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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 galactic magnetic field reversal near the Sagittarius spiral arm may be responsible for the southern excess (or northern shortage) of cosmic rays near 10 to the 18th power eV. The north-south asymmetry produced by such a reversal would increase with energy in the same manner as the observed asymmetry. The existence of a reversal has been inferred from analyses of Faraday rotation measures.
The global shift toward electric vehicles (EVs) for climate sustainability lacks comprehensive insights into the impact of the built environment on EV ownership, especially in varying spatial contexts. This study, focusing on New York State, integrates data fusion techniques across diverse datasets to examine the influence of socioeconomic and built environmental factors on EV ownership. The utilization of spatial regression models reveals consistent coefficient values, highlighting the robustness of the results, with the Spatial Lag model better at capturing spatial autocorrelation. Further, results underscore the significance of charging stations within a 10-mile radius, indicative of a preference for convenient charging options influencing EV ownership decisions. Factors like higher education levels, lower rental populations, and concentrations of older population align with increased EV ownership. Utilizing publicly available data offers a more accessible avenue for understanding EV ownership across regions, complementing traditional survey approaches.
The Ultra-Soft X-ray Telescope (UXT) was a sounding rocket mission flown three times in 1984 - 1986. At the beginning of the project, the data existed solely in form of raw telemetry data stored on 9 track tapes. The primary goal of this proposal has been to extract event files from the raw telemetry stream and to create instrument response models and calibrated spectra from it. We have completed this project, and the data will soon be available to all via the HEASARC archive of high-energy data at Goddard Space Flight Center. We are in the process of combining the results with the ALEXIS and DXS observations of the Local Bubble in modelling the 72 eV iron line (recently observed by the X-ray Quantum Calorimeter) and the carbon emission lines that are uniquely visible in this dataset. Our results agree with the XQC observation which predicts a maximum emission in the 72 eV iron lines that is below the limit observable with UXT. However, this leaves an open question as to what lines were responsible for the observed Be-band emission. The answer to this question will likely require more observations of soft X-rays with the Chandra LETGS and new atomic data models of potentially emitting ions.
We consider the effect of dark matter in the form of multi-eV axions on the extragalactic background light. Our treatment differs from that of other workers in that we assume axions to be clustered in Galactic halos, with nonzero velocity dispersions. We also approach the problem in a fully general relativistic manner, treating the axion halos as luminous elements of a pressure-free perfect fluid in a standard Friedmann-Robertson-Walker universe. We find that the ultraviolet extragalactic background light places a firm upper limit of 9 eV on the axion rest energy, and that this drops to 4 eV for the simplest axion models, all but closing the multi-eV axion window (which begins at 3 eV). These results are close to earlier upper limits of 5 and 8 eV derived from the extragalactic background by Turner and Ressell, respectively. Although our methods differ somewhat from theirs, our findings support their conclusion that axions, if they exist, are likely to have rest energies well below the eV range.
The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. This chapter focuses on residential electric vehicle (EV) incentive programs and multimodal electrified transportation services as means to increase equity in household transportation electrification. Specifically, NREL modeled EV adoption and affordability under business-as-usual and enhanced low-income incentives scenarios and transportation-related energy burdens under multimodal electric travel scenarios, including shared EVs, e-bikes, and improved transit services. Based on our analysis and community guidance, we identified strategies for 1)?increasing equity in new and used light-duty EV adoption and EV charging infrastructure distribution, focused on household used EV ownership and home charging access and 2) affordable, time-efficient, and equitable multimodal electrified transportation options, specifically considering the non-vehicle-owning population. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.
NREL will aid Smartville, Inc. to rapidly characterize electric vehicle (EV) battery state-of-health (SOH), characterize lifetime models, and forecast lifetime under various second-use reconditioning strategies.
Abstract The observation of X-rays during quiescence from transiently accreting neutron stars provides unique clues about the nature of dense matter. This, however, requires extensive modeling of the crusts and matching the results to observations. The pycnonuclear fusion reaction rates implemented in these models are theoretically calculated by extending phenomenological expressions and have large uncertainties spanning many orders of magnitude. We present the first sensitivity studies of these pycnonuclear fusion reactions in realistic network calculations. We also couple the reaction network with the thermal evolution code dStar to further study their impact on the neutron star cooling curves in quiescence. Varying the pycnonuclear fusion reaction rates alters the depth at which nuclear heat is deposited although the total heating remains constant. The enhancement of the pycnonuclear fusion reaction rates leads to an overall shallower deposition of nuclear heat. The impurity factors are also altered depending on the type of ashes deposited on the crust. These total changes correspond to a variation of up to 9 eV in the modeled cooling curves. While this is not sufficient to explain the shallow heat source, it is comparable to the observational uncertainties and can still be important for modeling the neutron star crust.
Microcalorimeters that leverage microwave kinetic inductance detectors to read out phonon signals in the particle-absorbing target, referred to as kinetic inductance phonon-mediated (KIPM) detectors, offer an attractive detector architecture to probe dark matter (DM) down to the fermionic thermal relic mass limit. A prototype KIPM detector featuring a single aluminum resonator patterned onto a 1-gram silicon substrate was operated in the Northwestern EXerimental Underground Site (NEXUS) low-background facility at Fermilab for characterization and evaluation of this detector architecture’s efficacy for a DM search. An energy calibration was performed by exposing the bare substrate to a pulsed source of 470-nm photons, resulting in a baseline resolution on the energy absorbed by the phonon sensor of 2.1 ± 0.2 eV, a factor of two better than the current state of the art, enabled by quasiparticle lifetimes extending up to 6.5 ms. However, due to the subpercent phonon collection efficiency, the resolution on energy deposited in the substrate is limited to σ E = 318 ± 29 eV. We further model both the signal pulse shape as a function of device temperature to extract quasiparticle lifetimes, and the observed noise spectra.
Caldera ICM is part of Caldera software platform, a suite of collective, open-source tools that was developed to improve the state of the art in modeling the impacts of Electric Vehicle (EV) charging on the grid. The foundation of Caldera ICM is it’s first-of-its-kind library of high-fidelity charging models for a wide variety of vehicles, validated by test data under a range of operating conditions. The charging models are used to accurately model the EV charging on an electric vehicle supply equipment (EVSE) – also known as a charger. ICM uses as inputs, the EV characteristics such as battery size, battery chemistry (i.e. NMC, LTO), watt hour per mile, inverter efficiency and max charge rate; as well as EVSE characteristics such as max current and supply equipment type (i.e. L2 vs XFC). Using these inputs Caldera ICM creates an uncontrolled charging profile curve for each compatible EV-EVSE pair. These charge profiles can be used stand alone to estimate charge duration given start State Of Charge (SOC) and end SOC or charge energy size given start SOC and charge duration. In Caldera Grid, another tool in the Caldera software platform, these charge profiles are used to represent EV charging as a load on the grid using charge event data such as EV type, SE type, start SOC, final SOC, park start time and park end time. ICM currently supports energy shifting Smart Charge Management (SCM) strategies such as "Time Of Use (TOU) immediate”, “TOU random” and “random start” by delaying the charge to start at a preferable time with respect to the strategy and, one voltage support SCM strategy named autonomous voltage control strategy by providing reactive power back to the electric grid.
In order to elucidate the nature of the imperfections which adversely affect pyroelectric processes in boracites, the thermal and dispersive characteristics of the low frequency dielectric response in iron-iodide boracite (Fe3B7O13I) and copper-chloride boracite (Cu3B7O13Cl) have been investigated. These characteristics were measured as a function of crystallographic orientation and applied field in both the ferroelectric and paraelectric states. The low frequency dielectric relaxation of 100 line oriented multi-domain copper-chloride boracite clearly indicates the dipole nature of the lattice imperfections. The activation energies calculated from a noninteracting Debye model, are 0.53 eV in the ferroelectric phase and 0.10 eV in the paraelectric phase.
Results from a Ginga observation of the AM Her type object H0538 + 608 are presented. During the first half of the observation (pulsing state) the source showed periodic modulations caused by the white dwarf rotation, and the modulation profile was characterized by a flat top and a flat bottom. During the second half, the source was in a remarkable flaring state and the periodic modulation disappeared. The dramatic change of the X-ray light curve is interpreted in terms of a change of accretion pattern between an eclipsing spot and a noneclipsing spot. The spectrum is well described throughout by a thin thermal model including an iron K-line with equivalent width of some hundred eV, supporting the standard model for the hard X-ray emission region. The temperature in the pulsing state was found to change with the spin phase of the white dwarf. In the flaring state, the temperature showed a positive correlation with the source luminosity.
We perform maximum likelihood estimates (MLEs) for single and double flavor ultralight dark matter (ULDM) models using the Spitzer Photometry and Accurate Rotation Curves (SPARC) database. These estimates are compared to MLEs for several commonly used cold dark matter (CDM) models. By comparing various CDM models we find, in agreement with previous studies, that the Burkert and Einasto models tend to perform better than other commonly used CDM models. We focus on comparisons between the Einasto and ULDM models and analyze cases for which the ULDM particle masses are: free to vary; and fixed. For each of these analyses, we perform fits assuming the soliton and halo profiles are: summed together; and matched at a given radius. When we let the particle masses vary, we find a negligible preference for any particular range of particle masses, within $10^{-25}\,\text{eV}\leq m\leq10^{-19}\,\text{eV}$, when assuming the summed models. For the matched models, however, we find that almost all galaxies prefer particles masses in the range $10^{-23}\,\text{eV}\lesssim m\lesssim10^{-20}\,\text{eV}$. For both double flavor models we find that most galaxies prefer approximately equal particle masses. We find that the summed models give much larger variances with respect to the soliton-halo (SH) relation than the matched models. When the particle masses are fixed, the matched models give median and mean soliton and halo values that fall within the SH relation bounds, for most masses scanned. When the particle masses are fixed in the fitting procedure, we find the best fit results for the particle mass $m=10^{-20.5}\,\text{eV}$ (for the single flavor models) and $m_1=10^{-20.5}\,\text{eV}$, $m_2=10^{-20.2}\,\text{eV}$ for the double flavor, matched model. We discuss how our study will be furthered using a reinforcement learning algorithm.
We report on Chandra ACE observations of the ultracompact AM CVn binary ES Cet. This object has a 10.3 minute binary period and is the most compact of the confirmed AM CVn systems. We have, for the first time, unambiguously detected the X-ray counterpart to ES Cet. In a 20 ksec ACIS-S image a point-like X-ray source is found within 1 sec. of the catalogued optical position. The mean countrate in ACIS-S is 0.013/s, and there is no strong evidence for variability. We folded the X-ray data using the optical ephemeris of Warner & Woudt, but did not detect any significant modulation. If an approx. = 100% modulation similar to those seen in the ultracompact candidates V407 Vu1 and Rx J0806.3+1527 were present then we would have detected it. The upper limit (3(sigma)) to any modulation at the putative orbital period is approx. 40% (rms). We extract the first X-ray spectrum from ES Cet, and find that it is not well described by simple continuum models. We find suggestive evidence for discrete spectral components at approx. 470 and 890 eV, that can be modelled as gaussian emission lines. In comparison with recent X-ray detections of nitrogen and neon in another AM CVn system (GP Com), it appears possible that these features may represent emission lines from these same elements; however, deeper spectroscopy will be required to confirm this. Our best spectral model includes a black body continuum with kT = 0.8 keV along with the gaussian lines. The 0.2 - 5 keV X-ray flux was approx. 7 x 10(exp -14) ergs/sq cm s. The luminosity implied by this flux for any reasonable distance is much smaller than that expected for a mass accretion rate as high as m = 10(exp -8) solar mass/yr, suggesting that the bulk of the accretion luminosity is below 100 eV and not seen with Chandra. We discuss the implications of our results for the nature of ES Cet.
The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.
This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.
The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.
Electric vehicles (EVs) are becoming a promising source of grid ancillary services due to the temporal and spatial charging flexibility, quick response and storage capability. Such advantages are increasing with government policy promotion and technology improvement. However, the exploration of EV flexibility requires the coordination of both transmission system operators (TSOs) and distribution system operators (DSOs), to ensure the safe and reliable operation of power network. In this paper, we propose a coordinated evaluation method that determines the optimal utilization of EV temporal flexibility without compromising EV owners’ usage. At the distribution level, DSOs first evaluate EV aggregators’ operational boundaries to exploit distribution level services. At the transmission level, TSOs then determine EV charging schedules and ancillary service capacity simultaneously, taking into account the requirement from DSOs. Here, we validate the model in a case study using the IEEE 123 node test feeder and EV charging sessions obtained from a transportation simulation tool that uses real-world data.
In this work Δ learning is used to map orbital-free density functional theory (OF-DFT) ionic forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the approximate force difference in terms of the ion positions is constructed and serves as a stand in for the ground truth force difference. Descriptor vectors for ion configurations are constructed using all distance between ions in conjunction with an indexing based on a nearest neighbor ranking. It is demonstrated that such a scheme of descriptors can uniquely describe an ionic configuration up to a rotation and reflection when no ambiguity in the nearest neighbor ranking exists. How to handle the case when an ambiguity exists in the nearest neighbor ranking is discussed. As a proof of principle, the model is trained and tested on warm dense hydrogen at temperatures between 1 and 15 eV. Once tested, the model was used to perform molecular dynamic simulations of warm dense hydrogen. Furthermore, the resulting energies and pressures are within 1% and 2% of their respective target KS values.