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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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Uncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties
Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration (N), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius (r eff ) and modulates the r eff -dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of r eff -dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD-dependent estimation of r eff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of r eff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m –2 depending on the assumed DSD shape, which is about 13% of the best observational estimate.
Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling
Abstract Field‐scale properties of fractured rocks play a crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion technique to infer two such parameters, fracture density and fractal dimension, from cross‐borehole thermal experiments data. It is based on a particle‐based heat‐transfer model, whose evaluation is accelerated with a deep neural network (DNN) surrogate that is integrated into a grid search. The DNN is trained on a small number of the heat‐transfer model runs and predicts the cumulative density function of the thermal field. The latter is used to compute fine posterior distributions of the (to be estimated) parameters. Our synthetic experiments reveal that fracture density is well constrained by data, while fractal dimension is harder to determine. Adding nonuniform prior information related to the DFN connectivity improves the inference of this parameter.
Matrix Approach to Land Carbon Cycle Modeling
Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin-up of land carbon cycle models by tens of times. The accelerated spin-up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever-increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.
Ultra-sensitive magnetic microscopy with an optically pumped magnetometer
Optically pumped magnetometers (OPMs) based on lasers and alkali-metal vapor cells are currently the most sensitive non-cryogenic magnetic field sensors. Many applications in neuroscience and other fields require high-resolution, high-sensitivity magnetic microscopic measurements. In order to meet this demand we combined a cm-size spin-exchange relaxation-free (SERF) OPM and flux guides (FGs) to realize an ultra-sensitive FG-OPM magnetic microscope. The FGs serve to transmit the target magnetic flux to the OPM thus improving both the resolution and sensitivity to small magnetic objects. We investigated the performance of the FG-OPM device using experimental and numerical methods, and demonstrated that an optimized device can achieve a unique combination of high resolution (80 μm) and high sensitivity (8.1 pT/). Additionally, we also performed numerical calculations of the magnetic field distribution in the FGs to estimate the magnetic noise originating from the domain fluctuations in the material of the FGs. We anticipate many applications of the FG-OPM device such as the detection of micro-biological magnetic fields; the detection of magnetic nano-particles; and non-destructive testing. From our theoretical estimate, an FG-OPM could detect the magnetic field of a single neuron, which would be an important milestone in neuroscience.
Extending empirical constraints on the SZ–mass scaling relation to higher redshifts via HST weak lensing measurements of nine clusters from the SPT-SZ survey at z ≳ 1
We present a Hubble Space Telescope (HST) weak gravitational lensing study of nine distant and massive galaxy clusters with redshifts 1.0 ≲ z ≲ 1.7 (z median = 1.4) and Sunyaev Zel’dovich (SZ) detection significance ξ > 6.0 from the South Pole Telescope Sunyaev Zel’dovich (SPT-SZ) survey. We measured weak lensing galaxy shapes in HST/ACS F606W and F814W images and used additional observations from HST/WFC3 in F110W and VLT/FORS2 in U HIGH to preferentially select background galaxies at z ≳ 1.8, achieving a high purity. We combined recent redshift estimates from the CANDELS/3D-HST and HUDF fields to infer an improved estimate of the source redshift distribution. We measured weak lensing masses by fitting the tangential reduced shear profiles with spherical Navarro-Frenk-White (NFW) models. We obtained the largest lensing mass in our sample for the cluster SPT-CL J2040—4451, thereby confirming earlier results that suggest a high lensing mass of this cluster compared to X-ray and SZ mass measurements. Combining our weak lensing mass constraints with results obtained by previous studies for lower redshift clusters, we extended the calibration of the scaling relation between the unbiased SZ detection significance ζ and the cluster mass for the SPT-SZ survey out to higher redshifts. We found that the mass scale inferred from our highest redshift bin (1.2 < z < 1.7) is consistent with an extrapolation of constraints derived from lower redshifts, albeit with large statistical uncertainties. Thus, our results show a similar tendency as found in previous studies, where the cluster mass scale derived from the weak lensing data is lower than the mass scale expected in a PlanckνΛCDM (i.e. νΛ cold dark matter) cosmology given the SPT-SZ cluster number counts.
Quality quandaries: Combining engineering and statistics to assess the probability of an event
Our article illustrates how to combine engineering and statistical models and propagate the distributions of model inputs to estimate the probability of an event with uncertainty.
The PAU Survey: narrow-band photometric redshifts using Gaussian processes
Here, we study the performance of the hybrid template machine learning photometric redshift (photo- z ) algorithm delight , which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey (PAUS). We calibrate the fluxes of the 40 PAUS narrow bands with six broad-band fluxes ( uBVriz ) in the Cosmic Evolution Survey (COSMOS) field using three different methods, including a new method that utilizes the correlation between the apparent size and overall flux of the galaxy. We use a rich set of empirically derived galaxy spectral templates as guides to train the Gaussian process, and we show that our results are competitive with other standard photometric redshift algorithms. delight achieves a photo- z 68th percentile error of σ 68 = 0.0081(1 + z ) without any quality cut for galaxies with i auto < 22.5 as compared to 0.0089(1 + z ) and 0.0202(1 + z ) for the bpz and annz 2 codes, respectively. delight is also shown to produce more accurate probability distribution functions for individual redshift estimates than bpz and annz 2. Common photo- z outliers of delight and bcnz 2 (previously applied to PAUS) are found to be primarily caused by outliers in the narrow-band fluxes, with a small number of cases potentially indicating spectroscopic redshift failures in the reference sample. In the process, we introduce performance metrics derived from the results of bcnz 2 and delight , allowing us to achieve a photo- z quality of σ 68 < 0.0035(1 + z ) at a magnitude of i auto < 22.5 while keeping 50 per cent objects of the galaxy sample.
Sunyaev-Zel'dovich Effect and X-ray Scaling Relations from Weak-Lensing Mass Calibration of 32 SPT Selected Galaxy Clusters
Uncertainty in mass-observable scaling relations is currently the limiting factor for galaxy-cluster-based cosmology. Weak gravitational lensing can provide direct mass calibration and reduce the mass uncertainty. We present new ground-based weak lensing observations of 19 South Pole Telescope (SPT) selected clusters at redshifts 0.29 <= z <= 0.61 and combine them with previously reported space-based observations of 13 galaxy clusters at redshifts 0.576 <= z <= 1.132 to constrain the cluster mass scaling relations with the Sunyaev-Zel'dovich effect (SZE), the cluster gas mass M-gas and Y-X, the product of M-gas and X-ray temperature. We extend a previously used framework for the analysis of scaling relations and cosmological constraints obtained from SPT-selected clusters to make use of weak lensing information. We introduce a new approach to estimate the effective average redshift distribution of background galaxies and quantify a number of systematic errors affecting the weak lensing modelling. These errors include a calibration of the bias incurred by fitting a Navarro-Frenk-White profile to the reduced shear using N-body simulations. We blind the analysis to avoid confirmation bias. We are able to limit the systematic uncertainties to 5.6% in cluster mass (68% confidence). Our constraints on the mass-X-ray observable scaling relation parameters are consistent with those obtained by earlier studies and our constraints for the mass-SZE scaling relation are consistent with the simulation-based prior used in the most recent SPT-SZ cosmology analysis. We can now replace the external mass calibration priors used in previous SPT-SZ cosmology studies with a direct, internal calibration obtained for the same clusters.
Stimulated emission tomography for efficient characterization of spatial entanglement
Stimulated-emission tomography (SET) is an excellent tool for characterizing the process of spontaneous parametric down conversion (SPDC), which is commonly used to create pairs of entangled photons for use in quantum information protocols. The use of stimulated emission increases the average number of detected photons by several orders of magnitude compared to the spontaneous process. In a SET measurement, the parametric down conversion is seeded by an intense signal field prepared with specified mode properties rather than by broadband multimodal vacuum fluctuations, as is the case for the spontaneous process. The SET process generates an intense idler field in a mode that is the complex conjugate to the signal mode. In this work we use SET to estimate the joint spatial mode distribution (JSMD) in the Laguerre-Gaussian (LG) basis of the two photons of an entangled photon pair. The pair is produced by parametric down conversion in a beta barium borate (BBO) crystal with type-II phase matching pumped at a wavelength of 405 nm along with a 780 nm seed signal beam prepared in a variety of LG modes to generate an 842 nm idler beam of which the spatial mode distribution is measured. We observe strong idler production and good agreement with the theoretical prediction of its spatial mode distribution. Our experimental procedure should enable the efficient determination of the photon-pair wavefunctions produced by low-brightness SPDC sources and the characterization of high-dimensional entangled-photon pairs. Published by the American Physical Society 2024
Temperature-Dependent Short-Circuit Capability of Silicon Carbide Power MOSFETs
Our paper presents a comprehensive short-circuit ruggedness evaluation and numerical investigation of up-to-date commercial silicon carbide (SiC) MOSFETs. The short-circuit capability of three types of commercial 1200-V SiC MOSFETs is tested under various conditions, with case temperatures from 25 to 200 degrees C and dc bus voltages from 400 to 750 V. It is found that the commercial SiC MOSFETs can withstand short-circuit current for only several microseconds with a dc bus voltage of 750 V and case temperature of 200 degrees C. Moreover, the experimental short-circuit behaviors are compared, and analyzed through numerical thermal dynamic simulation. Specifically, an electrothermal model is built to estimate the device internal temperature distribution, considering the temperature-dependent thermal properties of SiC material. Based on the temperature information, a leakage current model is derived to calculate the main leakage current components (i.e., thermal, diffusion, and avalanche generation currents). Finally, numerical results show that the short-circuit failure mechanisms of SiC MOSFETs can be thermal generation current induced thermal runaway or high-temperature-related gate oxide damage.
Doubling protected land area may be inefficient at preserving the extent of undeveloped land and could cause substantial regional shifts in land use
Abstract Projection of land use and land‐cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land‐use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well‐defined conservation goals while minimizing impacts to agriculture.
Study of spatiotemporal dynamics of a nanosecond atmospheric-pressure dielectric barrier discharge in millimeter-long air gaps
The spatiotemporal dynamics of a nanosecond atmospheric-pressure dielectric barrier discharge in 1- to 3-mm-long air gaps was studied experimentally. By using a segmented electrode, data on the time evolution of the discharge in different regions of the discharge gap were obtained. The uniformity of the discharge over the cross section is estimated by analyzing the spatial distribution of its glow.
A Review of Doses for Dental Imaging in 2010–2020 and Development of a Web Dose Calculator
Dental imaging is one of the most common types of diagnostic radiological procedures in modern medicine. We introduce a comprehensive table of organ doses received by patients in dental imaging procedures extracted from literature and a new web application to visualize the summarized dose information. We analyzed articles, published after 2010, from PubMed on organ and effective doses delivered by dental imaging procedures, including intraoral radiography, panoramic radiography, and cone-beam computed tomography (CBCT), and summarized doses by dosimetry method, machine model, patient age, and technical parameters. Mean effective doses delivered by intraoral, 1.32 (0.60–2.56) μSv, and panoramic, 17.93 (3.47–75.00) μSv, procedures were found to be about1% and 15% of that delivered by CBCT, 121.09 (17.10–392.20) μSv, respectively. In CBCT imaging, child phantoms received about 29% more effective dose than the adult phantoms received. The effective dose of a large field of view (FOV) (>150 cm2) was about 1.6 times greater than that of a small FOV (<50 cm2). The maximum CBCT effective dose with a large FOV for children, 392.2 μSv, was about 13% of theeffective dose that a person receives on average every year from natural radiation, 3110 μSv. Monte Carlo simulations of representative cases of the three dental imaging procedures were then conducted to estimate and visualize the dose distribution within the head. The user-friendly interactive web application (available at http://dentaldose.org) receives user input, such as the number of intraoral radiographs taken, and displays total organ and effective doses, dose distribution maps, and a comparison with other medical and natural sources of radiation. The web dose calculator provides a practical resource for patients interested in understanding the radiation doses delivered by dental imaging procedures.
Single-Logarithmic Corrections to Small-$x$ Helicity Evolution
The small- x x quark helicity evolution equations at double-logarithmic order, with the kernel \sim\alpha_s\ln^2(1/x) ∼ α s ln 2 ( 1 / x ) , have been derived previously. In this work, we derive the single-logarithmic corrections to the equations, to order \alpha_s\ln(1/x) α s ln ( 1 / x ) of the evolution kernel. The new equations include the effects of the running coupling and the unpolarized small- x x evolution, both of which are parametrically significant at single-logarithmic order. The large- N_c N c and large- N_c\ N_f N c N f approximations to the equation are computed. (Here, N_c N c and N_f N f are the numbers of quark colors and flavors, respectively.) Their solutions will provide more precise estimates of the quark helicity distribution at small x x , contributing to the resolution of the proton spin puzzle.
Photovoltaic Shading Testbed for Module-Level Power Electronics: 2016 Performance Data Update
The 2012 NREL report 'Photovoltaic Shading Testbed for Module-Level Power Electronics' provides a standard methodology for estimating the performance benefit of distributed power electronics under partial shading conditions. Since the release of the report, experiments have been conducted for a number of products and for different system configurations. Drawing from these experiences, updates to the test and analysis methods are recommended. Proposed changes in data processing have the benefit of reducing the sensitivity to measurement errors and weather variability, as well as bringing the updated performance score in line with measured and simulated values of the shade recovery benefit of distributed PV power electronics. Also, due to the emergence of new technologies including sub-module embedded power electronics, the shading method has been extended to include power electronics that operate at a finer granularity than the module level. An update to the method is proposed to account for these emerging technologies that respond to shading differently than module-level devices. The partial shading test remains a repeatable test procedure that attempts to simulate shading situations as would be experienced by typical residential or commercial rooftop photovoltaic (PV) systems. Performance data for multiple products tested using this method are discussed, based on equipment from Enphase, Solar Edge, Maxim Integrated and SMA. In general, the annual recovery of shading losses from the module-level electronics evaluated is 25-35%, with the major difference between different trials being related to the number of parallel strings in the test installation rather than differences between the equipment tested. Appendix D data has been added in this update.
2019 Cost of Wind Energy Review
This report uses representative utility-scale and distributed wind energy projects to estimate the levelized cost of energy (LCOE) for land-based and offshore wind power plants in the United States. Data and results detailed here are derived from 2019 commissioned plants and representative industry data as well as state-of-the-art modeling capabilities. Modeling is conducted to provide more granular detail on specific cost categories. This report represents the ninth annual installment and is intended to provide insight into current component-level costs as well as a basis for understanding variability in wind energy LCOE across the country.
Thoughts on Analyzing Neutron Multiplicity Data
Bayesian methods offer many advantages for analyzing neutron multiplicity data. Ideally one would make nonparametric estimates of the the posterior distributions, the probabilities - given the data - for unknowns to take on particular values. Once one has the posterior, uncertainty quantification is very natural. We describe how different ways of formulating the statistical theory of fission chains are mathematically equivalent. When confronted with actual data and the burden of finite statistics, different methods of expressing the theory offer advantages for understanding the underlying source parameters from an information theory standpoint.