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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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Once a site location is selected for the installation of an electric vehicle (EV) charging station, the next step is to plan and design the specific station. Local and state governments can require and/ or incentivize accessible and safe design elements, so the station is constructed in an equitable manner.
When electric vehicle (EV) batteries degrade below a certain capacity, they may no longer be suitable for automotive use but can be repurposed as second-life batteries (SLBs) for other applications, such as EV charging stations. When integrated with photovoltaic (PV) systems, SLB can store surplus solar energy, reducing reliance on the grid and lowering operational costs. This paper presents a novel techno-economic assessment framework for deploying SLBs in combination with PV in grid-connected EV charging stations. The proposed framework integrates the value proposition, charging station operation, optimal dispatch strategies, battery degradation modeling, input data requirements, and detailed procedures for generating key economic performance metrics. Insightful analyses are performed to assess the performance of SLBs in comparison to new batteries across various cost scenarios. The results indicate that SLBs become financially attractive when their cost is 40% or lower than new batteries.
This is the AmeriFlux version of the carbon flux data for the site US-DUF Denver Urban Field Station. Site Description - This site was established on a 25m freestanding former radio tower at the Forest Service's Rocky Mountain Research Station headquarters in Fort Collins, CO. The tower is situated next to a parking lot and several one and two story buildings. To the north and west is the campus of Colorado State University, and to the north and east is low density residential development. To the south and east are a busy intersection and rapid transit bus line, and to the south is green space surrounding the City of Fort Collins' Spring Creek Trail.
We analyzed 40 year data sets of daily average visibility (a proxy for surface aerosol concentration) and hourly precipitation at seven weather stations, including three stations located on the Taihang Mountains, during the summertime in northern China. There was no significant trend in summertime total precipitation at almost all stations. However, light rain decreased, whereas heavy rain increased as visibility decreased over the period studied. The decrease in light rain was seen in both orographic-forced shallow clouds and mesoscale stratiform clouds. The consistent trends in observed changes in visibility, precipitation, and orographic factor appear to be a testimony to the effects of aerosols. The potential impact of large-scale environmental factors, such as precipitable water, convective available potential energy, and vertical wind shear, on precipitation was investigated. No direct links were found. To validate our observational hypothesis about aerosol effects, Weather Research and Forecasting model simulations with spectral-bin microphysics at the cloud-resolving scale were conducted. Model results confirmed the role of aerosol indirect effects in reducing the light rain amount and frequency in the mountainous area for both orographic-forced shallow clouds and mesoscale stratiform clouds and in eliciting a different response in the neighboring plains. The opposite response of light rain to the increase in pollution when there is no terrain included in the model suggests that orography is likely a significant factor contributing to the opposite trends in light rain seen in mountainous and plain areas.
Abstract Noninvasive, camera‐based, scent station surveys have become a common approach to estimate population parameters for mammalian carnivores. Many carnivores are particularly sensitive to olfactory cues; and, as such, the quality and quantity of odor attractant used during surveys can significantly affect the accuracy and precision of the survey. Additionally, survey length is also an important consideration because many mammalian predators are highly mobile and elusive in nature. Using scent station surveys in autumn 2013 and 2014 on the Savannah River Site, Aiken, South Carolina, USA, we tested the effects of 5 lures: fatty acid, fish oil, synthetic fermented egg, beaver castor, and skunk essence on visitation rates, visitation probability, and latency to visitation of multiple predator species. We also estimated occupancy probability for 2 carnivore species using different survey lengths to determine the effect of survey period on the precision of occupancy estimates. We found that skunk essence had greater visitation probability and visits than other lures evaluated. Species‐specific bait‐preference models revealed that raccoons ( Procyon lotor ) were more likely to visit skunk essence, and coyotes ( Canis latrans ) were less likely to visit fish oil as compared with fatty acid. Latency to visitation ranged from 104.7 to 112.1 hours and did not vary among scents. Longer survey durations resulted in more precise estimates of occupancy and detection probability. Our results suggest a wide range of scents commonly used for carnivore capture may perform as well as, or better than, fatty acid for noninvasive carnivore surveys. Further, study designs should carefully consider the duration of scent‐station surveys depending on the level of precision desired in estimates of occupancy and detection probability. © 2019 The Wildlife Society.
Unburned methane entrained in exhaust from natural gas-fired compressor engines (“combustion slip”) can account for a substantial portion of station-level methane emissions. A novel in-stack, tracer gas method was coupled with Fourier transform infrared (FTIR) species measurements to quantify combustion slip from natural gas compressor engines at 67 gathering and boosting stations owned or managed by nine “study partner” operators in 11 U.S. states. The mean methane emission rate from 63 four-stroke, lean-burn (4SLB) compressor engines was 5.62 kg/h (95% CI = 5.15–6.17 kg/h) and ranged from 0.3 to 12.6 kg/h. The mean methane emission rate from 39 fourstroke, rich-burn (4SRB) compressor engines was 0.40 kg/h (95% CI = 0.37– 0.42 kg/h) and ranged from 0.01 to 4.5 kg/h. Study results for 4SLB engines were lower than both the U.S. EPA compilation of air pollutant emission factors (AP-42) and Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) by 8 and 9%, respectively. Study results for 4SRB engines were 43% of the AP-42 emission factor and 8% of the GHGI emission factor, the latter of which does not distinguish between engine types. Total annual combustion slip from the U.S. natural gas gathering and boosting sector was modeled using measured emission rates and compressor unit counts from the U.S. EPA Greenhouse Gas Reporting Program. Modeled results [328 Gg/y (95% CI = 235–436 Gg/y) of unburned methane] would account for 24% (95% CI = 17–31%) of the 1391 Gg of methane emissions for “Gathering and Boosting Stations”, or 6% of the net emissions for “Natural Gas Systems” (5598 Gg) as reported in the 2020 U.S. EPA GHGI. In conclusion, gathering and boosting combustion slip emissions reported in the 2020 GHGI (374 Gg) fall within the uncertainty of this model.
Quantification and understanding of how heat, rainfall, and air quality vary within cities are needed to identify the area with the worst conditions, develop solutions to extreme weather, and assess the impact of proposed policies. However, neighborhood-level variability is not well quantified because there are few environmental measurement stations within cities. In Baltimore City, a community-based network of weather stations to address this issue has been developed through a partnership between universities, state agencies, and Baltimore residents. The weather stations are hosted by community partners, and the data collected are enabling the mapping of urban weather across the city and the testing of models and proposed mitigation strategies. In addition, the network provides direct community involvement, with resulting benefits of increased community engagement, education, and empowerment. Researchers have an opportunity to democratize the scientific process and ensure that local knowledge and lived experiences of city residents inform future decision-making. The approach could be used as a model for other cities that apply similar monitoring instruments for other environmental exposures.
We describe the Moderator Test Station (MTS), under construction at the Spallation Neutron Source. We will leverage the Beam Test Facility (BTF) at the Spallation Neutron Source to provide a moderator neutronics test stand with which we will verify the anticipated performance gains expected and required from the innovative moderator concepts central to the SNS Second Target Station (STS) and the future of the First Target Station (FTS). These concepts include large-volume para-hydrogen moderators and high-brightness tube moderators. The MTS will add to the existing BTF a proton beam chopper similar to that already used in the SNS at the RFQ exit (the so-called MEBT chopper), various proton beam transport components, a neutron-producing target, a cryogenic moderator test stand, a reflector-shielding assembly, and a performance assessment neutron beamline. The MTS will provide the ability to test a wide variety of moderators in a prototypic configuration, simultaneously measuring the neutronic performance of the moderator concept central to the anticipated STS gains and developing the instrumentation necessary to provide that performance in a production environment. The presentation will describe the MTS, its progress, and the way we optimize the prototypic nature of the moderators we will test.
The numerical analysis of hydro power stations is an important method of the hydraulic design and is used for the development and optimisation of hydro power stations in addition to the experiments with the physical submodel of a full model in the hydraulic laboratory. For the numerical analysis, 2D and 3D models are appropriate and commonly used.The 2D models refer mainly to the shallow water equations (SWE), since for this flow model a large experience on a wide field of applications for the flow analysis of numerous problems in hydraulic engineering already exists. Often, the flow model is verified by in situ measurements. In order to consider 3D flow phenomena close to singularities like weirs, hydro power stations etc. the development of a hybrid fluid model is advantageous to improve the quality and significance of the global model. Here, an extended hybrid flow model based on the principle of the SWE is presented. The hybrid flow model directly links the numerical model with the experimental data, which may originate from physical full models, physical submodels and in-situ measurements. Hence a wide field of application of the hybrid model emerges including the improvement of numerical models and the strong coupling of numerical and experimental analysis.
Anomalies in radio-frequency (rf) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC’s Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-based detection, combining rf amplitude and beam position monitor data. Due to the simplicity of the rf amplitude data, classical methods are sufficient to identify faults, but the recall is constrained by the low-frequency and asynchronous characteristics of the data. In this work, we leverage high-frequency, time-synchronous rf phase data to enhance anomaly detection in the LCLS accelerator. Due to the complexity of phase data, classical methods fail, and we instead train deep neural networks within the Coincident Anomaly Detection (CoAD) framework. We find that applying CoAD to phase data detects nearly 3 times as many anomalies as when applied to amplitude data, while achieving broader coverage across rf stations. Furthermore, the rich structure of phase data enables us to cluster anomalies into distinct physical categories. Through the integration of auxiliary system status bits, we link clusters to specific fault signatures, providing additional granularity for uncovering the root cause of faults. We also investigate interpretability via Shapley values, confirming that the learned models focus on the most informative regions of the data and providing insight for cases where the model makes mistakes. This work demonstrates that phase-based anomaly detection for rf stations improves both diagnostic coverage and root cause analysis in accelerator systems and that deep neural networks are essential for effective analysis.
This paper describes a new large-range rapid-scan X-ray fluorescence (XRF) imaging station at beamline 6-2 at the Stanford Synchrotron Radiation Lightsource at SLAC National Accelerator Laboratory. This station uses a continuous rapid-scan system with a scan range of 1000 × 600 mm and a load capacity of up to 25 kg, capable of 25–100 µm resolution elemental XRF mapping and X-ray absorption spectroscopy (XAS) of a wide range of objects. XRF is measured using a four-element Hitachi Vortex ME4 silicon drift detector coupled to a Quantum Detectors Xspress3 multi-channel analyzer system. A custom system allows the X-ray spot size to be changed quickly and easily via pinholes ranging from 25 to 100 µm, and the use of a poly-capillary or axially symmetric achromatic optic may achieve a <10 µm resolution in the future. The instrument is located at wiggler beamline 6-2 which has an energy range of 2.1–17 keV, creating K emission for elements up to strontium, and L or M emission for all other elements. XAS can also be performed at selected sample positions within the same experiment, allowing for a more detailed chemical characterization of the elements of interest. Furthermore, sparse excitation energy XRF imaging can be performed over a wide range of incident X-ray energies. User friendliness has been emphasized in all stages of the experiment, including versatile sample mounts, He purged chambers for low- Z analyses, and intuitive visualization hardware and software. The station provides analysis capabilities for a wide range of materials and research fields including biological, chemical, environmental and materials science, paleontology, geology and cultural heritage.
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.
The soiling loss factor (SLF) of photovoltaic (PV) modules/system is an interplay between the dust frequency and intensity of the site, rain frequency and intensity of the site, tilt angle and height of the module installation, and wind speed and humidity of the site. The integrated area of the downward peaks of the SLF time series plots for a year provides the annual soiling loss for the system at each tilt angle. Sandia National Laboratories, in collaboration with Arizona State University, installed five regional soiling stations around the country and collected soiling loss data over a year. Four of these soiling stations are located at the U.S. Department of Energy Regional Test Centers (Florida, Albuquerque, Colorado and Vermont), while the fifth station is located at the Arizona State University Photovoltaic Reliability Lab (Arizona). This paper presents an analysis on the SLF for each test site at ten different tilt angles. Based on the analysis of a yearlong data obtained in 2015, it appears to indicate that the Arizona site experienced the highest annual soiling loss with a significant dependence on the tilt angle while the other four sites experienced a negligibly small annual soiling loss with practically no dependence on the tilt angle.
Due to the promising potential for environmental sustainability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for EV charging stations is developed and aims to balance current capital investment costs and future operational revenue. The charging station considered in this work is assumed to be equipped with solar photovoltaic panel (PV) and an energy storage system which could be electric battery or the recently invented hydro-pneumatic energy storage (GLIDES, Ground-Level Integrated Diverse Energy Storage) system. A co-optimization model that minimizes investment and operation cost is established to determine the global optimal solution while combining the capacity and operational decision making. The operational decision making considers EV mobility which is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of the EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in the design space and then fixed in solving the co-optimization model. The model can be used to provide insights for charging station placement in different practical situations. The sampled parameters include: the total number of EV charging slots, the PV area, the maximum capacity of the energy storage system, and daily mean EV arrival number in the Erlang-loss system. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models (RBF, Kriging etc). The effectiveness of proposed surrogate modeling approach is demonstrated in the numerical experiments.
Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) and aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves quality of resultant treatment plans as compared with conventional VMAT or IMRT treatments.
Hydrogen safety sensors must meet specific performance requirements, mandated by the U.S. Department of Energy, for hydrogen fueling station monitoring. Here, we describe the long-term performance of two zirconia-based mixed potential electrochemical hydrogen gas sensors, developed specifically with a high sensitivity to hydrogen, low cross-sensitivity, and fast response time. Over a two-year period, sensors with tin-doped indium oxide and strontium doped lanthanum chromite electrodes were deployed at two stations in four field trials tests conducted in Los Angeles. The sensors documented the existence of hydrogen plumes ranging in concentration from 100 to as high as 2700 ppm in the area surrounding the dispenser, consistent with depressurization from 700 bar following vehicle refueling. As expected, the hydrogen concentration reported by the mixed potential sensors was influenced by wind direction. Baseline stability testing at a Chino, CA station showed no measureable baseline drift throughout 206 days of uninterrupted data acquisition. In conclusion, the high baseline stability, excellent correlation with logged fueling/depressurization events, and absence of false alarms suggest that the zirconia-based mixed potential sensor platform is a good candidate for protecting hydrogen infrastructure where frequent calibrations or sensor replacement to reduce the false alarm frequency have been shown to be cost prohibitive.
Solar radiation is a prime driver of energy and carbon fluxes in natural ecosystems. Radiation measurements are essential for interpreting ecosystem scale greenhouse gases and energy fluxes as well as many other observations performed at ecosystem stations of the Integrated Carbon Observation System (ICOS). Here, we describe and explain the relevance of the radiation variables that are monitored continuously at ICOS ecosystem stations and define recommendations to perform these measurements with consistent and comparable accuracy. The measurement methodology and instruments are described including detailed technical specifications. Guidelines for instrumental set up as well as for operation, maintenance and data collection are defined considering both ICOS scientific objectives and practical operational constraints. For measurements of short-wave (solar) and long wave (infrared) radiation components, requirements for the ICOS network are based on available well-defined state-of-the art standards (World Meteorological Organization, International Organization for Standardization). For photosynthetically active radiation measurements, some basic instrumental requirements are based on the performance of commercially available sensors. Since site specific conditions and practical constraints at individual ICOS ecosystem stations may hamper the applicability of standard requirements, we recommend that ICOS develops mid-term coordinated actions to assess the effective level of uncertainties in radiation measurements at the network scale.
In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found at the AGAGE home page: https://agage.mit.edu/instruments. Data from the AGAGE and affiliated stations (total of 9 sites) between August 1993 and March 2021 are provided in “Agage_gcmd_gcms_data_2022_05_26_tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2022_v1.pdf". Additional information can be found on the AGAGE website (https://agage.mit.edu).