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DOE OSTI2023

Bioenergy Feedstock Library 2023 Annual Summary Report

The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNUF) located at INL, is a physical sample repository and a web-accessible electronic database. The BFL stores physical and chemical characteristics of biomass and waste carbon sources for energy use as well as samples generated from across U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) and U.S. Department of Agriculture funded projects. The objective of this Bioenergy Feedstock Library Annual Summary Report for 2023 is to focus on the updates to (1) publicly available analytical data and equipment tracked through the BFNUF, (2) physical samples available for request, (3) sample and data archival progress from recent BETO-funded projects, and (4) publicly available datasets created upon request from BETO, INL projects, or outside entities compared to the 2022 Bioenergy Feedstock Library Annual Summary Report. This report highlights key statistics and available data and information important for INL, BFL users, academics, and industry.

09 BIOMASS FUELS↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗
DOE OSTI2022

Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ~ 450 % compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (Second Quarter 2022)

The U.S. Department of Energy's Alternative Fueling Station Locator contains information on public and private nonresidential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2022 (Q2). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the tenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Third Quarter 2022

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the third calendar quarter of 2022 (Q3 2022) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the eleventh report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (Fourth Quarter 2022)

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the fourth calendar quarter of 2022 (Q4 2022) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the twelfth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Uplifting winds: The surprisingly positive community-wide impact of wind energy installations on property values

A primary concern of stakeholders when considering a new wind project is the potential negative effects wind turbines may have on home values. Yet, what has been surprisingly overlooked in the literature and general discourse around wind energy is that the well-researched positive economic development and fiscal and amenity benefits of wind energy (e.g., increased tax base, tax revenue, better public services and employment gains) might positively affect jurisdiction-wide housing values. With a focus on school districts in the United States, we compare home values in school districts with wind energy installations, before and after a wind energy installation becomes operational, to home values in other school districts located in the same county but without a wind energy installation to provide some of the first causal evidence on the relationship between wind energy projects and district-wide property values. We find that wind projects lead to economically meaningful increases in district-wide housing values of approximately 3 %, when those values are compared to similar homes located in school districts in same-county without wind energy. The effect is strongly correlated with wind project size. The mechanisms, our research suggests, are likely related to relatively large increases in school district per-pupil revenues and expenditures, which are also correlated with wind project size. We suggest other possible mechanisms for the increased values as well.

Attitudes↗
DOE OSTI2023

Grid-Integrated Production of Fischer-Tropsch Synfuels from Nuclear Power

Idaho National Laboratory (INL) investigates the relative economic profitability of an integrated energy system (IES) coupling an NPP with a synfuel production process at selected case study locations across the United States. In the synfuel IES, a high-temperature steam electrolysis (HTSE) plant is thermally and electrically coupled with an NPP to produce zero-carbon hydrogen. The synthetic fuel is produced from this H 2 combined with a CO 2 supply using the reverse water gas shift process followed by the Fischer-Tropsch (FT) reaction. This analysis considers a system in which the CO 2 is sourced from regional CO 2 emitters via the construction and operation of pipeline-based CO 2 supply networks. Locating the FT plant at the same site as the NPP and HTSE plants enables the NPP to provide zero-carbon heat and power to the HTSE plant and zero-carbon power to the FT plant as well as avoid the requirement for long-distance H 2 product transport from the HTSE plant to the FT plant. Hydrogen storage is used to enable the NPP to dispatch power to the electrical grid (instead of the HTSE plant) when grid demand increases, thus enabling the FT plant to continue to operate in a steady-state production mode. The ability to cease hydrogen production for several hours within each day enables the NPP to provide power to the grid to balance the electricity market during peak periods and maximize revenues for the nuclear synfuel IES. The FT process design considered has a 99% carbon conversion efficiency. The use of nuclear energy and nuclear energy-derived hydrogen enables synfuel production to achieve this high level of carbon utilization. Additionally, the life-cycle carbon emissions of the nuclear-based synfuel production process are very low, with WTW emissions of approximately 25 gCO 2 e/MJ, including steam credits (generated from FT process excess heat), and approximately 7 gCO 2 e/MJ, if steam credits are excluded. This compares favorably with the WTW emissions of 90.5 gCO 2 e/MJ for a compression-ignition, direct injection (CIDI) vehicle with a fuel economy of 31.6 miles per gallon gasoline equivalent (MPGGE), using low-sulfur diesel produced using conventional petroleum production and refining processes. Several NPPs in various regions of the U.S. are considered as case study analyses. Supply locations and transportation via pipeline of the CO 2 feedstock to the NPP site are analyzed through the National Energy Technology Laboratory (NETL) CO 2 Transport Cost model. The team finds that the amount of CO 2 generated by different sectors is sufficient for the synfuel production process at all locations considered. The CO 2 transportation costs are functions of the distance of the source to the NPP location, the CO 2 capture cost at the source, and the quantity of CO 2 transported. Historical electricity prices for the NPP case study locations are collected and analyzed. Monthly average prices, price range, and duration of negative-price periods vary among these locations. For each location, an auto-regressive moving average (ARMA) model is trained on historical electricity price data. ARMA validation is done to ensure the synthetic price distributions represent one of historical prices with high fidelity. Synthetic time series from these ARMA models are used in a coupled dispatch and system optimization in the Holistic Energy Resource Optimization Network (HERON) to compute the differential net present value (NPV) of the IES. The team finds that this econometric is positive, ranging from $14M–1.3bn (2020) depending on the location. The optimal synfuel IES configuration to obtain this increase in NPV often maximizes the size of the synfuel production process with regards to the size of the NPP. However, the team shows that the NPP still plays a stabilizing role for the grid: In periods of high prices and high loads, more electricity from the NPP is sent to the grid. A high variability of electricity prices and extreme maximum prices tend to drive up electricity production. While it requires significant investment, the synfuel IES could increase the economic profitability for the existing fleet of LWRs across the country while still maintaining the grid stabilizer role of NPPs. During its lifetime, the main costs for the nuclear synfuel IES are the carbon feedstock transportation costs, followed by the capital expenses (CAPEX) and operation and maintenance (O&M) costs while the revenue comes first from the IRA H 2 production tax credit (PTC) and then from the sales of synfuel products. The profitability of the synfuel IES is most sensitive to the value of the hydrogen PTC and the synfuel products as well as the cost of the carbon feedstock, highlighting the importance of governmental incentives regarding hydrogen, carbon emissions, and synfuel in driving the deployment of future nuclear synfuel IESs.

08 HYDROGEN↗
DOE OSTI2021

FEED Study of Carbon Capture Inc. DAC and CarbonCure Utilization Using United States Steel's Gary Works Waste Plant

Direct Air Capture (DAC) has been proposed as a means of reducing atmospheric concentrations of CO2. While DAC has been evaluated through lab-scale, bench-scale, and small-scale pilot units, large-scale deployment has not been achieved. Feasibility studies are one tool to understand the potential design, operation, performance, and impact of commercial-scale DAC. This paper presents the results of a feasibility study for a passive DAC system deployed at >100,000 tpy scale in three different regions across the U.S. and awarded to Carbon Collect by the U.S. Department of Energy National Energy Technology Laboratory (DOE NETL). The MechanicalTree™technology has been designed and engineered by Carbon Collect based on the concept initially developed at Arizona State University Center for Negative Carbon Emissions. It uses a tower of stacked, sorbentcontaining disks supported by a lifting mechanism, exposed to the air to capture CO2 during the adsorption phase. The disks are then lowered into a regeneration chamber for vacuum and steam regeneration to produce CO2 product during the desorption step. The modular tree design allows large installations with repeatable, mass-manufactured units connected to common utilities such as steam supply, vacuum, and CO2 processing for compression and geologic storage. The passive DAC system eliminates the equipment and energy of forced air fans by using natural air circulation to contact the sorbent with the CO2 in the air. Because of this, the performance is dependent on the wind speed in addition to the temperature and relative humidity. Performance and flow rate fluctuations were incorporated into equipment and facility design with considerations for turndown to 10% of maximum rated flow to allow operation in all seasons. The feasibility study was undertaken to evaluate the technology in different regions and climates. Three locations were selected for this study representing different climates: Alabama (hot and humid), California (hot and arid), and Wyoming (continental). For each region, the adsorption/desorption cycle was optimized including tuning the heat integration and cycle timings. The performance of individual trees was then scaled to the full facility with the same design of more than 20,000 trees common between all regions. The installations had expected average CO2 capture rates of between 330,000 and 485,000 tonnes of CO2 per year depending on the climate. The initial engineering design for the facility at each location was performed with cost and performance estimates for the trees, carbon purification and compression, and the balance of plant. To supply thermal and electrical energy for the facility, carbon-free or low-carbon power must be considered. Options for low-carbon, continuous thermal and electrical energy were considered. The best-performing option from those considered was identified to be an electrically-heated molten salt energy storage system, powered by an on-site photovoltaic field. During molten salt thermal discharge, steam is produced from heat exchange with the molten salt and used to generate power in a steam turbine as well as steam for regenerating the DAC carbon trees. The thermal and electrical supply and analysis is presented in the context of low-carbon power for carbon removal. Modelling and economics of transportation and geologic storage of the CO2 is considered and presented for each location. The results of the feasibility study are incorporated into the presented techno-economic and life-cycle assessments. This work is intended to provide an understanding of the performance, cost, and impact of capturing CO2 at the commercial scale and the impact of climate and regional siting on the considered passive DAC system.

42 ENGINEERING↗
DOE OSTI2020

Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid

Energy Efficiency has historically focused on delivering savings to offset growth in energy supply. Today’s growing emphasis on decarbonization of the energy supply is driving renewables adoption and increased interest in electrification. As a result, energy efficiency is being assessed not just in its ability to offset load growth, but also for its ability to alleviate location-specific constraints on transmission and distribution infrastructure. This work demonstrates that advanced measurement and verification modeling techniques can be used to estimate the spatio-temporal grid impact of a portfolio of energy efficiency programs. It extends measurement-based methods to an entire Demand Side Management portfolio and uses a single model to predict annual as well as seasonal building energy use with near-zero bias. In addition, new metrics are introduced to assess grid level impacts of energy efficiency. The results show that the efficiency program portfolio delivers savings of over 12% at the territory-wide proxy level, with substation and feeder level savings ranging from 0.4% to 26%, and ~5%-42% respectively. Overall, these savings impacted 1.0%–1.4% of the energy used at these locations in the grid. This work provides a methodology with potential to connect efficiency with distribution planning, carrying implications for non-wires alternatives and targeted delivery of efficiency programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs)

The Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs) are a set of prototype computational tools for simulating and optimizing midstream natural gas pipeline system operations subject to location and time-dependent hydrogen blending. The models can accurately resolve dynamic gas flows through large-scale pipeline networks using non-ideal gas equations of state. The codes can be used as decision support for planning and design decisions involving intra-day energy flow schedules as well as spatiotemporal economic values of natural gas, hydrogen, and net energy delivered to consumers while ensuring that pipeline hydraulic limitations, gas compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. The inputs to the codes are a model of the pipeline system as well as time-series data that specify boundary conditions on the network. For optimization, the code module requires price and quantity offers for natural gas and hydrogen and price and quantity bids for energy, which are used as time-dependent constraints in an optimal control problem. The outputs are time-series data that provide a predictive simulation of gas flows, mass fractions, and pressures, or with additional degrees of freedom give an approximately optimal solution for gas injections/withdrawals, compressor settings, and sensitivities to the objective function that provide locational values of energy.

Zlotnik, Anatoly↗
DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Second Quarter 2023

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2023 by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with a 2030 infrastructure requirement scenario. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the fourteenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: First Quarter 2024

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE's) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2024 (Q1 2024) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared to the infrastructure requirement scenario outlined in the National Renewable Energy Laboratory (NREL) report, "The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure" (https://www.nrel.gov/docs/fy23osti/85654.pdf). This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the 17th report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and NREL publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Dissemination of blaNDM-5 and mcr-8.1 in carbapenem-resistant Klebsiella pneumoniae and Klebsiella quasipneumoniae in an animal breeding area in Eastern China

Animal farms have become one of the most important reservoirs of carbapenem-resistant Klebsiella spp. (CRK) owing to the wide usage of veterinary antibiotics. “One Health”-studies observing animals, the environment, and humans are necessary to understand the dissemination of CRK in animal breeding areas. Based on the concept of “One-Health,” 263 samples of animal feces, wastewater, well water, and human feces from 60 livestock and poultry farms in Shandong province, China were screened for CRK. Five carbapenem-resistant Klebsiella pneumoniae (CRKP) and three carbapenem-resistant Klebsiella quasipneumoniae (CRKQ) strains were isolated from animal feces, human feces, and well water. The eight strains were characterized by antimicrobial susceptibility testing, plasmid conjugation assays, whole-genome sequencing, and bioinformatics analysis. All strains carried the carbapenemase-encoding gene bla NDM-5 , which was flanked by the same core genetic structure (IS 5 - bla NDM-5 - ble MBL - trpF - dsbD -IS 26 -IS Kox3 ) and was located on highly related conjugative IncX3 plasmids. The colistin resistance gene mcr-8.1 was carried by three CRKP and located on self-transmissible IncFII(K)/IncFIA(HI1) and IncFII(pKP91)/IncFIA(HI1) plasmids. The genetic context of mcr-8.1 consisted of IS 903 - orf - mcr-8.1-copR-baeS-dgkA - orf -IS 903 in three strains. Single nucleotide polymorphism (SNP) analysis confirmed the clonal spread of CRKP carrying- bla NDM-5 and mcr-8.1 between two human workers in the same chicken farm. Additionally, the SNP analysis showed clonal expansion of CRKP and CRKQ strains from well water in different farms, and the clonal CRKP was clonally related to isolates from animal farms and a wastewater treatment plant collected in other studies in the same province. These findings suggest that CRKP and CRKQ are capable of disseminating via horizontal gene transfer and clonal expansion and may pose a significant threat to public health unless preventative measures are taken.

Yang, Chengxia↗
DOE OSTI2022

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: First Q2022

The U.S. Department of Energy's Alternative Fueling Station Locator contains information on public and private nonresidential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2022 (Q1). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the ninth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗