering to the principles of sustainable mobility and highlighting the impacts and dynamics of the disruptive technologies on the key parameters that define sustainable mobility. Second, a quantitative analysis presents the synergies of the four technologies by modeling a W+SABEV system and demonstrates that the combination of the four technologies can shorten the payback time of greenhouse gas (GHG) emission burdens for infrastructure and vehicles. Finally, compared to a plug-in charging BEV system, a W+SABEV system pays back the additional GHG emission burdens of wireless charging infrastructure deployment within 5 years if the wireless charging utility factor (ratio of en route charging time vs. trip time) is above 19%.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Abstract Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI’s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Carbon Capture, Transport, & Storage: Supply Chain Deep Dive Assessment (Final Report)

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. This report was completed by the Department of Energy (DOE) to examine carbon dioxide (CO 2 ) capture, transport, and storage technologies and associated supply chains that will be required to support the United States (U.S.) decarbonization goals by 2050. Specifically, the analysis sought to understand supply chain bottlenecks to achievingan upper-bound 2.0 gigatonnes (Gt) of CO 2 capture and storage (CCS) per year in the United States. A literature review shows that in aggressive infrastructure deployment scenarios, the United States’ likely upper bound of CCS capacity is 1.7 Gigatons per annum (Gtpa) by 2050. This suggests the study design of 2.0 Gtpa capacity by 2050 is more aggressive yet and represents a conservative upper bound for supply chain analyses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Deployment and Evaluation of SciStream on OLCF's Advanced Computing Ecosystem (ACE)

The growing demand for real-time analysis, experimental steering, and decision-making in scientific workflows has created a need for tightly coupled integrations between experimental facilities and high-performance computing (HPC) systems. The Department of Energy’s Integrated Research Infrastructure (IRI) initiative highlights data streaming as a key capability for enabling memory-to-memory data transfers, bypassing the limitations of traditional store-and-forward models. SciStream is a toolkit developed by researchers at Argonne National Laboratory (ANL) to support such streaming by addressing cross-domain security, delegated authentication, and application transparency. We deployed and evaluated SciStream on the Oak Ridge Leadership Computing Facility’s (OLCF) Advanced Computing Ecosystem (ACE) infrastructure, leveraging the Olivine OpenShift cluster and its high-bandwidth Data Streaming Nodes (DSNs) as gateway nodes. Our evaluation included synthetic streaming workloads derived from IRI science workflows, a streaming simulator, and integration with RabbitMQ to handle low-level messaging. This report documents the deployment process, performance evaluation, and challenges encountered, along with opportunities for future improvements.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

An Analysis of Delay and Travel Times at Sao Paulo International Airport (AISP/GRU): Planning Based on Simulation Model

The occurrence of flight delays in Brazil, mostly verified at the ground (airfield), is responsible for serious disruptions at the airport level but also for the unchaining of problems in all the airport system, affecting also the airspace. The present study develops an analysis of delay and travel times at Sao Paulo International Airport/ Guarulhos (AISP/GRU) airfield based on simulation model. Different airport physical and operational scenarios had been analyzed by means of simulation. SIMMOD Plus 4.0, the computational tool developed to represent aircraft operation in the airspace and airside of airports, was used to perform these analysis. The study was mainly focused on aircraft operations on ground, at the airport runway, taxi-lanes and aprons. The visualization of the operations with increasing demand facilitated the analyses. The results generated in this work certify the viability of the methodology, they also indicated the solutions capable to solve the delay problem by travel time analysis, thus diminishing the costs for users mainly airport authority. It also indicated alternatives for airport operations, assisting the decision-making process and in the appropriate timing of the proposed changes in the existing infrastructure.

Santana, Erico Soriano Martins↗

NASA NTRS2019

MERRA-2 Data and Analytic Services at NASA GES DISC for Climate Extremes Study

NASA's climate reanalysis datasets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) contains numerous long-term atmosphere, land, and ocean data products from 1980-present. MERRA-2 datasets, such as precipitation, soil moisture, and temperature, have been used widely to study extreme events. The native archived MERRA-2 data files are day-file (hourly time interval) and month-file, containing up to 125 parameters in one file. Due to the large number of data files and volumes, it is challenging for users, especially the applications research community, to handle the original hourly data files for long time periods to analyze extreme events. In this presentation, we review MERRA-2 data for studies of extreme conditions, and demonstrate analytic services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). One of the current operational services, 'subsetter', allows users to download only specific data of interest, i.e. data selected by parameter, region, and time period. New services are under development that will provide more 'on-the-fly' statistical calculations when downloading data; improve efficiency when accessing long time-series data. We will provide additional "How-to" resources that include step-by-step instructions on data access and usage. We have tested restructuring of day-files in an optimized data cube, which has significantly improved system performance for accessing long time-series. Overall performance is associated with cube size and structure, data compression method, and how the data are accessed. The optimized data cube structure will enable better online analytic services for statistical analysis and extreme events mining. To demonstrate the service, we use an extreme drought associated with the anomalous 2016 monsoon over southern Asia. This prototype time-series service may be augmented in the cloud infrastructure in the future.

data access↗

NASA NTRS2018

Principles for a Practical Moon Base

NASA planning for the human space flight frontier is now coming into alignment with goals promoted by other planetary-capable national space agencies. US policy aims to achieve the “horizon goal” of Humans to Mars through significant learning about systems, operations, and partnerships in the cislunar and lunar-surface environment first. US Space Policy Directive 1 made this shift explicit: “the United States will lead the return of humans to the Moon for long-term exploration and utilization, followed by human missions to Mars and other destinations”. The stage is now set for sufficient public and private American investment in a wide range of lunar activities. Assumptions about Moon base architectures and operations are likely to drive the invention of requirements that will in turn govern development of systems, commercial-services purchase agreements, and priorities for technology investment. Yet some fundamental architecture-shaping lessons already captured in the literature are not evident drivers, and remain absent from most depictions of lunar base concepts. A prime example is general failure to recognize that most of the time (i.e., before and between intermittent human occupancy), a Moon base must be robotic: most of the activity, most of the time, must be implemented by robot agents rather than astronauts. This paper reviews key findings of a seminal robotic-base design-operations analysis commissioned by NASA in 1989. It culminates by discussing implications of these lessons for today’s Moon Village and SPD-1 paradigms: exploration by multiple actors; public-private partnership development and operations; cislunar infrastructure; production-quantity exploitation of volatile resources near the poles to bootstrap further space activities; autonomy capability that was frontier in 1989 but now routine within terrestrial industry. We need to engineer today’s generation of practical, justifiable, and inspirational Moon base concepts.

Sherwood, Brent↗