DOE-ICoM/Torrent.jl
Computationally efficient flood simulation using Lagrangian rivulets.
Daniel, Brent [Pacific Northwest National Laborato↗
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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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DOE-ICoM/RIFT
Rapid Infrastructure Flood Tool (RIFT) is a two-dimensional hydrodynamic model based on the complete shallow water equations. RIFT has specifically been designed with rapid simulation in mind by utilizing commodity high performance computing technology and best-available nation-wide data. RIFT is used to predict the movement of water over land and resolve the spatial and temporal variability of flood depths, extent, and velocity. RIFT can be applied to many flood situations and has primarily been used to quantify flood extents from dam/levee failure or inland rainfall flooding.
Perkins, Bill [Pacific Northwest National Laborato↗
DOE EV Data Collection - Vehicle Data
Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE EV Data Collection - Maintenance Data
Maintenance data includes information on maintenance performed on the electric vehicles, including preventive maintenance, service calls, and availability of the vehicles. The parameters collected, and their definitions, will vary due to the differences in maintenance tracking systems that exist between fleets. Parameter definitions are detailed in the data dictionary, and specific vehicle information is available in the vehicle attributes table. Vehicle ID can be used as a key between maintenance data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE EV Data Collection - Charging Data
Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The **Charge Data Daily/Session Dictionaries** contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The **Charger Attributes** table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The **Charger Attributes Data Dictionary** contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The **Charging Data** tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE EV Data Collection - Facility Data
Facility data includes information on electricity consumption by larger-scale infrastructure, including buildings, solar arrays, and energy storage systems. Parameter definitions can be found in the data dictionary. If a connection between specific vehicle information and facility data exists, it will be available in the vehicle attributes table. Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Detailed Simulation Datasets Quantifying U.S. DOE VTO/HFTO R&D Benefits Across Light- to Heavy-Duty Vehicles
For more than 20 years, Argonne National Laboratory’s Vehicle & Mobility Systems Department has assessed how R&D investments by the U.S. Department of Energy’s Transportation Technologies Office and Alternative Fuels and Feedstocks Office affect vehicle energy use and cost. The analyses are performed using Autonomie, Argonne’s full-vehicle simulation tool for energy consumption, performance, and cost. The study covers five time frames ranging from present day through 2050, with more than 30 vehicle classes and applications (10 light duty and >20 medium and heavy duty), as well as six powertrain configurations (conventional, start-stop, hybrid electric vehicle, plug-in hybrid electric vehicle, battery-electric vehicle, and fuel cell electric vehicle) and five fuels (gasoline, diesel, natural gas, hydrogen, and electricity). Low and high technology uncertainty scenarios have been considered to capture a realistic range of outcomes. The resulting datasets include the assumptions used (i.e., efficiency, $/kWh), vehicle-level data (power, energy, weight, and cost), and outputs such as energy consumption, manufacturer’s suggested retail price, and total cost of ownership. These data are critical to stakeholders working in transportation, technology assessment, and long-term R&D planning.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Illinois State Geological Survey (ISGS), Illinois Basin - Decatur Project (IBDP) Geological Models, July 7, 2021. Midwest Geological Sequestration Consortium (MGSC) Phase III Data Sets. DOE Cooperative Agreement No. DE-FC26-05NT42588.
Three geological models in Petrel (Mark of Schlumberger) and data output from shallow groundwater modeling using TOUGH Codes from Lawrence Berkeley National Laboratory, included under folders: Static_Geologic_Model, Dynamic_Reservoir_Model, Geomechanical_Model, and Groundwater_Model.
3D Geological Model↗
Illinois State Geological Survey (ISGS), Illinois Basin - Decatur Project (IBDP) Seismic Data, July 7, 2021. Midwest Geological Sequestration Consortium (MGSC) Phase III Data Sets. DOE Cooperative Agreement No. DE-FC26-05NT42588.
Seismic data from the IBDP, included primarily under folders: Active_Seismic_Data and Passive_Seismic_Events_Monitoring. The data included under IBDP_Located_Microseismic_Event_Data are a subset of microseismic (Passive Seismic Events Monitoring) data acquired throughout the project from pre-injection to shut-in. These data represent the “located” microseismic events and the two datasets, located in folders Downhole_Geophone_Data and Surface_Seismometer_Data, are correlated.
3D Seismic,Carbon Sequestration,Decatur,Illinois B↗
Quillinan, et al 2018 DOE Geothermal Technology Office REE Report for NEWTS Database and Case Studies
Produced water data processed into the NEWTS data format for easy input into aqueous chemistry modeling software, including oil & gas and coal bed methane produced waters, and geothermal waters. Includes information on rare earth elements (REEs) and critical minerals (CMs). Case studies are included to demonstrate solved streams using aqueous chemistry software. An input template is provided for modeling stream data in OLI Studio. Original data from: Quillinan, Scott, Nye, Charles, Engle, Mark, Bartos, Timothy T., Neupane, Ghanashyam, Brant, Jonathan, Bagdonas, Davin, McLing, Travis, McLaughlin, J. Fred, Phillips, Erin, Hallberg, Laura L., Shahabadi, Mahdi, and Johnson, Matthew. Assessing rare earth element concentrations in geothermal and oil and gas produced waters: A potential domestic source of strategic mineral commodities (Final Report). United States: N. p., 2018. Web. doi:10.2172/1509037.
Aqueous Chemistry↗
DOE Energy Frontier Research Centers Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio)
New capabilities to predict, design and control the chemistries of carbon could answer a global imperative to transition from fossil-based to sustainable transportation fuels. While the use of inexpensive hydrocarbons has been an unparalleled achievement and enabler of economic prosperity for many nations, singular dependence upon crude oil has given rise to systemic vulnerabilities in climate, energy, economic, and national security. Lignocellulosic biomass, a renewable and carbon-neutral resource, has the potential to displace an estimated annual equivalent of three billion barrels of oil in the U.S. alone (National Research Council 2009, U. S. Department of Energy, 2011). However, biomass has only one-third the energy density of crude oil (Agrawal and Singh 2009, Richard 2010) and lacks petroleum’s versatility as a feedstock for fuels and chemicals. These limitations keep biomass conversion below the efficiency level needed for strategic impact while the scientific challenge of routing carbon from one molecular context to another remains unmet. In 2009, the Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio) recognized the potential of chemical catalysis and fast pyrolysis to overcome such limitations by transforming the main components of biomass (cellulose, xylan, and lignin) from grasses and trees directly to liquid hydrocarbons and aromatic co-products. Enabled by the EFRC high-risk, high-reward approach to grand challenge science, C3Bio researchers have been key national players in disrupting the conventional paradigm of the cellulosic biorefinery into a new future of “no carbon left behind”—the full utilization of carbon from plant cell walls in energy-dense fuels (Fig. 1). We identified catalytic and fast-pyrolytic pathways that utilize cellulose, xylan and, most significantly, lignin. We developed catalytic processes that deoxygenate and transform monomers and isolated polymers into useful products and tested their use with intact biomass. We gained control of lignin synthesis within plants and initiated tailoring biomass to its end-use through the tools of plant molecular biology and genetic engineering. C3Bio breakthroughs have increased the energy density of biomass-derived substrates via catalytic and pyrolytic conversions into products such as benzoquinones, furfural and hydroxymethylfurfural, levoglucosan and levulinic acid, methoxypropylphenols and propylbenzene. Such advances in biomass conversion would not have been possible without simultaneous advances in analytical instrumentation and methodologies, and imaging technologies and applications. The legacy science developed by C3Bio enables design and control strategies for achieving a targeted product portfolio of fuel and chemical feedstocks from a diverse range of native and tailored biomass. Our research provides the knowledge base required for a bio-economy with product streams as diverse in functionality as those of the petrochemical industry. Coupling targeted computational modeling with experimentation, we achieved: (1) fundamental understanding of biopolymers and cell wall architecture assembly, (2) discovery of new chemistries that allow the development of highly selective pathways to fuels and desirable chemicals, and (3) an integrated systems-level understanding to control catalytic and pyrolytic pathways.
09 BIOMASS FUELS↗
Using Radioiodine Speciation to Address Environmental Remediation and Waste Stream Sequestration Problems at the Fukushima Daiichi Nuclear Power Plant and a DOE Site
Iodine-129 ( 129 I), with a half-life of half-life of 16 million years, is commonly considered the single greatest risk driver in high-level and low-level nuclear repositories. This risk stems from several basic properties of 129 I, and under many geochemical conditions, it can move as an anion at nearly the rate of water through the subsurface environment. 129 I is also extremely radiologically toxic because over 90% of body burden accumulates in the thyroid, which weighs only about 14g in an adult. There is also a large worldwide inventory of radioiodine as a result of its high fission yield and this inventory is rapidly increasing as a result of nuclear energy production. Radioiodine is produced at a rate of 40 GBq (1 Ci) per gigawatt of electricity produced by nuclear power. To illustrate how the properties of 129 I magnify its risk, 129 I accounts for only 0.00002% of the radiation released from the Savannah River Site in Aiken, South Carolina, but contributes 13% of the population dose, a six orders of magnitude magnification of risk with respect to its radioactivity. The currently favored solid phase for LLW immobilization is cement, while HLW immobilization is the incorporation of waste into glass (vitrification). However, so far, the incorporation into cement and subsequent leaching of only iodide has been seriously investigated. The major problem with this approach is that it ignores the complex speciation of iodine, i.e., it ignores iodate and organo-iodine which have different chemistries. Most of the past research was devoted to the mechanism of iodide uptake in cement hydrate phases that is sorption and/or incorporation. Very few data exist on iodate and organo-iodine incorporation in cement, even though large quantities of liquid waste containing also radioiodine have already been solidified in cement Iodine-129 from low-level waste is commonly disposed of in cementitious materials. Grout, a dense cementitious fluid, mixed with a reducing slag, is often used to immobilize radionuclides. However, the reducing environment might not be conducive to immobilize iodine. For example, the silver based immobilization technologies (e.g., AgCl, Ag-impregnated granular activated carbon, Ag-mordenite) remove iodine from the aqueous phase by promoting the formation of Ag-iodide precipitates. The solubility of AgI is eight orders of magnitude lower than it is for AgIO 3 . Similarly, coprecipitation of iodine into calcium carbonate phases occurs only with IO 3 - and not with I - and org-I. If one would want to immobilize iodine more effectively, different engineering approaches would need to be used to promote binding of I - , IO 3 - , or organo-I. Using laboratory experiments with grout, slag, and silver-based adsorbents, and GC-MS and I K-edge XANES and EXAFS and C K-edge XANES spectroscopy for identifying iodine speciation, the major problems with these methods have been identified as focused too much on just one of the iodine species for immobilization, while others, especially organo-I, remained mobile. Finally, we established that most of the adsorbents that are used contain sufficient amounts of organic matter to create organo-I . It is anticipated that increased attention directed at understanding and quantifying the speciation of radioiodine, as opposed to simply total radioiodine, will lead to improved remediation results to be used for long-term radioiodine disposal in cementitious waste forms.
129-Iodine↗
Roll-to-Roll Advanced Materials Manufacturing DOE Laboratory Collaboration (FY2019 Final Report)
R2R processing is used to manufacture a wide range of products for various applications which span many industrial business sectors. The overall R2R methodology has been in use for decades and this continuous technique traditionally involves deposition of material(s) onto moving webs, carriers or other continuous belt-fed or conveyor-based processes that enable successive steps to build a final version which serves to support the deposited materials. Established methods that typify R2R include tape casting, silk-screen printing, reel-to-reel vacuum deposition/coating and R2R lithography. Products supported by R2R manufacturing include micro-electronics, electro-chromic window films, PVs, fuel cells for energy conversion, battery electrodes for energy storage, and barrier and membrane materials. Due to innovation in materials and process equipment, high-quality yet very low-cost multilayer technologies have the potential to be manufactured on a very cost-competitive basis. To move energy-related products from high-cost niche applications to the commercial sector, a means must be available to enable manufacture of these products in a cost-competitive manner that is affordable. Fortunately, products such as fuel cells, thin- and mid-film PVs, batteries, electrochromic and piezoelectric films, water separation membranes, and other energy saving technologies readily lend themselves to manufacture using R2R approaches. However, more early-stage research is needed to solve the challenge of linking the materials (particles, polymers, solvents, additives) used in ink and slurry formulations, though the ink processing, coating and drying processes, to the ultimate performance of the final R2R product, especially for a process that uses multiple layers of deposition to achieve the end product.
42 ENGINEERING↗
FY 2019 DOE-EERE Materials Vehicle Technologies Office Consolidated Report
The Materials Technology Program supports the Vehicle Technologies Office’s mission to help consumers and businesses reduce their transportation energy costs while meeting or exceeding vehicle performance expectations. The propulsion materials research portfolio seeks to develop higher performance materials that can withstand increasingly extreme environments and address the future properties needed for a variety of high-efficiency powertrain types, sizes, fueling concepts, and combustion modes. The lightweight materials research portfolio enables improvements in fuel economy by providing properties that are equal to or better than traditional materials at a lower weight. Because it takes less energy to accelerate a lighter object, replacing cast-iron and traditional steel components with lightweight materials such as advanced high-strength steels, magnesium alloys, aluminum alloys, and fiber-reinforced polymer composites can directly reduce a vehicle’s fuel consumption. By 2025, Materials Technology research activities seek to enable a 25% weight reduction of the glider for light-duty vehicles including body, chassis, and interior as compared to a 2015 baseline at no more than a $5/lb-saved increase in cost.
99 GENERAL AND MISCELLANEOUS↗
General Analysis of Data Collected from DOE Lidar Buoy Deployments Off Virginia and New Jersey
Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of offshore wind energy in the U.S. In general, each buoy is deployed for a year or more at a given location in order to capture at least a full annual cycle of weather conditions. The initial deployment for one buoy was off the coast of Virginia beginning in 2015, and the other buoy was first deployed off the coast of New Jersey beginning in 2016. Over the last two years, PNNL has had an opportunity to analyze the data collected during these first two deployments. This report describes a substantial analysis of data collected by the two lidar buoys operated off the coasts of Virginia and New Jersey. The centerpiece instrument for each buoy as deployed off Virginia and New Jersey is a lidar system, which is designed to measure the horizontal wind vector from approximately 40 m to 200 m above the sea surface with vertical resolution of 40 m. Since the initial deployments, the original lidars have been replaced with more powerful Leosphere 866 v2 systems. The analyses in this report will apply to the original Vindicator systems. In addition to the wind profiles from the lidars, the buoys collect near-surface measurements of wind speed and direction, air temperature, relative humidity, barometric pressure, and solar irradiance. Oceanographic variables measured include the two-dimensional wave spectrum, water temperature and conductivity, and ocean current vectors to a depth of 90 m. An assessment of overall data recovery and a basic analysis of the data collected was provided in a previous report This report substantially extends that analysis. The various sections describe the development and application of an inertial measurement unit (IMU) data recovery scheme for the New Jersey deployment; a climatological analysis of winds at hub height and at the surface together with thermodynamic variables measured at the surface for the full deployment periods; an analysis of oceanographic observations describe sea state; the development of a refinement for NOAA’s WaveWatch III model to allow its application to near-shore areas; a basic climatology of ocean currents observed from the buoys; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; and the development and evaluation of techniques to extract turbulence intensity and turbulence kinetic energy from the lidars. The analyses contained in this report provide a great deal of new information about offshore conditions on the U.S. East Coast. In addition, the experience gained will inform both configurations and analysis of data from future deployments of these lidar buoy systems.
17 WIND ENERGY↗
Boost the biopharmaceutical industry’s research efficiency with hyperscale data distribution; DOE SBIR Phase I Final Technical Report
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Enhanced Visualization Capabilities in OpenMC: Report of Activities under a DOE GAIN Voucher
This work was performed under a Cooperative Research and Development Agreement (CRADA) through the U.S. Department of Energy’s Gateway for Accelerated Innovations in Nuclear (GAIN) program in partnership with Oklo Inc., the industry participant. The broad goal of this project was to improve the visualization capabilities of OpenMC related to model development and simulation results. OpenMC is a Monte Carlo particle transport code that is developed and maintained by a broad community of contributors, of which Argonne National Laboratory plays a leading role.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Industrial Energy Management During a Pandemic: Lessons Learned from DOE Better Plants Program Partners
The COVID-19 pandemic is causing many challenges for manufacturers, especially those that depend on workers whose jobs cannot be performed remotely. This has detrimentally impacted the energy management workforce, energy support system performance, as well as operations and supply chains.This article discusses four primary challenges for industrial energy management caused by the pandemic, strained budget for energy projects, increased energy intensity caused by low production rates and other safety practices, limited access to energy systems, and the lost knowledge and experience of senior staff. This article also proposes some potential solutions for these challenges that include implementing no- and low-cost energy conservation measures, adopting alternative financing options, improving shutdown procedures, reducing baseloads, creating an effective online energy-monitoring system, performing virtual energy assessments, building a robust EnMS, and attending online and in-person training events.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗