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At least 235 records · Page 13

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DOE OSTI2026

Updates to Anaerobic Digestion Pathways for Animal Manure in R&D GREET 2025 Rev.1

Renewable natural gas (RNG) produced from animal manure can significantly reduce greenhouse gas (GHG) emissions from the transportation sector, but accurate life cycle assessment (LCA) of this fuel depends on precise modeling of emissions from business-as-usual (BAU) manure manage ment. This report documents updates to the Research & Development version of the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (R&D GREET) model that attempt to improve that precision.

09 BIOMASS FUELS↗

DOE OSTI2022

Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations

In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO 2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets

This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.

Trinko, David A.↗

DOE OSTI2021

The U.S. Energy System and the Production of Sustainable Aviation Fuel From Clean Electricity

Jet fuel is relatively small in terms of energy consumption and carbon dioxide emissions (10% of U.S. transportation sector in 2021, expected to increase to 14% by 2050). Still airlines have ambitious goals to reduce their greenhouse footprints from carbon-neutral growth beginning this year to reducing greenhouse gas emission for international flights by 50% by 2050 compared to 2005 levels. The challenge is heightened by the longevity of the current fleet (30–50 years) and by the difficulty in electrifying the future fleet because only 5% of the commercial aviation greenhouse gas footprint is from regional flights that might, conceivably be electrified using foreseeable technology. Therefore, large amounts of sustainable aviation fuel will be needed to reach the aggressive targets set by airlines. Only 3 million gallons (11.4 ML) of sustainable aviation fuel (SAF) (with a heat of combustion totaling about 400 TJ = 0.0004 EJ) was produced in the U.S. in 2019 for a 26 billion gallon per year market (3.6 EJ/year). Fischer-Tropsch and ethanol oligomerization (alcohol-to-jet) are considered for producing SAF, including the use of renewable electricity and carbon dioxide. In sequencing the energy transition, cleaning the U.S. grid is an important first step to have the largest greenhouse gas emissions reduction. While carbon dioxide and clean electricity can potentially provide the SAF in the future, an ethanol oligomerization option will require less energy.

25 ENERGY STORAGE↗

DOE OSTI2022

Using Incremental Changes to Convert Lignocellulosic Feedstocks to Cellulosic Ethanol

One billion tons of biomass feedstocks have been identified for the production of renewable biofuels and biochemicals. This is one of the key carbon feedstocks to supply energy to the transportation sector for light duty, heavy duty and aviation fuels. Utilization of lignocellulosic feedstocks supports an improved energy security by reducing demand of petroleum imports, agricultural development, job creation, and reducing greenhouse gas emissions. To date, however, operational challenges have stymied the industrial production of large volumes of lignocellulosic-based fuels and chemicals. As a result, significant research investment has been led by the United States Department of Energy to understand and improve operational reliability at pioneer cellulosic biorefineries. In this perspective article lignocellulosic conversion technologies are described that have been adopted from the starch ethanol process. The developed process culminated in successful demonstration of 1,000-h integrated runs using several feedstocks, including switchgrass, energy sorghum, and two types of corn kernel fiber. This report highlights process development that solved several of the issues that plagued—and continue to plague—many in the cellulosic sugars space such as biomass feeding into equipment, high ash content, diversified co-product value, and others.

09 BIOMASS FUELS↗

DOE OSTI2024

Environmental life-cycle analysis of hydrogen technology pathways in the United States

Hydrogen is a zero-carbon energy carrier with potential to decarbonize industrial and transportation sectors, but its life-cycle greenhouse gas (GHG) emissions depend on its energy supply chain and carbon management measures (e.g., carbon capture and storage). Global support for clean hydrogen production and use has recently intensified. In the United States, Congress passed several laws that incentivize the production and use of renewable and low-carbon hydrogen, such as the Bipartisan Infrastructure Law (BIL) in 2021 and the Inflation Reduction Act (IRA) in 2022, which provides tax credits of up to $3/kg depending on the carbon intensity of the produced hydrogen. A comprehensive life-cycle accounting of GHG emissions associated with hydrogen production is needed to determine the carbon intensity of hydrogen throughout its value chain. In the United States, Argonne’s R&D GREET ® (Greenhouse Gases, Regulated emissions, and Energy use in Technologies) model has been widely used for hydrogen carbon intensity calculations. This paper describes the major hydrogen technology pathways considered in the United States and provides data sources and carbon intensity results for each of the hydrogen production and delivery pathways using consistent system boundaries and most recent technology performance and supply chain data.

Elgowainy, Amgad↗

DOE OSTI2024

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

Computer Science↗

DOE OSTI2023

Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches

The air conditioning (AC) system is the primary building end-use contributor to the peak demand for energy. The energy consumed by this system has grown as fast as it has in the last few decades, not only in the residential section but also in the industry and transport sectors. Therefore, to combat energy crises, urgent actions on energy efficiency should be taken to support energy security. Consequently, the faults in AC system components increase energy consumption due to the degradation of the system’s performance and the losses in the energy conversion procedure. In this work, AC system fault detection and diagnosis (FDD) methods are investigated to propose analytic tools to identify faults and provide solutions to those problems. The analysis of existing work shows that data-driven approaches are more accurate for both soft and hard fault detection and diagnosis in AC systems. Therefore, the proposed methods are not accurate for simultaneous fault detection, while in some works, authors tested the method with several faults separately without investigating scenarios that combine more than one fault. Moreover, this study shows that integrating data-driven approaches requires deploying an optimal sensing and measurement architecture that can detect a maximum number of faults with minimally deployed sensors. The new sensing, information, and communication technologies are discussed for their integration in AC system monitoring in order to optimize system operation and detect faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

Nazaralizadeh, Solmaz (ORCID:0009000737004039)↗

DOE OSTI2024

Assessment of Economic Viability of Direct Current Fast Charging Infrastructure Investments for Electric Vehicles in the United States

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Durability Study of a High-Pressure Common Rail Fuel Injection System Using Lubricity Additive-Dosed Gasoline-Like Fuel—Improved Endurance with Upgraded Hardware

Gasoline compression ignition (GCI) is a promising combustion technology that can help the commercial transportation sector achieve operational flexibility and meet upcoming criteria pollutant regulations. However, high-pressure fuel injection systems (>1000 bar) are needed to enable GCI and fully realize its benefits compared to conventional diesel combustion. This work is a continuation of previous durability studies that identified three key technical risks after running gasoline-like fuel through a heavy-duty, common rail injection system: (i) cavitation damage to the inlet check valve of the high-pressure pump, (ii) loss of injector fueling capacity, (iii) cavitation erosion of the injector nozzle holes. Upgraded hardware solutions were tested on a consistent 400- to 800-hour NATO durability cycle with the same gasoline-like fuel as previous studies. The upgraded pump showed no signs of abnormal wear or cavitation damage to the inlet check valve. In contrast to previous studies, there were no signs of pump performance degradation observed after 400 hours of testing. Material selection and design upgrades were also made to the injector which, only showed a 6.5% loss in fueling capacity after 800 hours of durability testing compared to 49.3% previously. Finally, geometric nozzle hole features such as higher inlet radius of curvature and higher K-factor were found to correlate with reduced cavitation erosion. However, mitigation of eccentric radial needle motion (i.e., wobble) is likely needed to further suppress cavitation. In general, the results from this study indicate there are viable hardware-based solutions for improving the endurance of high-pressure systems when running with gasoline-like fuel.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Viability Assessment of Wind and Solar Renewable Energy Generation in Support of Nationwide Vehicle Electrification

In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals, multiple computational frameworks are used to locate suitable land for renewable infrastructure development, and to quantify spatiotemporal variations in the potential energy generation and financial viability of development sites across the Unites States. First, the Oak Ridge Siting Analysis for power Generation Expansion tool (OR-SAGE) is used to assess the suitability of land for potential wind and solar energy development across the contiguous U.S. From there, resource data from the National Solar Radiation Database (NSRDB) and the Wind Integration National Dataset (WIND) are used in concert with the National Renewable Energy Laboratory (NREL) Renewable Energy Potential (ReV) model to calculate the variation in potential generation capacity for each resource. Additionally, the capital and operational expenditures are calculated for an example configuration of each renewable technology. These measures are then used to calculate the levelized cost of energy (LCOE) of potential sites. All of these results are then processed and analyzed to determine where in the U.S. solar and wind energy are most viable. This viability is based on available generation potential, consistency and stability of energy generation over time, and economic viability with respect to LCOE.

Miller, Brandon [ORNL] (ORCID:0009000300169201)↗

DOE OSTI2025

Regional Analysis for an Economically and Environmentally Viable Transition to Heavy-Duty Vehicles with Alternative Powertrains

The transportation sector is responsible for a significant portion of greenhouse gas emissions. Within the sector, truck freight is responsible for a third of the associated emissions. Alternative powertrains are seen as a viable approach to significantly reduce these emissions. Prior to making a large-scale transition, it is important to consider the following questions: will the power grid support a transition to alternative powertrains?; will the transition truly reduce carbon emissions?; and will the transition impose an unnecessary economic burden on companies within the industry? The answer to these questions, however, can vary by geography, maturity/capacity of the energy distribution network or predicted vehicle load. We focus on the latter two questions, investigating the variation in estimated total cost of ownership and carbon emissions across the United States at the zip code level for both heavy-duty battery electric vehicles and heavy-duty fuel cell electric vehicles. As a benchmark, we compare estimated emissions and costs of alternative powertrain vehicles to that of conventional heavy-duty vehicles powered by diesel internal combustion engines. This work highlights areas with electric grids primed for a transition to alternative powertrain vehicles, such as the Pacific Northwest, and areas that require further infrastructure investment in renewables, such as many of the Mountain states, Missouri, and Florida. Additionally, this work illustrates the current advantages in carbon emissions of battery electric vehicles compared to fuel-cell electric vehicles, while providing insights into required regional investments for narrowing the gap.

Goulet, Nate [ORNL] (ORCID:0000000237314965)↗

DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗

NASA NTRS2001

Experiments and Model Development for the Investigation of Sooting and Radiation Effects in Microgravity Droplet Combustion

Today, despite efforts to develop and utilize natural gas and renewable energy sources, nearly 97% of the energy used for transportation is derived from combustion of liquid fuels, principally derived from petroleum. While society continues to rely on liquid petroleum-based fuels as a major energy source in spite of their finite supply, it is of paramount importance to maximize the efficiency and minimize the environmental impact of the devices that burn these fuels. The development of improved energy conversion systems, having higher efficiencies and lower emissions, is central to meeting both local and regional air quality standards. This development requires improvements in computational design tools for applied energy conversion systems, which in turn requires more robust sub-model components for combustion chemistry, transport, energy transport (including radiation), and pollutant emissions (soot formation and burnout). The study of isolated droplet burning as a unidimensional, time dependent model diffusion flame system facilitates extensions of these mechanisms to include fuel molecular sizes and pollutants typical of conventional and alternative liquid fuels used in the transportation sector. Because of the simplified geometry, sub-model components from the most detailed to those reduced to sizes compatible for use in multi-dimensional, time dependent applied models can be developed, compared and validated against experimental diffusion flame processes, and tested against one another. Based on observations in microgravity experiments on droplet combustion, it appears that the formation and lingering presence of soot within the fuel-rich region of isolated droplets can modify the burning rate, flame structure and extinction, soot aerosol properties, and the effective thermophysical properties. These observations led to the belief that perhaps one of the most important outstanding contributions of microgravity droplet combustion is the observation that in the absence of asymmetrical forced and natural convection, a soot shell is formed between the droplet surface and the flame, exerting an influence on the droplet combustion response far greater than previously recognized. The effects of soot on droplet burning parameters, including burning rate, soot shell dynamics, flame structure, and extinction phenomena provide significant testing parameters for studying the structure and coupling of soot models with other sub-model components.

Choi, Mun Young↗

NASA NTRS2005

Design, Development, and Innovation of an Interactive Multimedia Training Simulator for Responding to Air Transportation Bomb Threats

This paper describes an interactive multimedia simulator for air transportation bomb threat training. The objective of this project is to improve the air transportation sector s capability to respond to bomb threats received by commercial airports and aircraft. The simulator provides realistic training on receiving and responding to a variety of bomb threats that might not otherwise be possible due to time, cost, or operational constraints. Validation analysis indicates that the use of the simulator resulted in statistically significant increases in individual ability to respond to these types of bomb threats.

Chung, Christopher A.↗

NASA NTRS2020

High Pressure Transcritical Combustion (HPTC Combustion)

Combustion is the predominant source of the energy in the US and worldwide, and an overwhelming majority in the transportation sector, particularly liquid hydrocarbons (i.e., gasoline, diesel and jet fuel). Engine manufacturers move to higher operating pressures, above and beyond the critical pressures of the fuel, in order to optimize performance, maximize efficiency and reduce pollutant formation. The design of the next generation of engines, however, are limited by a lack of fundamental understanding of the thermo-physical properties, fluid behavior and chemical kinetics at these high pressures. Terrestrial laboratory studies are hampered by the ubiquitous buoyant flow that renders fundamental experiments difficult and/or impossible to interpret. Studying high pressure transcritical phenomena in microgravity allows researchers to investigate the fundamental aspects of phase change, chemical kinetics and property evaluation in a simplified geometry without the complicating influence of a buoyancy-induced flow.

Combustion↗

NASA NTRS2024

Characterizing the Structure of Lithium Metal Batteries using Local Ultrasonic Resonance Spectroscopy (LURS)

Safe use of high energy density batteries is critical for electrifying aerospace and other transportation sectors. Nondestructive evaluation (NDE) techniques are being developed for characterizing battery structures throughout their lifetimes so that damage mechanisms can be better understood, and prognostic tools can be developed. In this study Local Ultrasonic Resonance Spectroscopy (LURS) is applied to lithium metal pouch cell batteries of a variety of form-factors that are brought from fabrication to end of life under different charge rates, temperatures, and loading scenarios. LURS data shows improved sensitivity to changes in structure in comparison to amplitude-based methods, allowing differences in damage development to be identified as well as giving opportunities for quality control in manufacturing.

Matthew Webster↗