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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Laser Powder Bed Fusion Additive Manufacture Nb1Zr Development

Next generation fission and fusion nuclear reactors require materials that can withstand operating temperatures greater than 500 °C, neutron irradiation doses of up to 200 displacements per atom (dpa), and potentially corrosive coolants such as the alkali liquid metals sodium, lithium, and NaK (Na33K eutectic alloy). Refractory alloys, such as Nb1Zr (Nb-1wt%Zr) and Molybdenum alloy TZM (Mo-0.5wt%Ti-0.08wt%Zr) have been traditionally considered viable candidates for advanced fission and fusion reactor concepts. However, it is relatively difficult to generate complex geometries of interest from these alloys using traditional manufacturing methods. In addition, there needs to be a concentrated effort to address refractory metal challenges at elevated temperature operation. In order to generate complex geometries of interest, modern manufacturing techniques are considered to increase the technological readiness level (TRL), cost-effectiveness, and schedule savings. This work focused on the continued development of laser powder bed fusion (L-PBF) additive manufacturing (AM) to improve both design flexibility, evaluate microstructure and properties, and ultimately accelerate the TRL and qualification of these processes and alloys for components to potentially be put into service. Niobium alloy Nb1Zr was identified through a down-selection process outlined in previous reports as a candidate to develop in L-PBF AM. Historically, Nb1Zr had been explored for high temperature fast spectrum fission reactors for both terrestrial and space applications. Molybdenum alloy TZM has also been considered for these reactor concepts due to exceptional high-temperature strength, creep resistance, and stability under irradiation. L-PBF AM of TZM has previously been investigated at LANL under the Microreactor program, NASA, ORNL, and in academia. However, due to the crack prone nature of TZM, L-PBF AM of TZM resulted in significant microcracking and additional development is required to pursue viable maturation. Other AM methods have been found to be more successful in printing TZM, and those alternatives approaches are discussed in this effort. The efforts detailed in this report focused on continued development of Nb1Zr through L-PBF and development of TZM via L-PBF and electron powder bed fusion (E-PBF). The objective of this work was to further the development of these AM techniques for the chosen refractory alloys, elucidating and addressing associated challenges through characterization of several demonstration builds. At LANL, Nb1Zr builds were completed using an EOS M290 and M400 machines, and a refractory alloy-dedicated L-PBF system, the Xact Metal XM200G, was installed. The XM200G primary purpose was to do the Nb1Zr parameter development process; however, due to difficulties associated with the machine installation and qualification process, it was decided to pivot development to the larger M400 and M290 machines. Although the supply of Nb1Zr powder was limited, it was sufficient to generate sub-scale metallographic specimens for the purpose of parameter development. This was first accomplished on the EOS M400 then the M290 due to machine schedule availability. Further development of TZM has been initiated at the University of Texas El Paso (UTEP) under contract with LANL to use both a heated build envelope L-PBF machine and E-PBF machine that have been found in the literature to mitigate microcracking. UTEP was provided with TZM powder and build plates to support parallel TZM parameter development across both machines. As part of the contract, UTEP will also be conducting microstructural characterization once optimized process parameters have been identified. The optimized process parameters for each machine will be used to generate a series of metallographic, mechanical, and surface finish specimens for subsequent characterization and testing. In the next section, we provide a detailed discussion of the methodology used for investigating the feasibility of leveraging these alloys for use in advanced reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reactive burn model calibration using high-throughput initiation experiments at sub-millimeter length scales

We report a first-of-its-kind model calibration was performed using Sandia National Laboratories’ high-throughput initiation (HTI) experiment for two types of vapor-deposited explosive films consisting of hexanitrostilbene (HNS) or pentaerythritol tetranitrate (PETN). These films exhibit prompt initiation, and they reach steady detonation at sub-millimeter length scales. Following prior work on HNS, we test the hypothesis of approximating these explosive films as fine-grained homogeneous solids with simple Arrhenius kinetics burn models. The model calibration process is described herein using a single-step as well as a two-step Arrhenius rate law, and it consists of systematic parameter sampling leading to a reduction in the model degrees of freedom. Multiple local minima are observed; results are given for seven different optimized parameter sets. Each model set is further evaluated in a two-dimensional simulation of the critical failure thickness for a sustained detonation. Overall, the two-step Arrhenius kinetics model captures the observed behavior for HNS; however, neither model produces a good fit to the PETN data. We hypothesize that the HTI results for PETN correspond to a heterogeneous response, owing to the smaller reaction zone of PETN compared to HNS (i.e., it does not homogenize the fine-grained hot spots as well). Future work should consider using the ignition and growth model for PETN, as well as other reactive burn models such as xHVRB, AWSD, PiSURF, and CREST.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

17 WIND ENERGY↗
DOE OSTI2022

Phase transitions in Zr at sub-nanosecond time scales

Here solid-solid phase transitions are investigated in Zr thin films using shock compression induced by a short laser pulse (<1 ns). Shock wave profiles are measured at free surfaces for films of different thicknesses (a few micrometers) using chirped-pulse line velocimetry with 10 ps time resolution. Experiments are performed at pressures up to ~50 GPa, which is sufficient to reach the ω and β phases under equilibrium conditions. The shock wave structures are analyzed using a general Lagrangian analysis method, which allows for the calculation of stress–strain paths and assessments of phase transition behavior. In agreement with recent short laser pulse experiments using ultra-fast x-ray diffraction, we do not find any clear evidence of the α–ω transition, though this would be expected from the phase diagram. Instead, we infer a direct transformation to a metastable β-phase at lower shock pressures (<20 GPa) and equilibrium β at higher pressures. Through the velocimetry analysis, we find α–β transformation onset times of less than ~100 ps and completion times of less than ~200 ps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Xenon implantation of nanodiamond films for targeted color center emission at sub-nanosecond time scales

In this work, the lifetime of nitrogen-vacancy color centers within nanodiamonds is reduced from 550±13 ps to 297±10 ps through the implantation of xenon. Coupled-mode analysis is employed to characterize the mechanism responsible for the reduction in emission lifetime. The observed spectral lineshape is found to be consistent with a Voigt profile consisting of two Lorentzian resonant peaks at 637 nm and 811 nm that are inhomogeneously broadened by a Gaussian distribution. A convolution of the frequency-domain Lorentzian output, with linewidths less than 1 nm, from the coupled-mode system of equations with a Gaussian with standard deviation of 85 nm is performed to generate the Voigt profile. The shortened emission lifetime is found to be consistent with a coupled mode theory model incorporating coupling between nitrogen-vacancy and xenon-vacancy color centers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2020

Radar Super Resolution using a Deep Convolutional Neural Network

Super-resolution involves synthetically increasing the resolution of gridded data beyond its native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large scale precipitation features and the associated sub-pixel scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6-months of reflectivity observations from the Langley Hill WA (KLGX) radar, and we find that it substantially outperforms common interpolation schemes for x4 and x8 resolution increases based on several objective error and perceptual quality metrics.

radar, machine learning, super resolution, Remote ↗
DOE OSTI2022

Measuring and directing charge transfer in heterogenous catalysts

Abstract Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance the stability, activity, and selectivity of heterogeneous catalysts. While charge transfer is tunable using the atomic structure and chemistry of the catalyst-support interface, direct experimental evidence is missing for three-dimensional catalyst nanoparticles, primarily due to the lack of a high-resolution method that can probe and correlate both the charge distribution and atomic structure of catalyst/support interfaces in these structures. We demonstrate a robust scanning transmission electron microscopy (STEM) method that simultaneously visualizes the atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts using a model Au-catalyst/SrTiO 3 -support system. Using this method, we further reveal the atomic-scale mechanisms responsible for the highly active perimeter sites and demonstrate that the charge transfer behavior can be readily controlled using post-synthesis treatments. This methodology provides a blueprint for better understanding the role of charge transfer in catalyst stability and performance and facilitates the future development of highly active advanced catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Large eddy simulation of atmospheric boundary layer flow over complex terrain in comparison with RANS simulation and on-site measurements under neutral stability condition

Large eddy simulation (LES) of the atmospheric boundary layer (ABL) flow over complex terrain is presented with a validation using meteorological tower (met-tower) data through an improved neutral stability sampling approach. The proposed stability sampling procedure includes a condition based on the most-likely occurrence time-periods of the neutral ABL and reduces the variabilities of the conditional wind statistics calculated at the met-towers in comparison to our previous work. The ABL flow simulations are carried out over a potential wind site with a prominent hill based using the OpenFOAM-based simulator for on/off-shore wind farm applications by applying the Lagrangian-averaged scale-invariant dynamic sub-grid scale turbulence model. A low-dissipative scale-selective discretization scheme for the non-linear convection term in the LES governing equation is adopted implicitly to ensure both the second-order accuracy and bounded solution. The LES inflow is generated through a precursor method with a “tiling” approach based on the flow driving parameters obtained from a corresponding Reynolds-averaged Navier–Stokes (RANS) simulation. Overall, the averaged wind velocity profiles predicted by the LES approach at all met-tower locations show a similar tendency as the RANS results, which are also in reasonable agreement with the met-tower data. An obvious difference in wind speed standard deviation profiles is seen between LES and RANS, especially at regions downstream of the hill edge, where the LES shows under-predicted results at the highest measurement levels in comparison to the tower data. The computational costs of the LES are found to be about 20 times higher than the RANS simulations.

Energy & Fuels↗
DOE OSTI2022

Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar

Model parameters are a major source of uncertainty in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the most sensitive parameters has the potential to improve solar irradiance predictions. Here, we adopted a multiobjective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California, in order to focus on parameter calibration under cloudy conditions with different aerosol loading. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE. Our investigation reveals, however, that optimizing TE14 has a limited impact on irradiance simulations under less-polluted conditions in the SGP.

14 SOLAR ENERGY↗
DOE OSTI2023

Orographic Cirrus and Its Radiative Forcing in NCAR CAM6

Cirrus clouds play an important role in the Earth's radiative energy budget, thereby affecting the climate state and climate change. Orographic gravity wave (OGW)-induced sub-grid scale vertical velocity (i.e., cooling rate) is not resolved by large-scale models and its impact on ice formation in cirrus clouds is not well quantified. In this study, one sub-grid scale OGW scheme (e.g., McFarlane) is used in the Community Atmosphere Model version 6 (CAM6) to generate vertical velocity variance (σw) for cirrus formation. Results from the default model and simulations with the OGW-induced σw are evaluated against the DOE ARM Small Particles in Cirrus (SPARTICUS) campaign observations. The OGW based on the McFarlane scheme increases the sub-grid scale σw over mountains compared to the default model and improves the model agreement with the SPARTICUS observations. Larger σ w due to OGWs can trigger more frequent homogeneous nucleation in orographic cirrus and generates a higher number concentration of ice crystals observed during the SPARTICUS campaign. Moreover, our evaluation of the model simulations against satellite observations indicates that the McFarlane scheme generates high in-cloud ice number concentrations (>200 L -1 ) in the upper troposphere over mountains and high plateaus at mid- and high-latitudes of the winter hemisphere as shown in the observations. More ice crystals with smaller sizes absorb more infrared radiation (+0.523 ± 0.125 W m -2 ). The net radiative cloud forcing change at the top of the atmosphere is +0.330 W m -2 due to the orographic cirrus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anomalies in the topology of the temperature fluctuations in the cosmic microwave background: An analysis of the NPIPE and FFP10 data releases

We present a topological analysis of the temperature fluctuation maps from the Planck 2020 Data Release 4 NPIPE dataset and the Planck 2018 Data Release 3 FFP10 dataset. We performed a multiscale analysis in terms of the homology characteristics of the maps, invoking relative homology to account for the analysis in the presence of masks. We performed our analysis for a range of smoothing scales spanning sub- and super-horizon scales corresponding to a full width at half maximum (FWHM) of 5',10',20',40',80',160',320', and 640', and employed simulations based on the standard model for comparison, which assumes the initial fluctuation field to be an isotropic and homogeneous Gaussian random field. Examining the behavior of topological components, represented by the 0D homology group, we find the observations to be approximately 2σ or less deviant from the simulations for all resolutions and scales for the NPIPE dataset. For the FFP10 dataset, we detect a 2.96σ deviation between the observations and simulations at N = 128, FWHM = 80'. For the topological loops, represented by the first homology group, the simulations and observations are consistent within 2σ for most resolutions and scales for both the datasets. However, for the NPIPE dataset, we observe a high deviation between the observation and simulations in the number of loops at FWHM = 320', but at a low dimensionless threshold ν = –2.5. Under a Gaussian assumption, this would amount to a deviation of ~4σ. However, the distribution in this bin is manifestly non-Gaussian and does not obey Poisson statistics either. In the absence of a true theoretical understanding, we simply note that the significance is higher than what may be resolved by 600 simulations, yielding an empirical p-value of at most 0.0016. Specifically in this case, our tests indicate that the numbers arise from a statistically stable regime, despite being based on small numbers. For the FFP10 dataset, the differences are not as strong as for the NPIPE dataset, indicating a 2.77σ deviation at this resolution and threshold. The Euler characteristic, which is the alternating sum of the ranks of relative homology groups, reflects the deviations in the components and loops. To assess the significance of combined levels for a given scale, we employed the empirical and theoretical versions of the χ 2 test as well as the nonparametric Tukey depth test. Although all statistics exhibit a stable distribution, we favor the empirical version of the χ 2 test in the final interpretation, as it indicates the most conservative differences. For the NPIPE dataset, we find that the components and loops differ at more than 95%, but agree within the 99% confidence level with respect to the base model at N = 32, FWHM = 320'. The Euler characteristic at this resolution displays a per mil deviation. In contrast, the FFP10 dataset shows that the observations are consistent with the base model within the 95% confidence level, at this and smaller scales. This is consistent with the observations of the Planck analysis pipeline via Minkowski functionals. For the largest smoothing scale, N = 16, FWHM = 640', both datasets exhibit an anomalous behavior of the loops, where FFP10 data exhibit a deviation that is larger by an order of magnitude than that of the NPIPE dataset. In contrast, the values for the topological components and the Euler characteristic agree between observations and model to within a confidence level of 99%. However, for the largest scales, the statistics are based on low numbers and may have to be regarded with caution. Even though both datasets exhibit mild to significant discrepancies, they also exhibit contrasting behaviors at various instances. Therefore, we do not find it feasible to convincingly accept or reject the null hypothesis. Disregarding the large-scale anomalies that persist at similar scales in WMAP and Planck, observations of the cosmic microwave background are largely consistent with the standard cosmological model within 2σ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M 2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nanoscale optical nonreciprocity with nonlinear metasurfaces

Optical nonreciprocity is manifested as a difference in the transmission of light for the opposite directions of excitation. Nonreciprocal optics is traditionally realized with relatively bulky components such as optical isolators based on the Faraday rotation, hindering the miniaturization and integration of optical systems. Here we demonstrate free-space nonreciprocal transmission through a metasurface comprised of a two-dimensional array of nanoresonators made of silicon hybridized with vanadium dioxide (VO 2 ). This effect arises from the magneto-electric coupling between Mie modes supported by the resonator. Nonreciprocal response of the nanoresonators occurs without the need for external bias; instead, reciprocity is broken by the incident light triggering the VO 2 phase transition for only one direction of incidence. Nonreciprocal transmission is broadband covering over 100 nm in the telecommunication range in the vicinity of λ = 1.5 µm. Each nanoresonator unit cell occupies only ~0.1 λ 3 in volume, with the metasurface thickness measuring about half-a-micron. Our self-biased nanoresonators exhibit nonreciprocity down to very low levels of intensity on the order of 150 W/cm 2 or a µW per nanoresonator. We estimate picosecond-scale transmission fall times and sub-microsecond scale transmission rise. Our demonstration brings low-power, broadband and bias-free optical nonreciprocity to the nanoscale.

42 ENGINEERING↗
DOE OSTI2022

Dissipative Floquet Dynamics: from Steady State to Measurement Induced Criticality in Trapped-ion Chains

Quantum systems evolving unitarily and subject to quantum measurements exhibit various types of non-equilibrium phase transitions, arising from the competition between unitary evolution and measurements. Dissipative phase transitions in steady states of time-independent Liouvillians and measurement induced phase transitions at the level of quantum trajectories are two primary examples of such transitions. Investigating a many-body spin system subject to periodic resetting measurements, we argue that many-body dissipative Floquet dynamics provides a natural framework to analyze both types of transitions. We show that a dissipative phase transition between a ferromagnetic ordered phase and a paramagnetic disordered phase emerges for long-range systems as a function of measurement probabilities. A measurement induced transition of the entanglement entropy between volume law scaling and sub-volume law scaling is also present, and is distinct from the ordering transition. The two phases correspond to an error-correcting and a quantum-Zeno regimes, respectively. The ferromagnetic phase is lost for short range interactions, while the volume law phase of the entanglement is enhanced. An analysis of multifractal properties of wave function in Hilbert space provides a common perspective on both types of transitions in the system. Our findings are immediately relevant to trapped ion experiments, for which we detail a blueprint proposal based on currently available platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models

Abstract Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mass‐Conserving Downscaling of Climate Model Precipitation Over Mountainous Terrain for Water Resource Applications

Abstract A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined.

Geology↗
DOE OSTI2023

Spectrum of kinetic-Alfvén-wave turbulence: intermittency or tearing mediation?

We investigate the spectral properties of the electromagnetic fluctuations of sub-ion scale turbulence in weakly collisional, low-beta plasmas using a two-field isothermal gyrofluid model. The numerical results strongly support a description of the turbulence as a critically balanced Kolmogorov-like cascade of kinetic Alfvén wave fluctuations, as amended by previous studies to include intermittency effects. The measured universal index of the energy spectra from systems with different flux-unfreezing mechanisms excludes the role of tearing mediation in determining the spectra. The fluctuations remain isotropic in the plane perpendicular to the strong background magnetic fields as they cascade to smaller scales, which explains the absence of tearing mediation. The calculation of high-order, multipoint structure functions of magnetic fluctuations suggests that the intermittent structures have a quasi-2D, sheet-type morphology. In conclusion, these results are useful for explaining recent observations of the spectrum and structure of magnetic and density fluctuations in the solar wind at sub-proton scales, and are relevant for modelling the energy dissipation in a broad range of astrophysical systems.

79 ASTRONOMY AND ASTROPHYSICS↗