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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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The C-130 aircraft observations were successfully collected during May and July 1998. The aircraft data has been processed and is already available to the community. Integrated data sets for modelling applications have been prepared and modelling studies have been completed. Included is a list of papers that have been submitted since the start of the project.
The U.S. Department of Energy Occupational Radiation Exposure Report for Calendar 2023 presents the results of analyses of occupational radiation exposures at the U.S. Department of Energy (DOE), including the National Nuclear Security Administration (NNSA) operations, during calendar year 2023. This report includes occupational radiation exposure data for over 80,000 DOE Federal employees, contractors, and subcontractors as well as members of the public who have worked in or entered controlled areas monitored for exposure to radiation. DOE publishes this annual report to provide DOE Management, Program Offices, workers, health physicists, and other stakeholders an evaluation of DOE-wide performance regarding compliance with Title 10 of the Code of Federal Regulations (CFR) Part 835, Occupational Radiation Protection (10 CFR 835) radiation exposure limits and adherence to as low as reasonably achievable principles. This report provides a discussion regarding radiation protection and exposure reporting requirements. It also includes calendar year (CY) 2023 information and analyses regarding aggregate, individual, site, DOE Program, transient individuals’ dose, as well as a historical review of DOE exposure data. DOE continues to be diligent in protecting its workers and the public from exposure to radiation from DOE operations as illustrated by the results contained in this report.
Infrared organic photodetector materials are investigated using transient absorption spectroscopy, demonstrating that ultrafast charge generation assisted by polymer aggregation is essential to compensate for the energy gap law, which dictates that excited state lifetimes decrease as the band gap narrows. Short sub–picosecond singlet exciton lifetimes are measured in a structurally related series of infrared–absorbing copolymers that consist of alternating cyclopentadithiophene electron–rich “push” units and strong electron–deficient “pull” units, including benzothiadiazole, benzoselenadiazole, pyridalselenadiazole, or thiadiazoloquinoxaline. While the ultrafast lifetimes of excitons localized on individual polymer chains suggest that charge carrier generation will be inefficient, high detectivity for polymer:PC 71 BM infrared photodetectors is measured in the 0.6 < λ < 1.5 µm range. The photophysical processes leading to charge generation are investigated by performing a global analysis on transient absorption data of blended polymer:PC 71 BM films. In these blends, charge carriers form primarily at polymer aggregate sites on the ultrafast time scale (within our instrument response), leaving quickly decaying single–chain excitons unquenched. Lastly, the results have important implications for the further development of organic infrared optoelectronic devices, where targeting processes such as excited state delocalization over aggregates may be necessary to mitigate losses to ultrafast exciton decay as materials with even lower band gaps are developed.
PEVs can represent a significant power resource for the grid. An IVCI with bi-direction V2G capabilities would allow PEVs to provide grid support services and thus generate a source of revenue for PEV owners. The fleet of EV Project vehicles represents a power resource between 30 MW and 90 MW, depending on the power rating of the grid connection (5-15 kW). Aggregation of vehicle capacity would allow PEVs to participate in wholesale reserve capacity markets. One of the key insights from EV Project data is the fact that vehicles are connected to an EVSE much longer than is necessary to deliver a full charge. During these hours when the vehicles are not charging, they can be participating in wholesale power markets providing the high-value services of regulation and spinning reserves. The annual gross revenue potential for providing these services using the fleet of EV Project vehicles is several hundred thousands of dollars to several million dollars annually depending on the power rating of the grid interface, the number of hours providing grid services, and the market being served. On a per vehicle basis, providing grid services can generate several thousands of dollars over the life of the vehicle.
The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The PALMO database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series OVERFLOW calculations were run on the NASA High-End Compute Capability (HECC) supercomputer. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop accurate airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. This work presents an example PALMO surrogate model that enables accurate airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle of attack within the bounds of the database. Airfoil performance tables predicted for an airfoil not used in training the model are used in three-dimensional OVERFLOW simulations to quantify the downstream accuracy on aggregate rotor performance metrics. For the NACA 3415 airfoil, which had no common thickness or camber with the training data, the surrogate predicted and CFD generated tables were within 2.1% of each other in the forward flight lift to drag metric. This suggests that performance tables generated for airfoils within the bounds of the PALMO database will yield aggregate rotor performance predictions on par with tables generated from directly running OVERFLOW airfoil calculations. The PALMO airfoil performance coefficients are available publicly.
Weather radar imagery is a valuable new technique for the rapid recovery of meteorite falls, to include falls which would not otherwise be recovered (e.g. Battle Mountain). Weather radar imagery reveals about one new meteorite fall per year (18 falls since 1998), using weather radars in the United States alone. However, an additional ~75 other nations operate weather radar networks according to the UN World Meteorological Organization (WMO). If the imagery of those radars were analyzed, the current rate of meteorite falls could be improved considerably, to as much as ~3.6 times the current recovery rate based on comparison of total radar areal coverage. Recently, the addition of weather radar imagery, seismometry and internet-based aggregation of eyewitness reports has improved the speed and accuracy of fresh meteorite fall recovery [e.g. 1,2]. This was demonstrated recently with the radar-enabled recovery of the Sutter's Mill fall [3]. Arguably, the meteorites recovered via these methods are of special scientific value as they are relatively unweathered, fresh falls. To illustrate this, a recent SAO/NASA ADS search using the keyword "meteorite" shows that all 50 of the top search results included at least one named meteorite recovered from a meteorite fall. This is true even though only ~1260 named meteorite falls are recorded among the >49,000 individual falls recorded in the Meteoritical Society online database. The US NEXRAD system used thus far to locate meteorite falls covers most of the United States' surface area. Using a WMO map of the world's weather radars, we estimate that the total coverage of the other ~75 national weather radar networks equals about 3.6x NEXRAD's coverage area. There are two findings to draw from this calculation: 1) For the past 16 years during which 18 falls are seen in US radar data, there should be an additional ~65 meteorite falls recorded in worldwide radar imagery. Also: 2) if all of the world's radar data could be analyzed, the rate of recovery of fresh meteorite falls can increase by as much as ~3.6x the current rate. The authors' experience to date indicates that the most effective course of action would be to have local meteorite research groups (outside of the US) form research consortia and develop a working relationship with their nation's weather bureau for access to data. These research consortia could utilize the same, proven methods used for US NEXRAD imagery, internet eyewitness report aggregation, seismometry analysis, etc. to locate meteorite falls. The consortia could then recover and analyze meteorite falls and enrich their own research efforts. It would be beneficial to conduct a global program to coordinate the development of methods and data tools, as well as to coordinate meteorite sample sharing and research. Perhaps an institution such as the Meteoritical Society could lead such an effort.
This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.
Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ∼ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.
The nanoporosity and structure of natural carbons has been investigated on the example of Karelian carbon-rich shungites by comparing the data of small-angle synchrotron radiation scattering and highresolution microscopy. The analysis of small-angle scattering data is based on the model of scattering spheres with lognormal size distribution. It is found that the structure of samples from the Maksovo and Zazhogino deposits subjected to high temperatures in the geological medium and (also to a lesser extent) a sample from the Shunga deposit can be described as an aggregation of polydisperse scattering spheres with lognormal size distribution; the characteristic scatterer size is determined for them. A comparison with microscopy data shows that these scatterers are mainly associated with pores, and the character of their size distribution is similar to that previously established for nanoglobules in schungites.
The alkali-silica reaction (ASR) damage in reactive aggregates affects the long-term durability of the concrete infrastructure. The generated ASR gel can expand by imbibing water from the pore solution and the resulting expansion pressure causes the aggregate fracture. This study aims to simulate the development of ASR damage in glass particles of two types of samples (glass in alkali solution and glass mortars). The dynamic micro X-ray CT technique was conducted to monitor the crack propagation in glass aggregates at different reaction stages (up to 64 hrs). The Boundary Element Method (BEM) and Displacement Discontinuity Method (DDM) were used to efficiently simulate the crack propagation within irregular glass particles under gel expansion pressures. The aggregate boundaries were built with the BEM elements and the initial cracks were meshed with the DDM elements. The estimated expansion pressure was applied to the initial crack surfaces. The discontinuous displacements and stresses were calculated along the crack path and crack tip. The mixed-mode Stress Intensity Factors (SIFs) were calculated based on plane stress conditions. The maximum circumferential stress criteria was used to simulate the propagation of cracks along a specific angle. The simulation results include the simulated crack path and the combined SIFs changing with the increments. With the estimate expansion pressure, the glass particle damages were simulated within conditions of alkali solution and confined motar samples. The predicted crack propagation path was compared with the X-ray CT imaging data. The comparison results demonstrate the DDM has the ability to predicting the ASR damage propagation inside aggregates.
The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.
Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.
Continued research into reservoir characterization along with offshore carbon dioxide (CO 2 ) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO 2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO 2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO 2 sources. High-graded regions for the CO 2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.
The elements of the aggregate exposure pathway have been refined to better facilitate organization, integration, and dissemination of exposure data.
Climate change is modifying global biogeochemical cycles, and is expected to exert increasingly large effects in the future. How these changes will in turn affect and interact with the structure and function of particular ecosystems is unclear, however, both because of scientific uncertainties and the very diversity of global vegetation models in use. Writing in Environmental Research Letters, Warszawski et al. (1) aggregate results from a group of models, across a range of emissions scenarios and climate data, to investigate these risks. Although the models frequently disagree about which specific regions are at risk, they consistently predict a greater chance of ecosystem restructuring with more warming; this risk roughly doubles between 2 and 3 °C increases in global mean temperature. The innovative work of Warszawski et al. represents an important first step towards fully consistent multi-model, multi-scenario assessments of the future risks to global ecosystems.
Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.
The Axom library consists of n collection of software components that provide core computer science infrastructure capabilities that can be shared by diverse high performance computing (HPC) applications. The current set of capabilities that Axom provides includes: Customizable support for error/warning and diagnostic message reporting and coordination among components of integrated applications (e.g., physics packages, libraries, etc.); Scalable parallel aggregation and filtering of message logging; In memory datastore for hierarchical, mesh-aware simulation data. The datastore supports data description, allocation, deallocation. parallel l/0, etc; Mesh data model abstractions that enable the development of mesh-based computational algorithms that work with many diverse, application· specific mesh types; and Computational geometry primitives for developing high performance parallel algorithms based on spatial geometry and meshes, e.g., spatial acceleration data structures, sets, maps, relations, etc.
Heating hot water distribution systems are typically used in commercial buildings to condition spaces to provide occupant thermal comfort. However, recent research shows significant distribution losses within these systems that drive down the overall hot water plant efficiency. This research focuses on detecting passing valves in reheat coils found in variable air volume (VAV) terminal units to reduce distribution losses. A passing valve allows hot water flow when the actuator on the valve is commanded to be closed. The fluid causes unintentional heating or cooling to occur, causing comfort and control issues, and wasting energy. We developed the passing valve detection algorithm using a framework based on the Brick schema and Mortar platform to ensure that the application is portable and can scale to many buildings. We applied the same application to analyze 1,335 VAV reheat terminal units in 20 buildings. The diversity found in these large datasets increases confidence that any building with VAV reheat terminal units with the required sensors and Brick data model can run our open-source algorithm with little or no modification. In aggregate, 5% of VAV units analyzed were categorized as having a sensor fault, 14% with potential passing valve fault, and 81% with no faults detected. However, there is a significant variation in the proportion of VAV units with a passing valve detected (1% to 83%) of each building’s analyzed units.