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DOE OSTI2025

Summary of Expansions and Updates in R&D GREET ® 2024

The research and development (R&D) version of Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET ® ) model, or R&D GREET, is developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. R&D GREET is a life cycle analysis (LCA) model, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy technology systems. Argonne has expanded and updated the model in various areas in R&D GREET 2024.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Summary of Expansions and Updates in R&D GREET® 2024 Rev.1

The research and development (R&D) version of Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model, or R&D GREET, is developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. R&D GREET is a life cycle analysis (LCA) model, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy technology systems. Argonne has expanded and updated the model in several areas in R&D GREET 2024 Rev.1. This report provides a summary of the expansions and updates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Draft Prototype Microreactor Transportation Safety Program

Microreactors are compact reactors capable of producing less than 50 megawatts of electrical energy. Typically, these reactors are factory-fabricated and designed to be easily transportable by truck, rail, vessel, or air. Microreactor designs often assume that the unit can be transported containing either unirradiated or irradiated fuel. The interest in microreactors is driven by several factors, including the need to generate power on at remote locations, at military installations, at facilities such as data centers, and in areas recovering from natural disasters. The U.S. Department of Defense is actively pursuing the microreactor concept to meet the increasing energy demands of military operations that require portable and dense power sources. Commercial vendors are also exploring microreactor concepts. The report Microreactor Transportation Emergency Planning Challenges (Maheras et al. 2024) outlined the emergency planning challenges associated with the transportation of microreactors by road, rail, and by barge/ship. The successful commercial deployment and redeployment of microreactors will also require the development of microreactor transportation safety programs. The elements in these safety programs are not specific to microreactors; however, the transport of microreactors may pose unique challenges in these areas. This report builds on the report Microreactor Transportation Emergency Planning Challenges (Maheras et al. 2024) and develops the elements of a prototype microreactor transportation safety program that describes the elements that should be contained vendor-developed microreactor transportation safety programs, identifying the unique elements associated with microreactor transport. This will provide vendors and their transportation contractors a basis for their transportation planning and will accelerate the commercial deployment and redeployment of microreactors by identifying those issues unique to microreactor transport. The emphasis of this report is on highway transport of microreactors. This is based on a U.S. Nuclear Regulatory Commission transportation package approval strategy of crawl-walk-run, where transport by highway is evaluated first (Coles et al. 2021, 2024, Maheras et al. 2021), then other surface modes (rail and barge/ship), and finally air transport. Evaluation of maritime transport of microreactors was recently initiated (Rigato et al. 2024, Maheras et al. 2025). The report first discusses microreactors in general and microreactor transportation safety program planning assumptions. The report then provides a description of the transportation safety planning process and provides an extensive discussion of the elements of transportation safety programs. Specific elements examined included transportation roles and responsibilities, transportation planning, transportation mode and route selection, carrier selection, transportation packaging, advance notification of shipments, public information and communications, emergency response plans and procedures, inspections, security, safe parking, shipment tracking, weather and road conditions, medical preparedness, training and exercises, and program evaluation. The report then identifies the unique elements of a transportation safety program associated with microreactor transport. These unique elements were in the areas of: the unusual nature of microreactor designs, compensatory measures, increased radiation dose rates in the vicinity of microreactors, transportation package approval versus 10 CFR 50.59, and the use of a risk-informed transportation package approval process.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Transportation Analysis of the Fermilab High-Beta 650 MHz Cryomodule

The prototype High-Beta 650 MHz cryomodule for the PIP-II project will be the first of its kind to be transported internationally, and the round trip from FNAL to STFC UKRI will use a combination of road and air transit. Transportation of an assembled cryomodule poses a significant technical challenge, as excitation can generate high stresses and cyclic loading. To accurately assess the behavior of the cryomodule, Finite Element Analysis (FEA) was used to analyze all major components. First, all individual components were studied. For the critical/complex components, the analysis was in fine detail. Afterwards, all models were brought to a simplified state (necessary for computational expenses), verified to have the same behavior as their detailed counterparts, and combined to form larger sub-assemblies, with the ultimate analysis including the full cryomodule. We report the criteria for acceptance and methods of analysis, and results for selected components and sub-assemblies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Multidimensional Modeling of Mixture Formation in a Hydrogen-Fueled Heavy-Duty Optical Engine With Direct Injection

Hydrogen (H 2 ), as a carbon-free fuel, is considered as one of the most promising solutions to reduce the carbon footprint of hard-to-decarbonize energy and transportation sectors. As such, hydrogen-fueled internal combustion engines (H 2 ICEs) have recently been receiving increasing attention, particularly in applications such as on-road/off-road heavy-duty transport and combined heat and power. The direct injection (DI) of gaseous hydrogen into the combustion chamber offers great potential for achieving high power density and high engine efficiency, while mitigating the risk of backfire and reducing pre-ignition. However, the numerical simulation of H 2 DI system remains a formidable challenge associated with the high computational cost of reproducing compressible supersonic flow and shocks in narrow injector passages and in near-nozzle regions. In general, there is a lack of well-established and validated practices for the modeling of high-pressure H 2 DI in large-bore engines. Here, to this end, this study focuses on computational fluid dynamics (CFD) modeling of the mixture formation process in a heavy-duty optical engine employing a medium-pressure H 2 DI system. Both large eddy simulations (LES) and Reynolds Averaged Navier–Stokes (RANS) simulations are performed and evaluated against optical data. Gaseous hydrogen is injected into the combustion chamber via a centrally located outward opening hollow-cone injector at a pressure of 40 bar. Simulations are carried out for two injection timings, namely, −120 and −60 °CA. The numerical predictions for H 2 distribution in different horizontal and vertical planes during the compression stroke are systematically compared against optical data obtained through planar laser-induced fluorescence (PLIF) measurements. Overall, the LES approach using the Dynamic Structure model is found to have good predictive capabilities for the early jet penetration in terms of length and shape, as well as the later H 2 distributions. However, the unsteady RANS approach with the renormalization group $k - ϵ$ model, which is widely used by industry to model heavy-duty ICEs, significantly underpredicts the H 2 mixing, even at similar mesh resolution to that used in LES. These results indicate that there is a need for the improvement of mixing submodels within the RANS approach when applied to H 2 DI simulations.

LES↗
DOE OSTI2026

Used Nuclear Fuel Management Using the Next Generation System Analysis Model

The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.

Craig, Brian [Argonne National Laboratory (ANL)]↗
DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗
DOE OSTI2025

Exploring urban typologies using comprehensive analysis of transportation dynamics

Abstract As urban areas continue to expand and develop, categorizing cities into typologies offers a valuable framework for understanding metropolitan dynamics and fostering inter-city collaboration. However, existing typologies related to urban mobility have limitations, failing to consider cities within a single large urban region and often overlooking crucial dimensions such as trip demand and traffic flow. In this paper, we introduce a transportation-focused characterization for cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: trip demand, road network, multi-modal network, traffic flow, and land use. Specifically, for the trip demand dimension, we include metrics capturing residents’ trip characteristics, such as mode share, intra-city trips, and inter-city trips. Additionally, we analyze the purpose of trips entering the city to gain a deeper understanding of incoming trip patterns. In the traffic flow dimension, we examine metrics like vehicle miles traveled, delay, and congestion to assess the traffic conditions on the street network. These, combined with other dimensions, provide a comprehensive view of a city’s transportation dynamics. Using unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work Cities; Job and Activity Magnet Cities; Anchor Cities; Multi-modal Cities; Hyper-connected Cities; Low-density Residential Cities; Medium-density Residential Cities; and Mixed-use Residential Cities. Our findings show that many clusters are strongly influenced by trip demand and traffic flow metrics. Finally, we examine the practicality of this typology and its potential to guide collaborative transportation management strategies. The typologies provide a foundation for dialogue among Bay Area cities, focusing on evaluating shared characteristics and leveraging successes or challenges to develop unified strategies for transportation management.

Kuncheria, Anu↗
DOE OSTI2023

Driving in the dark: Deciphering nighttime driver detection of free-ranging roadside wildlife

Wildlife-vehicle collisions are dangerous for motorists; however, few studies have addressed driver detection of roadside animals, and none have evaluated detection of free-ranging wildlife. Here, we used 24 volunteer drivers, infrared videography, a 75-km route, and free-ranging wildlife to quantify factors influencing (1) probability of wildlife detection, (2) detection distance, and (3) probability of dangerous encounters (i.e., detection distance < distance required for braking) for multiple species at night in South Carolina, USA. Detection probability of white-tailed deer (Odocoileus virginianus) was impacted by multiple driver, animal, and roadside factors. Deer detection distances increased by 20.99 m when drivers used high-beam headlights and 23.36 m when deer were moving but decreased by 0.71 m for every minute into a drive. Every encounter with wild pigs (Sus scrofa) and most encounters with small mammals were considered dangerous. Our findings suggest most drivers cannot safely detect deer, wild pigs, and small mammals at night.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

New roads and challenges for fuel cells in heavy-duty transportation

The recent release of hydrogen economy roadmaps for several major countries emphasizes the need for accelerated worldwide investment in research and development activities for hydrogen production, storage, infrastructure and utilization in transportation, industry and the electrical grid. Due to the high gravimetric energy density of hydrogen, the focus of technologies that utilize this fuel has recently shifted from light-duty automotive to heavy-duty vehicle applications. Decades of development of cost-effective and durable polymer electrolyte membrane fuel cells must now be leveraged to meet the increased efficiency and durability requirements of the heavy-duty vehicle market. This Review summarizes the latest market outlooks and targets for truck, bus, locomotive and marine applications. Required changes to the fuel-cell system and operating conditions for meeting Class 8 long-haul truck targets are presented. The necessary improvements in fuel-cell materials and integration are also discussed against the benchmark of current passenger fuel-cell electric vehicles.

08 HYDROGEN↗
DOE OSTI2021

Advanced Multi-Functional Diesel Particulate Filters (CRADA 455)

Diesel engines continue to play crucial roles in many transportation and off-road applications. More specifically, heavy duty diesel engines are expected to remain the only practical option for long-range hauling and many off-road industrial and agricultural applications for decades. As such, the U.S. Department of Energy places high priority on the development and optimization of advanced diesel engine and diesel aftertreatment systems for maximum efficiency and minimum emissions. This collaborative effort between PNNL and Deere & Co. is aimed at enabling the industrially relevant potential of DOCF (diesel oxidation catalyst-on-filter) technology towards the development of the next generation of advanced aftertreatment systems with high performance, superior flexibility, and reduced backpressure.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035

From daily commutes to cross-country road trips, millions of light-duty vehicles are on the road every day. The transportation sector is one of the United States’ largest sources of greenhouse gas emissions, and fuel is an important cost for drivers. The period from 2025-2035 could bring the most fundamental transformation in the 100-plus year history of the automobile. Battery electric vehicle costs are likely to fall and reach parity with internal combustion engine vehicles. New generations of fuel cell vehicles will be produced. Connected and automated vehicle technologies will become more common, including likely deployment of some fully automated vehicles. These new categories of vehicles will for the first time assume a major portion of new vehicle sales, while internal combustion engine vehicles with improved powertrain, design, and aerodynamics will continue to be an important part of new vehicle sales and fuel economy improvement.This study is a technical evaluation of the potential for internal combustion engine, hybrid, battery electric, fuel cell, nonpowertrain, and connected and automated vehicle technologies to contribute to efficiency in 2025-2035. In addition to making findings and recommendations related to technology cost and capabilities, Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy - 2025-2035 considers the impacts of changes in consumer behavior and regulatory regimes.

MARCHANT, GARY↗
DOE OSTI2022

The contribution of bioenergy to the decarbonization of transport: a multi-model assessment

The expected growth in the demand for passenger and freight services exacerbates the challenges of reducing transport GHG emissions, especially as commercial low-carbon alternatives to petroleum fuels are limited for shipping, air and long-distance road travel. Biofuels can offer a pathway to significantly reduce emissions from these sectors, as they can easily substitute for conventional liquid fuels in internal combustion engines. In this paper, we assess the potential of bioenergy to reduce transport GHG emissions through an analysis leveraging various integrated assessment models and scenarios, as part of the 33rd Energy Modeling Forum study (EMF-33). Furthermore, we find that bioenergy can contribute a significant, albeit not dominant, proportion of energy supply to the future transport sector: in scenarios aiming to keep the temperature increase below 2 °C by the end of the twenty-first century, models project that in 2100 bioenergy can provide on average 42 EJ/yr (ranging from 5 to 85 EJ/yr) for transport (compared to 3.7 EJ in 2018), mainly through lignocellulosic fuels. This makes up 9–62% of final transport energy use. Only a small amount of bioenergy is projected to be used in transport through electricity and hydrogen pathways, with a larger role for biofuels in road passenger transport than in freight. The association of carbon capture and storage (CCS) with bioenergy technologies (BECCS) is a key determinant in the role of biofuels in transport, because of the competition for biomass feedstock to provide other final energy carriers along with carbon removal. Among models that consider CCS in the biofuel conversion process the average market share of biofuels is 21% in 2100 (ranging from 2 to 44%), compared to 10% (0–30%) for models that do not. Cumulative direct emissions from the transport sector account for half of the emission budget (from 306 to 776 out of 1,000 GtCO 2 ). However, the carbon intensity of transport decreases as much as other energy sectors in 2100 when accounting for process emissions, including carbon removal from BECCS. Lignocellulosic fuels become more attractive for transport decarbonization if BECCS is not feasible for any energy sectors. Since global transport service demand increases and biomass supply is limited, its allocation to and within the transport sector is uncertain and sensitive to assumptions about political as well as technological and socioeconomic factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

DOE Standard Canister Loading Methodology Poster

Over 250 different types of spent nuclear fuel (SNF) are managed by the U.S. Department of Energy (DOE) at the Idaho National Laboratory (INL). The current configuration of SNF at INL is not transportable off-site. In the Yucca Mountain Repository License Application, the SNF was to be loaded into DOE Standard Canisters which are capable of storage, transportation, and eventual disposal using the proper overpacks. The DOE Standard Canister will allow INL to place the SNF in a Road-Ready configuration in preparation for transportation off-site. The focus of this work is development of a general loading methodology for the DOE Standard Canister. This methodology is to be general enough that it will apply to all SNF to be loaded into the DOE Standard Canister. The main sections of the methodology to be outlined are prerequisites before loading (including any issues with materials such as inserts or baskets), loading of SNF, and canister closure and quality assurance (including welding and inspection). Each subsection will analyze and provide the bases for each step.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2023

Designing a GIS-based supply chain for producing carinata-based sustainable aviation fuel in Georgia, USA

Carinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost-effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata-based SAF production. This study aims to design a supply chain model for carinata-based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed-integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GIS-based network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their locations over those minimum-cost routes. With this supply chain configuration, the minimum price of SAF was $\$$0.92 L –1 , which is $\$$0.44 higher than conventional aviation fuel (CAF). The associated carbon intensity of SAF was estimated at 940.7 g CO 2 e L –1 , a reduction of 66% relative to the carbon intensity of equivalent CAF. The study found that a carbon tax (or subsidy) of $\$$230.48 t CO 2 e –1 would be needed to overcome the cost differential with CAF and promote carinata-based SAF in Georgia.

09 BIOMASS FUELS↗
DOE OSTI2020

Gate field effects on the topological insulator BiSbTeSe 2 interface

Interfaces between two topological insulators are of fundamental interest in condensed matter physics. Inspired by experimental efforts, we study interfacial processes between two slabs of BiSbTeSe 2 (BSTS) via first principles calculations. Topological surface states are absent for the BSTS interface in its equilibrium separation, but our calculations show that they appear if the inter-slab distance is greater than 6 Å. More importantly, we find that topological interface states can be preserved by inserting two or more layers of hexagonal boron nitride between the two BSTS slabs. In experiments, the electric current tunneling through the interface is insensitive to back gate voltage when the bias voltage is small. Using a first-principles based method that allows us to simulate the gate field, we show that at low bias, the extra charge induced by a gate voltage resides on the surface that is closest to the gate electrode, leaving the interface almost undoped. This provides clues to understand the origin of the observed insensitivity of transport properties to back voltage at low bias. Our study resolves a few questions raised in experiment, which does not yet offer a clear correlation between microscopic physics and transport data. We provide a road map for the design of vertical tunneling junctions involving the interface between two topological insulators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗