SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A previously derived failure model for battery lifetime is discussed in terms of growth rate of the flaw, distribution of flaw sizes, and number of flaws. Equations are presented for determining the failure model for a nickel cadmium battery.
The origin, confinement, and transport of cosmic ray nuclei in the galaxy was studied. The work involves interpretations of the existing cosmic ray physics database derived from both balloon and satellite measurements, combined with an effort directed towards defining the next generation of instruments for the study of cosmic radiation. The shape and the energy dependence of the cosmic ray pathlength distribution in the galaxy was studied, demonstrating that the leaky box model is not a good representation of the detailed particle transport over the energy range covered by the database. Alternative confinement methods were investigated, analyzing the confinement lifetime in these models based upon the available data for radioactive secondary isotopes. The source abundances of several isotopes were studied using compiled nuclear physics data and the detailed transport calculations. The effects of distributed particle acceleration on the secondary to primary ratios were investigated.
Electric aircraft require high reliability electric motors to meet the stringent safety standards of the aviation industry. Motor winding insulation lifetime and reliability models for electric aircraft applications are needed to enable design of high reliability and high-performance electric motors for aviation applications. Three primary stresses effect the lifetime of motor winding insulation: thermochemical, mechanical, and electrical. This paper presents preliminary experiments on twisted pairs of magnet wire targeting understanding the thermo-chemical degradation of motor winding insulation through constant temperature thermal aging. Three types of magnet wire twisted pairs where tested: polyimide insulated magnet wire, epoxy coated polyimide insulated magnet wire, and epoxy potted polyimide magnet wire. The degradation of the samples over time at temperature was tracked using partial discharge inception voltage measurements. These initial experiments were used for development of procedures and expertise in partial discharge testing and thermo-chemical aging to inform future testing. The preliminary nature of these experiments should be noted when viewing the results. Lessons learned from the experiments are documented.
We present a new method to infer emissions and lifetimes of nitrogen oxides (NO x ) based on tropospheric nitrogen dioxide (NO 2 ) observations together with reanalysis wind fields for cities located in polluted backgrounds. Since the accuracy of the method is difficult to assess due to lack of “true values” that can be used as a benchmark, we apply the method to synthetic NO 2 observations derived from the NASA-Unified Weather Research and Forecasting (NU-WRF) model at a high horizontal spatial resolution of 4 km × 4 km for cities over the continental United States. We compare the inferred emissions and lifetimes with the values given by the NU-WRF model to evaluate the method. The method is applicable to 26 US cities. The derived results are generally in good agreement with the values given by the model, with the relative differences of 2 % ± 17 % (mean ± standard deviation) and 15 % ± 25 % for lifetimes and emissions, respectively. Our investigation suggests that the use of wind data prior to the satellite overpass time improves the performance of the method. The correlation coefficients between inferred and NU-WRF lifetimes increase from 0.56 to 0.79 and for emissions increase from 0.88 to 0.96 when comparing results based on wind fields sampled simultaneously with satellite observations and averaged over 9 h data prior to satellite observations, respectively. We estimate that uncertainties in NO x lifetime and emissions arising from the method are approximately 15 % and 20 %, respectively, for typical (US) cities. The total uncertainties reach up to 43% (lifetimes) and 45% (emissions) by considering the additional uncertainties associated with satellite NO 2 observations and wind data. We expect this new method to be applicable to NO 2 observations from the TROPOspheric Monitoring Instrument (TROPOMI) and geostationary satellites, such as Geostationary Environment Monitoring Spectrometer (GEMS) or the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument, to estimate urban NO x emissions and lifetimes globally.
Abstract Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.
We have studied the magneto-electroluminescence (MEL) response in light emitting diodes based on 2D-(PEA) 2 PbI 4 and 3D-MAPbI 3 hybrid organic–inorganic perovskites at cryogenic temperatures. We found that the MEL is negative, i.e., the EL decreases with the applied field strength, B. In addition, the MEL(B) response has a Lorentzian line shape whose width depends on the perovskite used. We interpret the MEL(B) response using the “Δg mechanism” in which the spin of the injected electron–hole (e–h) pairs oscillates between singlet and triplet configurations due to different precession frequencies of the electron and hole constituents that originate from the difference, Δg, in the electron and hole gyromagnetic constants, g. In this model, the MEL(B) linewidth is inversely proportional to the spin lifetime and Δg. The model used is validated by directly measuring the spin lifetime of photogenerated e–h pairs using the circularly polarized pump–probe transmission technique with 100 fs resolution.
The annual percentage increases in concentrations of the chlorofluorocarbons CFC-113 (an industrial solvent) and CFC-22 (a refrigerant) are the highest among major chlorofluorocarbons in the atmosphere today. The present-day atmospheric lifetimes for these species are computed using a global three-dimensional dynamical-chemical model. The present-day lifetimes of both are long (15.5 years for CFC-22 and 136 or 195 years for CFC-113, depending on assumed O2 absorption cross sections), underscoring the need to decrease their emissions in order to minimize their future role in ozone destruction and greenhouse warming.
The topics covered include the following: (1) CO2 laser kinetics modeling; (2) gas lifetimes in pulsed CO2 lasers; (3) frequency chirp and laser pulse spectral analysis; (4) LAWS A' Design Study; and (5) discharge circuit components for LAWS. The appendices include LAWS Memos, computer modeling of pulsed CO2 lasers for lidar applications, discharge circuit considerations for pulsed CO2 lidars, and presentation made at the Code RC Review.
The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.
The separation of the cosmic-ray Be isotopes achieved by two satellite experiments is discussed. From the measured isotopic ratio between the radioactive Be-10 (half-life = 1,500,000 years) and the stable Be-9, it is deduced that the cosmic rays propagate through matter with an average density of 0.24 = or - 0.07 atoms/ cu cm. The implications of this result for the cosmic-ray age is reviewed in the context of two models of confinement and propagation: the homogeneous model, normally identified with confinement ot the galactic gaseous disk, and a diffusion model in which the cosmic rays extend into a galactic halo. A newly deduced cosmic-ray pathlength distribution, a self-consistent model of solar modulation, and a set of fragmentation cross sections are calculated. It is concluded that in a homogeneous model, the cosmic-ray age is 15 (+7, -4) million years, about a factor longer than early estimates based on the abundances of the light nuclei Li, Be, and B and a nominal interstellar density of 1 atom/ cu cm.
A simple analytic model predicted Hall thruster channel erosion based on thruster geometry, operating conditions, and magnetic field configuration. This model relied on a one-dimensional representation of the plasma with a fixed ionization fraction and variable ion energies based on the magnetic field distribution. Sputtering was modeled as the result of elastic scattering of ions by neutrals within the channel. Not all scattered ions and neutrals were assumed to reach the channel walls as a result of additional subsequent scattering events. Incorporating this phenomenon resulted in a greater predicted decrease in erosion rate with time than predicted based only on geometric effects. Results from this model were compared to SPT 100 experimental erosion data.
Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.
The calculation of ozone column depth as a function of atmospheric O2concentration has been performed multiple times over the past 40 years using one-dimensional (1-D) photochemical models. Several of these calculations [1–3] have been done by the Kasting group. We refer to the current version of that model as the‘standard’1-D model (see §2). Recently, Cooke et al., henceforth C22 ,performed a similar calculation using a three-dimensional (3-D) coupled chemistry-climate model (WACCM6). They found substantially lower ozone column depths at lower pO2levels. For example, at0.01 times the present atmospheric level (PAL) of O(2), Segura et al. calculated 124 Dobson units(DU), whereas C22 calculated a mean column depth of just 66 DU—almost a factor of two lower. This comparison is shown in figure 1. C22 argued, correctly, that 3-D models should be able to do a better job on this problem than 1-D models because ozone column depths are affected by atmospheric transport, e.g. the Brewer–Dobson circulation in the stratosphere that blows ozone from the tropics towards the poles, as well as vertical transport that carries ozone downward from the stratosphere to the troposphere. Meridional transport is absent in 1-D models, and vertical transport is fixed, so model geometry almost certainly contributes to the observed discrepancies in ozone column depth. The 3-D model also has a variable tropopause height (higher at the equator, lower towards the poles), which is difficult to simulate in one dimension.
Concise report discusses recent developments in use of semiempirical mathematical models to predict rates of failure and operating lifetimes of complementary metal oxide/semiconductor (CMOS) application-specific integrated circuits (ASIC's). Each model represents specific mechanism of failure. Once failure mechanisms and models relevant to given ASIC chosen, adjustable parameters in models fitted to life-test data acquired from representative integrated-circuit structures on test coupons fabricated along with ASIC's. Then design parameters of ASIC's incorporated into models, and models yield lifetimes.
We discuss the far-ultraviolet upturn phenomenon (UVX) observed in elliptical galaxies and spiral galaxy bulges. Our premise is the UV radiation from these systems emanates primarily from extreme horizontal branch (EHB) stars and their progeny. We derive the broad-band UV colors 1500-V and 2500-V for globular clusters and elliptical galaxies from the available satellite data and investigate color-color and color-line strength correlation. Clusters can be bluer than any galaxy in 15-V and 25-V, implying larger hot star populations, but galaxies are significantly bluer than clusters in 15-25 at a given 15-V. We attribute this primarily to the effect of metal abundance on the mid-UV (2500 A) light. These redder colors of the galaxies also imply that the UVX in galaxies is not produced by metal-poor subpopulations similar to the clusters. We devlop a simple spectral synthesis formulation for all phases of single star evolution from the zero-age main sequence (ZAMS) to the white dwarf cooling track that requires only one or two parameters for each choice of age and abundance. We provide the ingredients necessary for constructing models with arbitrary horizontal branch (HB) morphologies in the age range 2 less than t less than 20 Gyr and for six metallicities in the range -2.26 less than (Fe/H) less than 0.58; we also consider the efect of enhanced Y in metal-rich models. The maximum lifetime UV output is produced by EHB stars with (M(sub env))(sup 0) approximately 0.02 solar mass and can be up to 30 times higher than for post-asymptotic giant branch (P-AGB) stars. The ultraviolet output of old populations is governed primarily by the distribution of (M(sub env))(sup 0)P(M(sub env))(sup 0), on the ZAHB. The UV output is not very sensitive to (Fe/H) or to Y, but it can change very rapidly with (M(sub env))(sup 0). Thus it is extremely sensitive to the precise nature of giant-branch mass loss. Our models use simple descriptions of P(M(sub env))(sup 0) to bracket the colors produced from any real distribution of stars. Our models accurately predict the range of UV colors observed for the globular clusters, given known constraints on their age, abundances, and HB morphologies. We find that models with (Fe/H) greater than or = 0 that do not contain EHB stars cannot reproduce the colors of most of the galaxies. The models also predict that the fraction of the far-UV light from P-AGB stars, which are spatially resolvable in nearby galaxies, is approximately 70% and approximately 20% for moderate UVX and strong UVX systems, respectively. We find that 25-V, but not 15-V, is sensitive to the age and abundance, though these cannot always be cleanly distinguished. The galaxy colors place limits of (Fe/H) greater than -0.5 and less than 15% on the contribution of globular cluster-type populations to the V light. Galaxy colors are consistent with solar-abundance models with ages in the range 6-14 Gyr. We discuss several implications of the observations and the models, including the question of light metal versus iron peak enhancements in galaxies, whether the UV color-Mg(sub 2) correlation is continuous or discrete, effects of helium abundnace on the UVX, and the key question of whether red giant branch mass loss can be large enough to produce the necessary EHB population in the strong UVX galaxies.
Despite the large binding energy of charge transfer (CT) excitons in type-II organic/2D heterostructures, it has been demonstrated that free carriers can be generated from CT excitons with a long lifetime. Using a model fluorinated zine phthalocyanine (F 8 ZnPc)/monolayer-WS 2 interface, we find that CT excitons can dissociate spontaneously into free carriers despite it being an enthalpy-uphill process. Specifically, it is observed that CT excitons can gain an energy of 250 meV in 50 ps and dissociate into free carriers without any applied electric field. This observation is surprising because excited electrons typically lose energy to the environment and relax to lower energy states. Here, we hypothesize that this abnormal enthalpy-uphill CT exciton dissociation process is driven by entropy gain. Kinetically, the entropic driving force can also reduce the rate for the reverse process – the conversion of free electron–hole pairs back to CT excitons. Hence, this mechanism can potentially explain the very long carrier lifetime observed in organic/2D heterostructures.
A three-region mathematical model of gas bubble dynamics has been shown suitable for describing diffusion-limited dynamics of more than one bubble in a given volume of extravascular tissue. The model is based on the dynamics of gas exchange between a bubble and a well-stirred tissue region through an intervening unperfused diffusion region previously assumed to have constant thickness and uniform gas diffusivity. As a result, the gas content of the diffusion region remains constant as the volume of the region increases with bubble growth, causing dissolved gas in the region to violate Henry's law. Earlier work also neglected the relationship between the varying diffusion region volume and the fixed total tissue volume, because only cases in which the diffusion region volume is a small fraction of the overall tissue volume were considered. We herein extend the three-region model to correct these theoretical inconsistencies by allowing both the thickness and gas content of the diffusion region to vary during bubble evolution. A postulated difference in gas diffusivity between an infinitesimally thin layer at the bubble surface and the remainder of the diffusion region leads to variation in diffusion region gas content and thickness during bubble growth and resolution. This variable thickness, differential diffusivity (VTDD) model can yield bubble lifetimes considerably longer than those yielded by earlier three-region models for given model and decompression parameters, and meets a need for theoretically consistent but relatively simple bubble dynamics models for use in studies of decompression sickness (DCS) in human subjects, Keywords: decompression sickness, gas diffusion in tissue, diffusivity
Column densities from satellite retrievals can provide valuable information for estimating emissions and chemical lifetimes objectively across the globe. To better understand the uncertainties associated with these estimates, we test four methods using simulated column densities from a point source: a box model approach, a 2D Gaussian fit, an Inverse Radius fit and an Exponentially-Modified Gaussian fit. The model results were simulated using the WRF and CAMx models for the year 2005, for a single point source outside Atlanta in Georgia, USA with specified emissions and three chemical scenarios: no chemical reactions, 12 h chemical lifetime and 1 h chemical lifetime. No other sources were included in the simulations. We find that the box model provides reliable estimates irrespective of plume speed and plume direction, if the plume speed and the chemical lifetime are known accurately. The 2D Gaussian fit was found to be sensitive to plume speed and direction, and requires omnidirectional dispersion in order to have a decent fit. However, the 2D Gaussian fit is only an approximate fit to the data, and the discrepancies mean that the results are dependent on the geographical domain used for the optimization. An Inverse Radius fit is introduced to correct this issue, which is found to provide improved emissions and lifetime estimates. The Exponentially-Modified Gaussian fit also gave improved estimates. It is however dependent on accurate plume rotation such that reported chemical lifetimes with this method could be significantly underestimated.