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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Techno-Economic Simulation Results Using dGeo for EGS-Based District Heating in the Northeastern United States

This dataset presents the results of techno-economic simulations performed using the Distributed Geothermal Market Demand Model (dGeo) to evaluate the feasibility of Enhanced Geothermal Systems (EGS)-based district heating in the Northeastern United States. Developed by the National Renewable Energy Laboratory (NREL), dGeo is a geospatially resolved, bottom-up modeling framework designed to explore the deployment potential of geothermal distributed energy resources. The dataset, created as part of the Cornell EGS Ground-Truthing Project, provides census tract-level data that includes inputs and outputs such as thermal demand, road length, energy prices, geothermal system sizing, annual energy contributions from geothermal and natural gas peaking boilers, system capital costs (CAPEX), operation and maintenance costs (OPEX), and the levelized cost of heat (LCOH). Key simulation parameters include geothermal gradients, measured well depths, production temperatures, and district heating piping lengths based on S1400 neighborhood road lengths. The simulations assume a target bottom hole temperature of 80C and the development of new district heating networks in each census tract.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022-2023, Seward Peninsula, Alaska

Snow/ground interface temperature measurements were collected at the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) and at the Kougarok Road Site at mile marker 64 (KG_MM64) on the Seward Peninsula, Alaska. Data were collected between October 1, 2022 to September 18, 2023 using iButton Link DS1921G-F5# Thermochron miniature temperature sensors (https://www.ibuttonlink.com/products/ds1921g) and Tinytag TGP-4017 internal sensors (https://www.micronmeters.com/product/tgp-4017-internal-sensor-40-to-85-c-40-f-to-185-f) deployed across the Kougarok and Teller sites. These sensors are a cost-efficient way to collect snowpack temperatures at a higher spatial resolution than what is normally achieved. iButton data were collected every 4 hours, while Tinytag data were collected every 30 minutes. In total, data were collected from 196 iButtons and 26 Tinytags. This dataset contains four *.csv files of near-ground surface temperatures at various locations throughout each study site and two *.kml files of sensor locations. Data were collected throughout the snow cover season so that snowpack characteristics could be derived using the temperature data. Sensors were placed both inside and outside of vegetation to better capture the spatial variability of snow properties across each domain.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) 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 OSTI2024

Continuous snow depth, ground interface temperature and shallow soil temperature measurements from 2021-10-1 to 2022-6-14, Seward Peninsula, Alaska

The dataset contains co-located snow depth, ground interface temperature, and shallow soil temperature measured at 98 discrete locations in a watershed located along the Nome-Teller road at mile marker 27 (referred to as T27), and at 53 discrete locations on a hillslope located along the Kougarok road at mile marker 64 (referred to as K64), in Seward Peninsula, Alaska. The dataset aims to understand the local heterogeneity of snow depth, snow temperature, and soil temperature dynamics and their interactions in a discontinuous permafrost region. The dataset is also valuable to train and evaluate machine learning and physical models to predict snow depth or the impact of snow depth on ground surface temperature. At each location, temperatures above and below the ground surface were measured by a pair of vertically deployed distributed temperature profiling probes designed based on Dafflon et al (2022). The probes have high precision temperature sensors spaced at 5 or 10 cm. The mean daily snow depth was estimated by identifying the pair of consecutive sensors with maximum drop of daily temperature high-frequency fluctuations. The ground interface temperature was measured by the sensor located 1-5 cm above the ground surface at 15-minute intervals. The shallow soil temperature was measured by the sensor located 1-5 cm below the ground surface at 15-minute intervals. The dataset includes a description of the probe locations in the "Probe_locations_*.csv" file and the 3 data files (Snow_depths_*.csv, Ground_interface_temperatures_*.csv, Shallow_soil_temperatures_*.csv). * is either T27 or K64, which are the two study sites. In each data file, each column corresponds to a measurement location. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv. A more detailed description of data processing, along with an updated dataset incorporating multiple seasons and improved snow depth estimation is available at https://doi.org/10.15485/2480365 (Wang et al., 2025a, Wang et al., 2025b).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

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.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

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape: Modeling Archive

This modeling archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025). This archive contains model input files and outputs from landscape-scale simulations conducted using ELM, the land model component of the Department of Energy’s Energy Exascale Earth System Model (E3SM), at the Council NGEE Arctic field site (Council Road mile marker 71) on Alaska’s Seward Peninsula. Input data and model output from two sets of ELM simulations are provided. The first set of simulations were conducted with the two default ELM Arctic plant functional types (PFTs; broadleaf deciduous boreal shrub and a C3 grass) and the second set of simulations were conducted with a set of nine Arctic-specific PFTs including nonvascular mosses and lichens, graminoids, forbs, evergreen dwarf shrubs, three height classes of deciduous shrubs (dwarf, low, and low to tall), and deciduous alder shrubs (Sulman et al., 2021). Parameter names and major parameter changes in the Arctic-specific PFT configuration are described in Sulman et al. (2021) and archived in the Sulman et al. (2021) dataset (see below). Simulations were spatially explicit, covering an approximately 6.4X3.3 km domain at the Council site with a spatial resolution of 100 m for a total of 2,112 simulated grid cells under each ELM PFT configuration. The modeling archive contains meteorological forcing (seven *.nc files and one *.txt file), a domain definition file (one *.nc files), land surface configuration files (two *.nc files), parameter files (two *.nc files), annual ELM output files spanning 1980-2014 (68 *.nc files), and a User’s Guide (*pdf file). Additional information on the provided files is in the “Modeling Archive Contents” section of the User’s Guide. Model outputs are aggregated to the column scale (i.e. PFT-specific outputs are not provided here).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-xTL NEON Toolik (TOOL)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xTL NEON Toolik (TOOL). This is the FLUXNET version of the carbon flux data for the site US-xTL NEON Toolik (TOOL) produced by applying the standard ONEFlux (1F) software. Site Description - The Toolik field site is located in a remote wilderness area at the Toolik Field Station which is on land managed by the Bureau of Land Management. The area is underlain by continuous permafrost, which exerts a major influence on hydrology and the distribution, structure, and function of terrestrial and aquatic ecosystems. Because of its location between the Brooks Range and the coastal plain, the vegetation and soils at TOOL are representative of much of the Alaskan foothills tundra. This terrestrial field site is collocated with NEON’s Toolik Lake aquatic site. The closest city, Fairbanks, Alaska is 400 miles away. The Dalton Highway (also known as the Haul Road or Pipeline Road), which traverses the middle of the state from Fairbanks to Prudhoe Bay, provides access to the area.

Network), NEON (National Ecological Observatory↗
DOE OSTI2026

AmeriFlux US-NYm NYSM - Belleville

This is the AmeriFlux version of the carbon flux data for the site US-NYm NYSM - Belleville. Site Description - Located in a grass/crop field setting. It is a in a flat, open field with high vegetation to the northeast, paved road to the east, and a small treeline to the north. Obstructions within 100m include trees, paved road, high crops. The soil type is Collamer silt loam, bedrock substratum. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2021

Evaluation of Safety Standards for Fuel System and Fuel Container Integrity of Alternative Fuel Vehicles

There are two Federal Motor Vehicle Safety Standards (FMVSS) in place that specify requirements for integrity of the fuel system and fuel container on compressed natural gas (CNG) fuel vehicles. These are FMVSS Nos. 303, “Fuel system integrity of compressed natural gas vehicles,” and FMVSS No. 304, “CNG fuel container integrity.” At this time, no FMVSS are defined for the fuel system and fuel container integrity of propane and liquefied natural gas (LNG) vehicles or fuel system integrity requirements for heavy-duty CNG vehicles on the road. However, there are voluntary industry design standards and best practices, as well as regulations defined in other countries. FMVSS No. 303 specifies requirements for the integrity of the CNG fuel system of light-duty vehicles and school buses, and FMVSS No. 304 specifies requirements for fuel container integrity on all CNG vehicles. FMVSS No. 304 applies to containers used for vehicle propulsion, whereas containers used to transport CNG and transportation of CNG containers are regulated by the DOT Pipeline and Hazardous Materials Safety Administration. FMVSS Nos. 303 and 304 are performance-based standards to consistently test and validate equipment and are not design restrictive. Despite the increasing number of natural gas and propane medium- and heavy-duty vehicles on the road, there are no FMVSS fuel system integrity requirements beyond light-duty and school buses for CNG vehicles and no FMVSS fuel system integrity requirements for propane vehicles. NHTSA is researching fuel system safety for medium- and heavy-duty natural gas and propane vehicles to update FMVSS Nos. 303 and 304. NHTSA is also researching current best practices and standards for high pressure fuel tanks in motor vehicles as they may apply to FMVSS No. 304.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

A Rechargeable Carbon-Oxygen Battery for Efficient, Low-Cost Energy Storage

As part of the Cyclotron Road program, Noon Energy Inc investigated the feasibility of a new class of rechargeable flow battery for long-duration energy storage at the 100+ hour storage capacities needed to make intermittent solar/wind power available 24/7 year-round. Whereas conventional batteries store energy in relatively expensive metals like cobalt, lithium, and vanadium, the new battery stores energy in abundant, ultra-low-cost carbon and oxygen, storage media that cost well below $1 per kWh capacity (less than the cost of their containers). It also can operate with a high roundtrip energy efficiency of ~80% and has 10x higher energy density than conventional aqueous flow batteries, even higher than lithium ion batteries at system level. These properties make it suitable for a variety of potential stationary energy storage applications as well as for electric transportation vehicles (long-haul marine shipping and trucking), so determining the best product-market fit and market entry was a key outcome of Noon’s Cyclotron Road fellowship. Noon had demonstrated initial materials development and proof-of-principle testing before the fellowship. Overall, within the fellowship Noon carried out key initial technology development in market applications, system design, risk assessment and prototyping.

25 ENERGY STORAGE↗
DOE OSTI2020

Y-12 Groundwater Protection Program Monitoring Well Inspection and Maintenance Plan

This plan describes the systematic approach for: inspecting the physical condition of monitoring wells at Y-12, determining maintenance needs that extend the life of a well, and identifying those wells that no longer meet acceptable monitoring well design or well construction standards and require plugging and abandonment. The inspection and maintenance of groundwater monitoring wells is one of the primary management strategies of the Y-12 Groundwater Protection Program (GWPP) Management Plan, that is, the “proactive stewardship of the extensive monitoring well network at Y-12" (Consolidated Nuclear Security, L.L.C. [CNS], 2018). Effective stewardship, and a program of routine inspections of the physical condition of each monitoring well, ensures that representative water-quality samples and hydrologic data are obtained from the well network and protects the subsurface environment. In accordance with the Y-12 GWPP Monitoring Optimization Plan (MOP) for Groundwater Monitoring Wells at the Y-12 National Security Complex, Oak Ridge, Tennessee (CNS, 2017), the status designation (active or inactive) for each well determines the scope and extent of well inspections and maintenance activities. This plan, in conjunction with the above document, formalizes the GWPP approach to focus available resources on monitoring wells which provide the most useful data, and for that reason the GWPP inspects and performs maintenance on the wells sampled by the GWPP. This plan applies to groundwater monitoring wells installed at Y-12 and the related waste management facilities located within the three hydrogeologic regimes: (1) the Bear Creek Hydrogeologic Regime (Bear Creek Regime), (2) the Upper East Fork Poplar Creek Hydrogeologic Regime (East Fork Regime), and (3) the Chestnut Ridge Hydrogeologic Regime (Chestnut Ridge Regime). The Bear Creek Regime encompasses the section of Bear Creek Valley (BCV) immediately west of Y-12. The East Fork Regime encompasses most of the Y-12 process, operations, and support facilities in BCV west of Scarboro Road. The Chestnut Ridge Regime is directly south of Y-12 and encompasses a section of Chestnut Ridge that is bounded to the west by a surface drainage feature (Dunaway Branch, located immediately west of Industrial Landfill II) and by Scarboro Road to the east. The GWPP maintains an extensive database of geographic and construction details and related information for the monitoring wells in each hydrogeologic regime in the Updated Subsurface Database for Bear Creek Valley, Chestnut Ridge, and Parts of Bethel Valley on the U.S. DOE Oak Ridge Reservation (CNS, 2019). A detailed description of the hydrogeologic framework at Y-12 can be found in the GWPP Management Plan (CNS, 2018).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Albedo Measurement of the Terra Pave White Albedo Product: Cooperative Research and Development Final Report

The Terra Pave team is creating an eco-friendly, cost-effective road coating that controls dust and dirt on high-traffic roads near solar field installations to reduce the dirt cover on panels, which lowers energy output. The Terra Pave White Albedo product also has the capability to increase the amount of solar radiation reflected from the ground to the backside of bifacial PV modules, thereby increasing the amount of electricity generated. This work will quantify the amount of solar radiation reflected from the Terra Pave White albedo product by measuring its albedo, which is the ratio of the reflected radiation to the incoming radiation. Albedo is measured with two horizontal pyranometers with one ground-facing and the other sky-facing.

14 SOLAR ENERGY↗
DOE OSTI2021

Summary of Expansions and Updates in GREET ® 2021

The GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model has been developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE). GREET is a life-cycle analysis (LCA) tool, 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 systems. Within the transportation sector, GREET covers road, air, water, and rail transportation sub-sectors. Recently, GREET was expanded to cover the building sector. Historically, GREET includes LCA of various materials such as steel, aluminum, cement, and different plastic types. Argonne has expanded and updated the model in various sectors in GREET 2021, and this report provides a summary of the release.

09 BIOMASS FUELS↗
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

Expanding the Knock/Emissions/Misfire Limits for the Realization of Ultra-Low Emissions, High Efficiency Heavy Duty Natural Gas Engines

The Vehicle Technologies Office funds early-stage, high-risk research to develop technology upon which industry can implement innovative transportation energy technologies that increase efficiency, decrease cost, and expand use of domestic energy sources, such as natural gas (NG). To improve the viability of medium and heavy-duty vehicles the natural gas engine efficiency must be closer to that of diesel. This work addresses fundamental limitations to achieve diesel-like efficiencies in heavy duty on-road natural gas engines. The primary goal of this research is to achieve diesel-like efficiencies in heavy duty on-road NG engines, focusing on the Cummins 15 liter engine platform (baseline diesel efficiency of 44% at peak torque). That goal has been achieved, though more work is needed to facilitate practical implementation of the developed technologies.

03 NATURAL GAS↗
DOE OSTI2022

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hybrid Hydraulic-Electric Architecture (HHEA) for Mobile Machines (Final Report)

Traditionally, off-road mobile machines such as excavators and wheel loaders are primarily powered by hydraulics, and throttling valves are used to control their work circuits. In recent years, two general trends are towards more energy efficient systems and electrification. With electrification, both efficiency and control performance can be improved by the elimination of throttling losses and the use of high-bandwidth inverter control. Electrification is generally accomplished with Electro-hydraulic actuators (EHA) but they are limited to lower powered systems due to the high cost of electric machines capable of high power or high torque. This project proposes a new system architecture for off-road vehicles - Hybrid Hydraulic Electric Architecture (HHEA) to improve efficiency and control performance without requiring large electric machines. The widely applicable architecture combines hydraulic power and electric power in such a way that the majority of power is provided hydraulically while electric drives are used to modulate this power. In particular, HHEA utilizes multiple common pressure rails to transmit the majority of power and small electric machines to modulate the power. The energy-saving potential of the the HHEA has been validated for the work circuits of a variety of mobile machines, from small 5-ton excavators to medium sized 20-ton excavators and wheel loaders, and representative duty cycles to reduce energy input by 50-80% compared to the commercial state-of-art load-sensing systems. In addition, the corner power requirements of the electrical machines can be downsized by 85% compared to the EHA approach. Various tradeoff studies have also been conducted, including sensitivities to individual components performances, controllers, accumulator sizes, and variations of the system architecture etc. A control strategy has been developed to maintain or exceed the motion control precision compared to current systems. The motion control strategy consists of a nominal controller, based on a passivity-based backstepping design, and a transition controller, based on least-norn feedforward design. The nominal controller is used in between common pressure rail switches whereas the transition controller compensates for any disturbance that common pressure rail switchings inflict on the system. The control strategy has been experimentally validated on both a medium pressure (200bar) hardware-in-the-loop (HIL) testbed and a high pressure (300+bar) HIL testbed. An efficient and power-dense integrated electric-hydraulic machine consisting of an axial flux electric machine and a radial hydrostatic piston hydraulic machine has been designed, constructed and tested. The machine has an active material power density of 6.1kW/kg, a rated speed of 12500 RPM, and a design efficiency of 85%. This is among the highest power density electric machines using conventional materials. While the integrated machine was designed for modulating the hydraulic power within the HHEA, it can also be used in other applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗