Estimating environmental impacts of large-scale transportation networks with vehicle registration data
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Environmental Sciences & Ecology↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A data integration framework of additive manufacturing based on FAIR principles
Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract
36 MATERIALS SCIENCE↗
Understanding and Improving Energy Efficiency of Regional Mobility Systems Leveraging System-Level Data
Increased congestion required urban Americans to travel 6.8 billion hours more and purchase 3.1 billion gallons of fuel for a congestion cost of $\$$153 billion, according to the 2019 Urban Mobility Report. How to effectively manage the regional mobility system and improve the energy efficiency presents a big challenge to public agencies. Recent years have witnessed massive multi-jurisdictional multi-modal system-level data from various sources, which provides an unprecedented opportunity to improve the mobility system and its energy efficiency. However, implications of system-level data for mobility and energy efficiency are unclear. Those system-level data sets are siloed, spatially and temporally sparse, biased, not unified, and lacking of insights for system management. Consequently, there is a real need to acquire, fuse, mine and learn from multi-source system-level data to prepare public agencies to deal more effectively with large-scale energy efficiency modeling, management and planning. This project proposes to intensively review inexpensive, replicable and openly-accessible data from multi-modal systems, develop a data-driven system-level modeling framework enabled and validated by data, identify the energy inefficiencies of mobility systems from infrastructure, vehicles, passenger systems, and quantify the benefits of system-level strategies to improve mobility/energy efficiency. In addition, this research develops models to effectively estimate energy consumption and emissions from various types of vehicles on the roadway networks, with high granularity and high fidelity. Traditional models often heavily rely on aggregated infrastructure or vehicle/passenger data, for example, the census survey, land-use, and traffic counts of one or several classes, which may lead to research gaps considering the emerging vehicle technologies. Those models do not contain individual vehicular information. We propose an integrated data-driven method that combines multiple network modeling components, featuring the utilization of state-wide vehicle registration data. The additional vehicle registration data improve the model performance, and produce high-resolution vehicle-specific estimates of emissions and network performance metrics. Two case studies on the Pittsburgh and Philadelphia regional network show that the proposed method can efficiently and effectively estimate the emissions of a large-scale network, and provide valuable information for evaluating common management strategies.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Domestic Sales Mix of Plug-In Electric Vehicles by Trim Variant and Vehicle Characteristics
As plug-in electric vehicles (PEVs) take up a larger share of the United States vehicle market, their variety increases as well. Since many electric vehicle manufacturers offer multiple PEV trim variants, the distribution of vehicle characteristics such as battery capacity, all-electric range, electricity consumption, and curb weight cannot be determined by sales data alone. Sales data and vehicle registration data were analyzed to quantify and map the trim variant distribution of PEV models from nine automakers. With this information, we quantify national sales-weighted characteristics of fourteen different models over ten years, including battery capacity, fuel economy, and vehicle weight. We find a positive correlation between share of all-wheel drive variants for Tesla vehicles and average annual snowfall, and general uniformity nationwide in other vehicle characteristics. The estimated trim variant distributions will be useful in informing decisions regarding materials recycling and EV impact on the energy grid, and this study found that the total installed battery capacity in PEV in the U.S. was 76 GWh through 2020.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
UMLS users and uses: a current overview
Abstract The US National Library of Medicine regularly collects summary data on direct use of Unified Medical Language System (UMLS) resources. The summary data sources include UMLS user registration data, required annual reports submitted by registered users, and statistics on downloads and application programming interface calls. In 2019, the National Library of Medicine analyzed the summary data on 2018 UMLS use. The library also conducted a scoping review of the literature to provide additional intelligence about the research uses of UMLS as input to a planned 2020 review of UMLS production methods and priorities. 5043 direct users of UMLS data and tools downloaded 4402 copies of the UMLS resources and issued 66 130 951 UMLS application programming interface requests in 2018. The annual reports and the scoping review results agree that the primary UMLS uses are to process and interpret text and facilitate mapping or linking between terminologies. These uses align with the original stated purpose of the UMLS.
Amos, Liz↗
Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo
Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty; which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature. In this paper, a flexible Bayesian approach to functional alignment is presented, which appropriately accounts for noise in the data without any pre-smoothing required. Additionally, by running parallel MCMC chains, the method can account for multiple optimal alignments via the multi-modal posterior distribution of the warping functions. To most efficiently sample the warping functions, the approach relies on a modification of the standard Hamiltonian Monte Carlo to be well-defined on the infinite-dimensional Hilbert space. In this work, this flexible Bayesian alignment method is applied to both simulated data and real data sets to show its efficiency in handling noisy functions and successfully accounting for multiple optimal alignments in the posterior; characterizing the uncertainty surrounding the warping functions.
97 MATHEMATICS AND COMPUTING↗
The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community
Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. Finally, the needed transformation of the LTG data culture requires emphasis in student education on science and management of data.
58 GEOSCIENCES↗
Individual Motorist Data - Ohio EV Ownership Trends
The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A Stochastic Framework for Estimating Load Profiles at EV Fast Charging Stations
This paper formulates a methodology for estimating the average daily load profiles of EV fast charging stations over a planning horizon of five to ten years. The developed methodology uses historic vehicle registration data, state-level EV adoption targets, seasonal driving patterns, local demographics, competition, and traffic volume information to predict average station usage. Through Monte Carlo simulations, an average daily load profile is obtained for each month in the planning horizon, and prediction uncertainty is quantified. The proposed framework will facilitate the accurate estimation of energy and demand costs incurred by the charging station over the planning period, thereby informing return-on-investment calculations.
Biswas, Shuchismita↗
Automated Registration of Vector Data to Overhead Imagery
The availability of open source, remote sensing-derived vector data has increased exponentially in recent years. Unfortunately, these vector data are rarely made available with the corresponding source images from which objects were extracted. As such, these derived datasets are commonly used in combination with target images which differ from the source. Satellite viewing geometry can cause objects, extracted from a source image, to appear shifted when overlaid with a target image. Whether for purely cartographic purposes, reusability of preexisting training labels, or any spatial analysis where spatial correspondence between vector and image are required, the following paragraphs outline a method to address this challenge through the automated registration of vector data to overhead imagery, including existing literature regarding this challenge, followed by a case study of Sioux Falls, South Dakota.
McKee, Jacob↗
Review of In Situ Sensing for Directed Energy Deposition for Industrial Part Quality Assessment
As the use additive manufacturing (AM) processes continues to grow in critical industries, improved quality assurance methods are becoming increasingly sought after for qualification and certification of AM components. Traditional nondestructive evaluation of printed components is often unable to supply the required confidence in print quality to justify qualification and certification, but the layer-by-layer nature of AM provides unprecedented opportunities for in situ quality inspection. This document summarizes recent developments in process monitoring research specifically related to Directed Energy Deposition (DED). Particular attention is given to three aspects of the highlighted manuscripts: (1) the type of sensors used, (2) features extracted from each sensor modality, and (3) analysis of extracted features for AM quality assessment. Based on the review of the state-of-the-art, several observations have been made. First, none of the reviewed works have applied their trained models to real part geometries, with many of the works relying on single track experiments, thin-walled structures, and cubes. Similarly, there have not been any works demonstrating model generalizability, i.e., a model trained on data from one build allows for fruitful analysis of data from another build. Many works used machine learning techniques to distinguish different process regimes (i.e., normal, keyholing, lack-of-fusion), but very few papers have investigated stochastic variation in an already “optimized” process. Sensor fusion approaches are also limited in the DED sensing literature, but the few works that have employed such techniques have demonstrated the benefits. Finally, registration of in situ data to the build coordinate system is of paramount importance to producing industrially relevant in situ monitoring systems. Data registration allows direct correlations between process anomalies detected in the process monitoring data to localized departures in part quality, but such techniques are generally lacking in the current literature.
36 MATERIALS SCIENCE↗
Grid Optimization Competition Challenge 3 Problem Formulation
This report contains the problem formulation for the Grid Optimization (GO) Competition Challenge 3. The Grid Optimization Competition is run by a team of researchers from a number of organizations, including the sponsor Advanced Research Projects Agency - Energy (ARPA-E), lead organization Pacific Northwest National Laboratory (PNNL), and technical contributors from Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), Georgia Institute of Technology (GT), University of Wisconsin (UW), and others. The GO Competition poses challenge problems in the field of power grid management, invites entrants to develop solvers for these problems, invokes the solvers on a set of problem instances using common hardware, ranks the solvers according to their performance, and awards prizes according to the rankings. The overall goal of the GO Competition is to spur innovative research on high impact and computationally challenging problems in power grid management from initial development through commercial deployment. Complete information about the GO Competition can be found online at [2]. The webpage covers previous Challenges, rules, timeline, registration information, data formats, scoring methods, computational platform information, information on supported solvers and languages, sponsor information, frequently asked questions, administrator contact information, publicly available problem instances, computer code for reading and evaluating problem and solution data, a sandbox for testing solvers, and a solver submission interface, results, and publications.
24 POWER TRANSMISSION AND DISTRIBUTION↗
The State of Electric Vehicle Adoption in Colorado for Multifamily versus Single-Family Dwellings: A Methodology for Quantifying Deviation from Parity
Given that electric vehicle adoption is well underway, the spatial distribution of electric vehicle owners by housing type—single-family or multifamily— shows whether parity (equal adoption rates) is being achieved or to what extent adoption by housing type is over or undersaturated (i.e., over- or under-adoption). We use a proprietary dataset of vehicle registrations with modeled housing type to analyze saturation ratios in Colorado in 2022. We found significant single-family oversaturation and multifamily undersaturation in 14% and 23% of ZIP codes, respectively, suggesting Colorado can still mitigate disparities in electric vehicle adoption by housing type through accessible vehicles and charging.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Moderate spectral resolution solar irradiance measurements, aerosol optical depth, and solar transmission, from 360 to 1070 nm, using the refurbished rotating shadow band spectroradiometer (RSS)
Abstract. This paper reports on a third-generation rotating shadow band spectroradiometer (RSS) used to measure global and diffuse horizontal plus direct normal irradiances and transmissions at 1002 wavelengths between 360 and 1070 nm. The prism-dispersed spectral data are from the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma (36.605∘ N, 97.486∘ W) and cover dates between August 2009 and February 2014. The refurbished RSS isolates the detector in a vacuum chamber with pressures near 10−7 torr. This prevents the deposition of outgassed vapors from the interior of the spectrometer shell on the cooled detector that affected the operation of the first commercial RSS. Methods for (1) ensuring the correct wavelength registration of the data and (2) deriving extraterrestrial responses over the entire spectrum, including throughout strong water vapor and oxygen bands, are described. The resulting data produced are archived as ARM data records and include cloud-screened aerosol optical depths, spectral irradiances and direct normal solar transmission, as well as normalized diffuse and global irradiances.
54 ENVIRONMENTAL SCIENCES↗
Third-Party Aggregation Rulemaking in MISO and SPP Footprints
The report highlights key considerations for retail regulators in the footprints of the Southwest Power Pool (SPP) or Midcontinent Independent System Operator (MISO) in states that previously opted out of allowing third-party aggregation participation under FERC Order 719 and that may want to explore reversing the decision due to a changing policy environment under new FERC orders (i.e., 2222) and/or tightening resource adequacy constraints. Through a document review and a series of 27 interviews with regulators, aggregators, and other industry professionals, this document provides a high-level policy overview of the retail regulator’s role in a selection of processes, rules, and regulations to better understand how states have treated and/or integrated aggregators into wholesale markets. The report summarizes findings in two sections: General Findings and Specific Policy Findings. The General Findings offer high level takeaways such as the fact that 17 of the 20 states in MISO and SPP footprints opted out of third party aggregations following Order 719. Additionally, there is only one state that is fully restructured in the MISO and SPP footprints whereas the rest are vertically integrated, and the majority of third-party aggregations in the U.S. take place in restructured states outside of these footprints. Even so, third-party aggregations do take place in MISO and SPP albeit in small numbers and in an ad hoc regulatory environment. The specific policy findings delve deeper into specific examples of state actions on the topics of jurisdiction, registration and licensing, data governance, dual participation, and dispute resolution. Tables in each category organize state actions into Tiers I-III, which roughly correspond to the possible level of involvement or possible change necessary by state regulators and/or legislators to implement these actions. The tier level does not indicate any value judgement, as each state has respective regulatory limitations and each decision comes with various tradeoffs. One main tradeoff is between simplicity and quick implementation versus comprehensive and prolonged implementation. In many cases, actions in Tier I could be implemented without significant changes by relying on the use of existing processes for an aggregator context. On the other hand, many actions in Tier III are more narrowly designed to address aggregators specifically, but often require more significant changes including the involvement of additional parties through stakeholder engagement or legislative action. In some cases, these tiers are discrete. However, state regulators may also choose to progress through these various tiers sequentially as they phase in aggregators while learning from their experience. With the ability to stack bulk system level services, distributed energy resource aggregations in MISO and SPP could provide various private benefits (e.g., increased value streams to the owner) as well as societally beneficial grid services (e.g., peaking capacity, ancillary services, and other services that increase the grid’s overall operational efficiency). In deciding how to best to enable these benefits, states and retail regulators must weigh various tradeoffs if considering reversing a previous opt out.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF
In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Finally, our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects.
36 MATERIALS SCIENCE↗
DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution
Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1°×0.1°), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of -3.96 and -0.71 W m -2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m -2 , respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales.
54 ENVIRONMENTAL SCIENCES↗
A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process
Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data-centric approach emphasizes leveraging sensor data available throughout the production process to optimize performance. Integration of extensive data analysis provides opportunities for improving precision, reducing waste, and enhancing the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes a comprehensive description of the deposition process, process parameters, welding characteristics and acoustic data collected in-situ, and X-Ray Computed Tomography data of the build.
42 ENGINEERING↗