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DOE OSTI2022

Crystal structure and magnetic properties in semiconducting Eu 3-δ Zn x Sn y As 3 with Eu-Eu dimers

Magnetic structure and crystal symmetry, which primarily determine the time-reversal and inversion symmetry, may give rise to numerous exotic quantum phenomena in magnetic semiconductors and semimetals when arranged in different patterns. Here, a new layered magnetic semiconductor, Eu 3-δ Zn x Sn y As 3 , was discovered and high-quality single crystals were grown using the Sn flux. According to structural characterization by x-ray diffraction and atomic-resolution scanning transmission electron microscopy, Eu 3-δ Zn x Sn y As 3 is found to crystallize in a hexagonal symmetry with the space group P6 3 /mmc (No. 194). After examining different specimens, we conclude that their stoichiometry is fixed at ~Eu 2.6 Zn 0.65 Sn 0.85 As 3 , which meets the chemical charge balance. Eu 3-δ Zn x Sn y As 3 is composed of septuple (Eu 1-δ Sn y As 2 )-Eu-(Zn x As)-Eu sequences. The shortest Eu–Eu distance in the system is between two Eu layers separated by Zn x As along the c-axis. Magnetization measurement shows an antiferromagnetic ordering in Eu 3-δ Zn x Sn y As 3 at T N ~ 12 K, where the magnetic easy-axis is along the c-axis, and Mössbauer spectroscopy observes magnetic hyperfine splitting on Eu and Sn at 6 K. Magnetic anisotropy is significantly different from the ones along the ab-plane in other layered Eu-based magnetic semimetals. Heat capacity measurements confirm the magnetic transition around 12 K. Electrical resistivity measurement indicates semiconductor behavior with a band gap of ~0.86 eV. Finally, various Eu-based magnetic semiconductors could provide a tunable platform to study potential topological and magnetic properties.

36 MATERIALS SCIENCE↗

NASA NTRS1999

Cloud 3D Effects Evidenced in Landsat Power Spectra and Autocorrelation Functions

An analysis of nadir reflectivity Fourier spatial power spectra and autocorrelation functions at solar wavelengths and for cloudy conditions has been carried out. The data come from Landsat Thematic Mapper (TM) observations, while Monte Carlo (MC) simulations are used to aid the interpretation of the Landsat results. We show that radiative processes produce consistent signatures on power spectra and autocorrelation functions. The former take a variety of forms not shown or explained in previous observational studies. We demonstrate that the TM spectra can potentially be affected by both radiative "roughening" at intermediate scales (approx. 1 -5 km), being more prevalent at large solar zenith angles, and the already documented radiative "smoothing" at small scales (less than 1 km). These processes are wavelength dependent, as shown by systematic differences between conservative (for cloud droplets) TM band 4 (approx. 0.8 microns) and absorbing band 7 (approx. 2.2 microns): band 7 exhibits more roughening and less smoothing. This is confirmed quantitatively by comparing least-squared fitted power spectral slopes for the two bands. It is also corroborated by a slower decrease with distance of autocorrelation function values for band 4 compared to band 7. The appearance of roughening at large solar zenith angles is a result of side illumination and shadowing and adds an additional complexity to the power spectra. MC spectra are useful in illustrating that scale invariant optical depth fields can produce complex power spectra that take a variety of shapes under different conditions. We show that radiative roughening increases with the decrease of single scattering albedo and with the increase of solar zenith angle (as in the observations). For high Sun there is also a clear shift of the radiative smoothing scale to smaller values as droplet absorption increases. The shape of the power spectrum is sensitive to the magnitude and type of cloud top height variability, with the spectral signatures of decorrelation between reflectance and optical depth at large scales becoming stronger as the magnitude of cloud top variations increase. Finally, the usefulness of power spectral analysis in evaluating the skill of novel optical depth retrieval techniques in removing 3D radiative effects is demonstrated. New techniques using inverse Non-local Independent Pixel Approximation (NIPA) and Normalized Difference of Nadir Reflectivity (NDNR) yield optical depth fields which better match the scale-by-scale variability of the true optical depth field.

Oreopoulos, Lazaros↗

DOE OSTI2024

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Mathematics↗

DOE OSTI2021

Quantifying Properties for a Mechanistic, Predictive Understanding of Aqueous Impact on Aging of Medium and Low Voltage AC and DC Cabling in Nuclear Power Plants

To address the gap in knowledge in understanding degradation in relevant service conditions, this project aimed to develop a mechanistic, predictive model of medium and low voltage cable failure based on the primary environmental degradation parameters of aqueous immersion time, temperature, and the oxidation extent. To do so, we took a two-fold approach toward evaluating degradation as related to the aqueous condition. First, we evaluated the chemical, mechanical, and electrical properties of polymers that comprise the cabling insulation under varied aqueous conditions at different temperatures. Secondly, we evaluated the performance of insulation materials under similar accelerated aging conditions in addition to the dielectric breakdown of the cabling under submersion conditions. Thermal oxidation and immersion of LDPE thin films were done using a Parr vessel and the extent of oxidation was monitored using the carbonyl index from ATR-FTIR spectrum, a ratio quantifying the carbonyl functionality produced on the LDPE film surface. Increasing the temperature, oxidation time, and oxygen pressure increased the water-vapor permeability and the carbonyl content. Thermogravimetric analysis of the films confirmed that there is indeed an initial weight loss of the smaller molecular weight molecules and volatiles. At higher temperatures, there is a secondary mass loss. Up to 70°C, the mass loss curves were similar until 80°C, when the aged films decreased at a much greater rate with respect to increasing temperature. This was seen in the 10, 50, 90% weight loss temperatures. The mechanical properties of sheet PP and HDPE as well as injection molded LDPE, HDPE, HDPP dog bones were plotted with respect to the time spent in the Parr vessel during thermal oxidation. For the sheet PP and HDPE, the UTS and the modulus of elasticity decreased while the elongation increased. The injection-molded LDPE, HDPE, and HDPP dog bones experienced a similar trend where the changes in the properties were within experimental uncertainty. This analysis of LDPE in dry and immersive oxygen-rich environments is an important baseline for future experiments looking at other degradation mechanisms. A predictive aging model for low-density polyethylene insulative cable housings was developed. This model revealed a combination of physical and chemical processes which lead to an increase in permeability and a decrease in strength of the insulator. In creating this predictive model, a gap of knowledge in the literature was revealed in two areas: understanding how the crystallinity of a polymer changes with time and visualizing the predicted pores formed through the insulator which leads to failure. Developing and using an adapted ATR-FTIR method, crystallinity was monitored and compared to bulk crystallinity found via DSC. Inhomogeneity in crystallinity changes were observed but the FTIR method but was found to be less reliable. Pore visualization was found utilizing electrical impedance spectroscopy saturated aged polyethylene films with synthesized citrate-capped gold nanoparticles, and chloroauric acid precursor. results indicate a decreasing impedance of the polyethylene films caused by the transport of ions through the film. SEM imaging was then performed for elemental analysis of the films and counter electrodes for the presence of ions. Chloride and gold ions were detected; however, no nanoparticles were found. This gives an estimated pore size of at least 0.3 nm through the aged film. Cyclic submergence of polyethylene and polypropylene in aqueous solutions of copper sulfate and Harrison’s solution were examined. It was discovered that aging in these mixed conditions and at 90°C did cause a small, yet significant increase to tensile strength for both unaged polyethylene and polypropylene. The PE and PP in cycled and submerged conditions did not have tensile strengths significantly different from dry-aged after 16 weeks. For both solutions, it was determined that capacitance increases with both water tree depth and cable temperature and is relatively independent of changing water tree AR. As for resistance, there is no apparent change between depth percentages of 10 and 80 for both solutions. This is because the water tree has not yet entered the conductor and thus there is no shorting current flowing through the tree yet. Between 80 and 100% water tree depth, it is observed that for both solutions there is a rapid decrease in resistance because the tree is now impacting the conductor shield and allowing shorting current to flow through it. The relationship between resistance and temperature was found to be inversed. As temperature increases, resistance decreases for both water tree solutions with distilled water having the most apparent difference across the range of temperatures simulated. With regards to water tree AR, resistance was found to be relatively independent at depth percentages of 10, 50, and 70, but there was some relationship at 100% depth for both solutions. At this depth, as water tree AR increased, resistance was found to also increase. Through the examination of voltage and electric field distribution plots, it was confirmed for EPR cables that increasing water tree depth leads to increasing distortion. The increase in shorting current at 100% depth for aqueous copper sulfate compared to distilled water was also visualized. Lastly, the relationship between shorting current and temperature for narrow water trees was visualized and validated using the distribution plots. This study has led to a better understanding of the effect of cable temperature, water tree solution, and geometry of the tree region on cable degradation. Furthermore, this study has documented simulation results of water tree degradation in EPR MV cables, which had been previously lacking in information. Future work in this area will add frequency into the mix and examine what effect it has on the rate of cable degradation as a result of water treeing. To compliment the work being carried by UMD on different polymer chemistries, harvested medium voltage (MV) cables were selected for accelerated aging study at ORNL. These medium voltage cables are representative of the vintages and materials that are currently in use at existing nuclear power plants (NPPs). These cables were immersed in water at 90°C and energized for a period of two years at elevated voltage. Partial discharge was measured during this period of time to track potential degradation. Unfortunately, due to the absence of failure, the extent of integration between the UMD techniques and the harvested MV insulation was limited. However, based on the findings and from the UMD research in the previous sections on polyethylene and polypropylene, follow-on characterization for harvested MV cable insulation should focus on sample preparation to take advantage of the UMD techniques to better understand mechanistic degradation in MV cable insulations. This would include: 1.) Utilization of solutions and impedance measurement insulation to track permeability in harvested and accelerated aging insulation samples, 2.) Adaptation of gold nanoparticle synthesis for electrical impedance spectroscopy with supporting SEM characterization to study pore formation in harvested and accelerated aged insulation samples, & 3.) ATR-FTIR crystallinity with multiple temperature dependent harvested MV cable insulation under different air and submerged accelerated aging. In addition, low voltage dielectric spectroscopy and high voltage dissipation factor, tan δ, could be implemented as a condition monitoring tool for harvested MV cable insulation in submerged environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Development of a Comparison Framework for Evaluating Environmental Contours of Extreme Sea States

Environmental contours of extreme sea states are often utilized for the purposes of reliability-based offshore design. Many methods have been proposed to estimate environmental contours of extreme sea states, including, but not limited to, the traditional inverse first-order reliability method (I-FORM) and subsequent modifications, copula methods, and Monte Carlo methods. These methods differ in terms of both the methodology selected for defining the joint distribution of sea state parameters and in the method used to construct the environmental contour from the joint distribution. It is often difficult to compare the results of proposed methods to determine which method should be used for a particular application or geographical region. The comparison of the predictions from various contour methods at a single site and across many sites is important to making environmental contours of extreme sea states useful in practice. The goal of this paper is to develop a comparison framework for evaluating methods for developing environmental contours of extreme sea states. This paper develops generalized metrics for comparing the performance of contour methods to one another across a collection of study sites, and applies these metrics and methods to develop conclusions about trends in the wave resource across geographic locations, as demonstrated for a pilot dataset. These proposed metrics and methods are intended to judge the environmental contours themselves relative to other contour methods, and are thus agnostic to a specific device, structure, or field of application. The metrics developed and applied in this paper include measures of predictive accuracy, physical validity, and aggregated temporal performance that can be used to both assess contour methods and provide recommendations for the use of certain methods in various geographical regions. The application and aggregation of the metrics proposed in this paper outline a comparison framework for environmental contour methods that can be applied to support design analysis workflows for offshore structures. This comparison framework could be extended in future work to include additional metrics of interest, potentially including those to address issues pertinent to a specific application area or analysis discipline, such as metrics related to structural response across contour methods or additional physics-based metrics based on wave dynamics.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Bounce-Averaged Hamiltonian for Charged Particles in an Axisymmetric but Nondipolar Model Magnetosphere

In order to facilitate bounce-averaged guiding center simulations of geomagnetically trapped particles, we express the kinetic energy of a particle with magnetic coordinates (L,phi) as an analytic function of the first two adiabatic invariants (M, J) and the L value of the field line. The magnetic field model is axisymmetric, consisting of a dipolar B field plus a uniform southward magnetic field parallel to the dipole moment mu(sub E). This model magnetosphere is surrounded by a circular equatorial neutral line whose radius b is an adjustable parameter. The L value of a field line is (by definition) inversely proportional to the flux enclosed by the corresponding magnetic shell of equatorial radius r(sub 0), and the L value at the neutral line (r(sub 0) = b) is denoted L*. The azimuthal coordinate phi measures magnetic local time. The best functional representation found for the normalized difference (L(exp 3)a(exp 3)/mu(sub E))(B(sub m) - B(sub 0)) between mirror-point field B(sub m) and equatorial field B(sub 0) along any field line is a 5-term expansion in powers (2/3 through 6/3) of the quantity X equivalent to (La/mu(sub E))(exp 1/2)K, where K equivalent to (J(exp 2)/8m(sub 0)M)(exp 1/2) is an adiabatically conserved quantity independent of particle energy, m(sub 0) is the rest mass of the particle, and a is the radius of the Earth. This functional form is motivated by results for limiting cases in which particles mirror very near and very far from the magnetic equator. Expansion coefficients corresponding to various powers of X are obtained from least squares fits to numerically computed results for X as a function of L and B(sub m). These are accurately expressible as fourth-order polynomials in (r(sub 0)/b)(exp 3), hence indirectly as functions of L/L* = 3La/2b. This representation, which leads (except for a manageably small region of parameter space) to better than 1% accuracy in the specification of B(sub m) as a function of K and L, allows bounce-averaged guiding center simulations to be performed without actually tracing the bounce motions of individual particles. Bounce-averaged drifts L' (meridional) and phi' (azimuthal) are proportional to derivatives of the Hamiltonian H (sum of kinetic and potential energies) with respect to phi and L, respectively. Our formulation thus provides a computationally efficient method for tracing the bounce-averaged adiabatic motion (conserving all three invariants) and nonadiabatic transport (violating the third invariant while conserving the first two invariants) of geomagnetically trapped particles in the model magnetosphere.

Schulz, Michael↗

NASA NTRS2011

Failure Modes in Capacitors When Tested Under a Time-Varying Stress

Steady step surge testing (SSST) is widely applied to screen out potential power-on failures in solid tantalum capacitors. The test simulates the power supply's on and off characteristics. Power-on failure has been the prevalent failure mechanism for solid tantalum capacitors for decoupling applications. On the other hand, the SSST can also be reviewed as an electrically destructive test under a time-varying stress. It consists of rapidly charging the capacitor with incremental voltage increases, through a low resistance in series, until the capacitor under test is electrically shorted. Highly accelerated life testing (HALT) is usually a time-efficient method for determining the failure mechanism in capacitors; however, a destructive test under a time-varying stress like SSST is even more effective. It normally takes days to complete a HALT test, but it only takes minutes for a time-varying stress test to produce failures. The advantage of incorporating specific time-varying stress into a statistical model is significant in providing an alternative life test method for quickly revealing the failure modes in capacitors. In this paper, a time-varying stress has been incorporated into the Weibull model to characterize the failure modes. The SSST circuit and transient conditions to correctly test the capacitors is discussed. Finally, the SSST was applied for testing polymer aluminum capacitors (PA capacitors), Ta capacitors, and multi-layer ceramic capacitors with both precious metal electrode (PME) and base-metal-electrodes (BME). It appears that testing results are directly associated to the dielectric layer breakdown in PA and Ta capacitors and are independent on the capacitor values, the way the capacitors being built, and the manufactures. The testing results also reveal that ceramic capacitors exhibit breakdown voltages more than 20 times the rated voltage, and the breakdown voltages are inverse proportional to the dielectric layer thickness. The possibility of ceramic capacitors in front-end decoupling applications to block the surge noise from a power supply is also discussed.

Liu, David (Donhang)↗

DOE OSTI2023

Effects of Size and Shape on the Tolerances for Misalignment and Probabilities for Successful Oriented Attachment of Nanoparticles

Oriented attachment (OA) of nanoparticles is an important pathway of crystal growth, but tools for quantitatively modeling OA are lacking. Here we present several simple models that relate the probability of achieving OA to basic geometric parameters such as particle size, shape, and lattice periodicity. A Moiré-domain model is applied to understand twist-misorientations between parallel surfaces, and it predicts that the range of twist angles yielding perfect OA is inversely related to the width of the contact area. This is confirmed and further developed using a surface functional model, which predicts how crystallographic registration forces drive the emergence of complex orientational energy landscapes. The energy landscapes are predicted to possess local minima that can trap particles in imperfect alignments, and these local minima become deeper and more numerous as the contact area increases, making OA more challenging for large particles. Further, a second set of models is presented to understand the sequence of events by which two crystallographic faces become co-planer after collision. We use a ‘central force approximation’ to quantitatively predict the odds of attaining coalignment between various faces when particles collide with random misalignments, and we show that in the absence of biasing forces, the probability of attaining alignment on a given face is roughly proportional to its solid angle as viewed from the center of the particle. The model predicts that OA is most favorable between well-faceted particles and becomes exceedingly unlikely for large spherical particles that express many microfacets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1997

Rendering Three-Dimensional Solar Coronal Structures

An X-ray or EUV image of the corona or chromosphere is a 2D representation of an extended 3D complex for which a general inversion process is impossible. A specific model must be incorporated in order to understand the full 3D structure. We approach this problem by modeling a set of optically-thin 3D plasma flux tubes which we render these as synthetic images. The resulting images allow the interpretation of the X-ray/EUV observations to obtain information on (1) the 3D structure of X-ray images, i.e., the geometric structure of the flux tubes, and on (2) the internal structure using specific plasma characteristics, i.e., the physical structure of the flux tubes. The data-analysis technique uses magnetograms to characterize photospheric magnetic fields and extrapolation techniques to form the field lines. Using a new set of software tools, we have generated 3D flux tube structures around these field lines and integrated the plasma emission along the line of sight to obtain a rendered image. A set of individual flux-tube images is selected by a non-negative least-squares technique to Provide a match with an observed X-ray image. The scheme minimizes the squares of the differences between the synthesized image and the observed image with a non-negative constraint on the coefficients of the brightness of the individual flux-tube loops. The derived images are used to determine the specific photospheric foot points and physical data, i.e., scaling laws for densities and loop lengths. The development has led to Computer efficient integration and display software that is compatible for comparison with observations (e.g., Yohkoh SXT data, NIXT, or EIT). This analysis is important in determining directly the magnetic field configuration, which provides the structure of coronal loops, and indirectly the electric currents or waves, which provide the energy for the heating of the plasma. We have used very simple assumptions (i.e., potential magnetic fields and isothermal corona) to provide an initial test of the techniques before complex models are introduced. We have separated the physical and geometric contributions of the emission for a set of flux tubes and concentrated, in this initial study, on the geometric contributions by making approximations to the physical contributions. The initial results are consistent with the scaling laws derived from Yohkoh SXT data.

Gary, G. Allen↗

NASA NTRS2006

Comprehensive Astronaut Immune Assessment Following a Short-Duration Space Flight

Immune system dysregulation has been demonstrated to occur during spaceflight and has the potential to cause serious health risks to crewmembers participating in exploration class missions. As a part of an ongoing NASA flight experiment assessing viral immunity (DSO-500), a generalized immune assessment was performed on 3 crewmembers who participated in the recent STS-114 Space Shuttle mission. The following assays were performed: (1) comprehensive immunophenotype analysis; (2) T cell function/intracellular cytokine profiles; (4) secreted Th1/Th2 cytokine profiles via cytometric bead array. Immunophenotype analysis included a leukocyte differential, lymphocyte subsets, T cell subsets, cytotoxic/effector CD8+ T cells, memory/naive T cell subsets and constitutively activated T cells. Study timepoints were L-180, L-65, L-10, R+0, R+3 and R+14. Detailed data are presented in the poster text. As expected from a limited number of human subjects, data tended to vary with respect to most parameters. Specific post-flight alterations were as follows (subject number in parentheses): Granulocytosis (2/3), reduced NK cells (3/3), elevated CD4/CD8 ratio (3/3), general CD8+ phenotype shift to a less differentiated phenotype (3/3), elevated levels of memory CD4+ T cells (3/3), loss of L-selectin on T cell subsets (3/3), increased levels of activated T cells (2/3), reduced IL-2 producing T cell subsets (3/3), levels of IFNg producing T cells were unchanged. CD8+ T cell expression of the CD69 activation markers following whole blood stimulation with SEA+SEB were dramatically reduced postflight (3/3), whereas other T cell function assessments were largely unchanged. Cytometric bead array assessment of secreted T cell cytokines was performed, following whole blood stimulation with either CD3/CD28 antibodies or PMA+ionomycin for 48 hours. Specific cytokines assessed were IFNg, TNFa, IL-2, IL-4, IL-5, IL-10. Following CD3/CD28 stimulation, all three crewmembers had a mission-associated reduction in the levels of secreted IFNg. One crewmember had a post-flight inversion in the IFNg/IL-10 ratio postflight, which trended back to baseline by R+14. Detailed cytokine data are presented in the poster text. This testing regimen was designed to correlate immunophenotype changes (thought to correspond to specific in-vivo immune responses or pathogenesis), against altered leukocyte function and cytokine profiles. In-flight studies are required to determine if post-flight alterations are reflective of the in-flight condition, or are a response to landing and readaptation.

Crucian, Brian↗

DOE OSTI2022

Understanding the Surface Structure and Catalytic Activity of SnO x /Au(111) Inverse Catalysts for CO 2 and H 2 Activation

Carbon dioxide hydrogenation is a promising approach for the reduction of greenhouse gas pollution via the production of fuels and high-value chemicals utilizing C1 chemistry. In this process, the activation of nonpolar molecules, CO 2 and H 2 , at mild conditions is challenging. In this study, we report a well-defined inverse SnO x /Au(111) catalyst that shows the ability to activate both CO 2 and H 2 at room temperature. Scanning tunneling microscopy (STM) and ambient pressure X-ray photoemission spectroscopy (AP-XPS) are combined to understand the surface structure, growth mode, chemical state, and activity of SnO x /Au(111) surfaces. Nanostructures of SnO x at the sub-monolayer level were prepared by depositing Sn on Au(111) followed by O 2 oxidation. For the as-prepared SnO x /Au(111), two-dimensionally formed SnO x thin films on a Au(111) substrate were observed with STM of two different moieties, discernible based on their height: clusters (~0.4 Å) and nanoparticles (NPs, 1–2.5 Å), which are assigned to Sn–Au alloys and SnO x , respectively, in corroboration with XPS analysis. Furthermore, SnO x /Au(111) was annealed under UHV to test its thermal stability. Upon annealing at 400–600 K, a disappearance of SnO x NPs and reappearance of highly dispersed Sn clusters were clearly noticeable from the STM and XPS results, identifying the thermal decomposition of SnO x and subsequent formation of Sn–Au alloys on the surface due to the recombination of Sn clusters with Au. We investigated the reactivity of the SnO x /Au(111) surfaces toward CH 4 , CO 2 , and H 2 . The SnO x /Au(111) surfaces have excellent CO 2 and H 2 activation abilities even at room temperature with negligible reactivity for methane activation. Our AP-XPS results show that H 2 can be activated on the SnO x NPs by the reduction to Sn. For CO 2 , the activation and further dissociation are identified by a reoxidation of Sn with newly formed Sn–O bonds and the formation of surface carbon. Therefore, we propose that SnO x is a potential catalyst or additive to achieve CO 2 hydrogenation under mild conditions.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1998

Combined Numerical/Analytical Perturbation Solutions of the Navier-Stokes Equations for Aerodynamic Ejector/Mixer Nozzle Flows

In spite of rapid advances in both scalar and parallel computational tools, the large number of variables involved in both design and inverse problems make the use of sophisticated fluid flow models impractical, With this restriction, it is concluded that an important family of methods for mathematical/computational development are reduced or approximate fluid flow models. In this study a combined perturbation/numerical modeling methodology is developed which provides a rigorously derived family of solutions. The mathematical model is computationally more efficient than classical boundary layer but provides important two-dimensional information not available using quasi-1-d approaches. An additional strength of the current methodology is its ability to locally predict static pressure fields in a manner analogous to more sophisticated parabolized Navier Stokes (PNS) formulations. To resolve singular behavior, the model utilizes classical analytical solution techniques. Hence, analytical methods have been combined with efficient numerical methods to yield an efficient hybrid fluid flow model. In particular, the main objective of this research has been to develop a system of analytical and numerical ejector/mixer nozzle models, which require minimal empirical input. A computer code, DREA Differential Reduced Ejector/mixer Analysis has been developed with the ability to run sufficiently fast so that it may be used either as a subroutine or called by an design optimization routine. Models are of direct use to the High Speed Civil Transport Program (a joint government/industry project seeking to develop an economically.viable U.S. commercial supersonic transport vehicle) and are currently being adopted by both NASA and industry. Experimental validation of these models is provided by comparison to results obtained from open literature and Limited Exclusive Right Distribution (LERD) sources, as well as dedicated experiments performed at Texas A&M. These experiments have been performed using a hydraulic/gas flow analog. Results of comparisons of DREA computations with experimental data, which include entrainment, thrust, and local profile information, are overall good. Computational time studies indicate that DREA provides considerably more information at a lower computational cost than contemporary ejector nozzle design models. Finally. physical limitations of the method, deviations from experimental data, potential improvements and alternative formulations are described. This report represents closure to the NASA Graduate Researchers Program. Versions of the DREA code and a user's guide may be obtained from the NASA Lewis Research Center.

DeChant, Lawrence Justin↗

NASA NTRSDate not supplied

Radiative Flux Measurements from ARISE: A Comparison with CERES Top-of-Atmosphere Radiative Fluxes

Uncertainty in top-of-atmosphere (TOA) radiation fluxes observations are larger in the Arctic than in other regions. These uncertainties are due to the low sun angles and the highly reflective, anisotropic, and heterogeneous surface conditions. Quantifying, attributing, and reducing Arctic TOA radiative flux uncertainty enables a better understanding of the rapidly changing Arctic. To advance this goal, we compare the Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) campaign measurements collected in September 2014. We compare CERES TOA and aircraft radiative flux measurements using two complementary approaches: grid box average fluxes and instantaneously matched footprints. The grid box mean flux comparison indicates an agreement between CERES and aircraft measurements within 2 uncertainty (calibration and inversion) in the longwave for all five grid boxes and for four-of-five grid boxes in the shortwave; shortwave and longwave mean differences are -7.9 and +2.3 Wm‑2, respectively. The comparison of 36 instantaneously matched footprints with aircraft measurements reveals mean differences of -10.5 and 0.4 Wm‑2 in the shortwave and longwave, respectively. To further explore the persistent negative difference in the shortwave, we further quantify the effects of temporal and spatial sampling differences, angular distribution models, and scene identification to CERES-aircraft differences. Our analysis indicates that sampling differences (including scene evolution) account for an additional 1.8 and 1.7% uncertainty in the shortwave and longwave, respectively and indicates no bias. After accounting for this sampling uncertainty, all CERES-aircraft grid box mean fluxes agree within 2 uncertainty. Scene identification errors due to sea ice concentration data set differences exhibit no bias in the shortwave flux difference and indicate the possibility of substantial differences in the CERES fluxes in specific cases with large spatial heterogeneity. Considering the instantaneously matched footprints, we find that the angular distribution models account may account for up to ‑7.3 Wm-2 of the persistent CERES-aircraft shortwave flux difference due to systematic differences in the anisotropy for sea ice partly cloudy scenes. Additional analysis using a special scan model with the CERES FM2 instrument suggests a significant view zenith angle dependence of the CERES fluxes for sea ice partly cloudy scenes where shortwave fluxes systematically decrease with increasing view zenith angle; no dependence is found for other scene types. We conclude that (1) spatial heterogeneity and scene temporal evolution substantially limit our ability to use aircraft measurements to place strong constraints on CERES TOA fluxes and (2) that the representation of anisotropy in sea ice partly cloudy scenes is a significant factor contributing to the persistent negative CERES-aircraft shortwave flux difference in this comparison and require additional data to analysis fully quantify the potential bias.

Patrick C Taylor↗

NASA NTRSDate not supplied

A comparison between CERES TOA Radiative Fluxes and Airborne Radiative Flux Measurements from ARISE

Uncertainty in top-of-atmosphere (TOA) radiation fluxes observations are larger in the Arctic than in other regions. These uncertainties are due to the low sun angles and the highly reflective, anisotropic, and heterogeneous surface conditions. Quantifying, attributing, and reducing Arctic TOA radiative flux uncertainty enables a better understanding of the rapidly changing Arctic. To advance this goal, we compare the Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) campaign measurements collected in September 2014. We compare CERES TOA and aircraft radiative flux measurements using two complementary approaches: grid box average fluxes and instantaneously matched footprints. The grid box mean flux comparison indicates an agreement between CERES and aircraft measurements within 2σ uncertainty (calibration and inversion) in the longwave for all five grid boxes and for four-of-five grid boxes in the shortwave; shortwave and longwave mean differences are -7.9 and +2.3 Wm 2, respectively. The comparison of 36 40 instantaneously matched footprints with aircraft measurements reveals mean differences of -120.25 and -1.00.4 Wm 2 in the shortwave and longwave, respectively. To further explore the persistent negative difference in the shortwave, Wwe further quantify the effects of temporal and spatial sampling differences, angular distribution models, and scene identification to CERES-aircraft differences. Our analysis indicates that sampling differences (including scene evolution) account for an additional 1.8 and 1.7% uncertainty in the shortwave and longwave, respectively and , but indicates no bias. After accounting for this sampling uncertainty, all CERES-aircraft grid box mean fluxes agree within 2σ uncertainty. Scene identification errors due to sea ice concentration data set differences exhibit no bias in the shortwave flux difference and indicate the possibility of substantial differences in the CERES fluxes in specific cases with large spatial heterogeneity. Considering the instantaneously matched footprints, we find that the angular distribution models account may account for up to 7.3 Wm-2 of the persistent CERES-aircraft shortwave flux difference due to systematic differences in the anisotropy for sea ice partly cloudy scenes. Additional analysis using a special programmable scan model with the CERES FM2 instrument suggests a significant view zenith angle dependence of the CERES fluxes for sea ice partly cloudy scenes where shortwave fluxes systematically decrease with increasing view zenith angle; no dependence is found for other scene types. We conclude that (1) spatial heterogeneity and scene temporal evolution substantially limit our ability to use aircraft measurements to place strong constraints on CERES TOA fluxes and (2) that the representation of anisotropy in sea ice partly cloudy scenes is likely a significant factor contributing to the persistent negative CERES-aircraft shortwave flux difference in this comparison and require additional data to analysis fully quantify the potential bias.

Patrick C. Taylor↗

DOE OSTI2023

Simultaneous determination of electrical conductivity and thickness of nonmagnetic metallic foils using two-sided multifrequency eddy current techniques

Here, in this paper, we describe a simple method for performing multifrequency eddy current characterization of free-standing uniform-thickness metallic foils using a forked inductive coil arrangement. The method involves measuring the mutual inductance between two coils when a foil is present between the coils, and when it is not present; the ratio of these mutual inductances is compared with an analytical solution, and foil conductivity, thickness, and sheet resistance are simultaneously estimated using numerical inversion and least-squares fitting. This method was used to characterize 34 non-ferrous metallic samples with thicknesses between 50 and 640 μm and with conductivities between 0.8 × 10 7 and 5.8 × 10 7 S/m. The estimated thicknesses from eddy current characterization agreed well with those measured using confocal optical techniques; the two approaches agreed to within 1 μm for samples that were thinner than 200 μm, and to within 0.5% for samples that had a thickness of 200 μm or greater. The estimated conductivities from eddy current characterization were in close agreement with expected values, given knowledge of the materials used. A particular strength of this approach is that the instrumentation needed is broadly available in research and development laboratories and the associated fixturing is easy to manufacture and assemble. A calibration procedure is described that can be used to reduce errors from geometric uncertainties. This calibration requires a sample that has only a known conductivity or thickness; both do not need to be known. The method described herein is likely extensible to conductivities and thickness well outside the ranges measured as part of this work.

47 OTHER INSTRUMENTATION↗