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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

High Performance, High Fidelity: A GPU‐Accelerated Doubly‐Periodic Configuration of the Simple Cloud‐Resolving E3SM Atmosphere Model Version 1 (DP‐SCREAMv1)

The development of the Simplified Cloud Resolving Energy Exascale Earth System Atmosphere Model (SCREAMv1) enables global storm-resolving simulations on modern GPU-based supercomputers. However, the high computational cost of SCREAMv1 limits its routine use for process-level studies, creating a need for efficient proxy configurations. This study addresses this gap by introducing DP-SCREAMv1, a doubly periodic cloud-resolving model designed to be fully consistent with SCREAMv1 while enabling high-resolution, long-duration simulations at significantly reduced computational expense by simulating a limited doubly periodic domain rather than the entire globe. Built on a C++/Kokkos architecture, DP-SCREAMv1 achieves exceptional performance scalability on GPU systems and includes a rich library of cases for validation and scientific exploration. In this work, we demonstrate short wall-clock times at SCREAMv1's default resolution and show that DP-SCREAMv1 supports routine execution of large-domain, high-resolution experiments that were previously challenging in practice. Furthermore, we show that DP-SCREAMv1 enables routine execution of “Giga-LES” style simulations and facilitates large-domain, high-resolution simulations that were recently considered burdensome to perform. These results document an efficient, fully consistent process-level configuration for SCREAMv1 (DP-SCREAMv1) and illustrate its use for long-duration and large-domain experiments at cloud-resolving to eddy-permitting resolution.

Environmental sciences↗
NASA NTRS1992

Multiple scattering effects on spaceborne lidar

A semianalytic Monte Carlo code originally developed for oceanographic calculations (Poole et al., 1981) has been modified for use in studying multiple scattering of space-based lidar. The approach is very similar to that described by Kunkel and Weinman (1976). The trajectory of each photon is followed from the transmitter through multiple scattering until the photon is either scattered backward out of the atmosphere, scattered forward into the ground and absorbed, or scattered out the sides of the cloud. The probability that the photon will return directly to the detector is computed and summed over all significant scattering events within the field of view of the detector. Multiple scattering of the lidar pulse causes an apparent increase in the transmittance of the medium. Multiple scattering effects for space-based lidar are more significant than for ground-based lidar due to the much larger beam diameter in the atmosphere. These larger diameters are due not only to the greater range between the lidar and the scattering volume, but also the need to maintain relatively large beam divergences to satisfy eye safety restrictions on the laser irradiance at the Earth's surface. The simulations presented here are for a wavelength of 1064 nm and the Deirmendjian C1 phase function, which yields an extinction coefficient of 17.259/km. We have looked at two cases: a space-based lidar at 296 km observing a C1 cloud 293 km from the lidar and, for comparison purposes, a ground-based lidar looking at a C1 cloud with a base height of either 2 km or 5 km. The C1 size distribution roughly approximates that of stratocumulus or altocumulus clouds (aufm Kampe and Weickmann, 1957).

Winker, David M.↗
NASA NTRS1975

Computational study of nonlinear plasma waves. I - Simulation model and monochromatic wave propagation. II - Sideband instability and satellite growth

A hybrid plasma simulation model is described and applied to the study of electrostatic wave propagation in a one-dimensional Maxwellian plasma with periodic boundary conditions. The model employs a cloud-in-cell scheme which can drastically reduce the fluctuations in particle simulation models and greatly ease the computational difficulties of the Vlasov equation approach. A grid in velocity space is introduced and the particles are represented by points in the x-v phase space. The model is tested first in the absence of an applied signal and then in the presence of a small-amplitude perturbation. The method is also used to study propagation of an essentially monochromatic plane wave. Results on amplitude oscillation and nonlinear frequency shift are compared with available theories.

Matsuda, Y.↗
NASA NTRS1992

Maintenance of strong rotational winds in Venus' middle atmosphere by thermal tides

The cloud-level atmosphere of Venus takes little more than four days to complete one rotation, whereas the solid planet below has a 243-day period. Computer simulations of the circulation of the Venus middle atmosphere between 40 and 85 kilometers, as driven by solar radiation absorbed in the clouds, reproduce (1) the observed cloud-level rotation rate, (2) strong vertical shears above and below the cloud tops, and (3) midlatitude jets and strong poleward flow on the day side. Simulated circulations converge to yield nearly the same zonal winds when initialized with both stronger or weaker rotation rates. These results support the hypothesis that the observed cloud-top rotation rate is maintained by statistical balance between fluxes of momentum by thermal tides and momentum advection by mean meridional circulation.

Newman, Matthew↗
DOE OSTI2024

Causally‐Informed Deep Learning to Improve Climate Models and Projections

Abstract Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

14YWT TEM images for LAUR [Images]

Transmission Electron Microscopy (TEM) images attached here are for machine learning study, and these will be uploaded to outer cloud at LANL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

PixelStorm: A Remote Display for Remote Sensing Ground Stations

PixelStorm is a software application for displaying native high-performance applications from remote cloud environments. It is tailored for remote sensing missions that require high framerates, high resolutions, and minimal loss of quality. PixelStorm utilizes hardware-accelerated video compression on graphics processing units and a Sandia-developed streaming network protocol. Using our architecture, we can demonstrate interactive native applications running across two 4K monitors at 60 frames per second while maintaining the visual fidelity required by our missions. This technology allows for the migration of mission critical desktop applications to cloud environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Making Atmospheric Convective Parameterizations Obsolete with Machine Learning Emulation

Parameterizations of moist convection in atmospheric models are notoriously problematic, and while global cloud resolving models (GCRM) are often touted as the ultimate solution, the computational cost is a considerable hurdle to overcome. Machine learning emulation of GCRMs for predictive modelling can leverage the DOE’s computational resource investments and allow widespread use of GCRMs such that traditional parameterizations become obsolete for most applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Proper Motions, Orbits, and Tidal Influences of Milky Way Dwarf Spheroidal Galaxies

We combine Gaia early data release 3 astrometry with accurate photometry and utilize a probabilistic mixture model to measure the systemic proper motion of 52 dwarf spheroidal (dSph) satellite galaxies of the Milky Way (MW). For the 46 dSphs with literature line-of-sight velocities we compute orbits in both a MW and a combined MW + Large Magellanic Cloud (LMC) potential and identify Car II, Car III, Hor I, Hyi I, Phx II, and Ret II as likely LMC satellites. 40% of our dSph sample has a >25% change in pericenter and/or apocenter with the MW + LMC potential. For these orbits, we use a Monte Carlo sample for the observational uncertainties for each dSph and the uncertainties in the MW and LMC potentials. We predict that Ant II, Boo III, Cra II, Gru II, and Tuc III should be tidally disrupting by comparing each dSph's average density relative to the MW density at its pericenter. dSphs with large ellipticity (CVn I, Her, Tuc V, UMa I, UMa II, UMi, Wil 1) show a preference for their orbital direction to align with their major axis even for dSphs with large pericenters. We compare the dSph radial orbital phase to subhalos in MW-like N-body simulations and infer that there is not an excess of satellites near their pericenter. With projections of future Gaia data releases, we find that dSph's orbital precision will be limited by uncertainties in the distance and/or MW potential rather than in proper motion precision. Finally, we provide our membership catalogs to enable community follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Low-mass protogalaxies and absorption lines in quasi-stellar objects.

The possibility is explored that the many weak unidentified absorption lines in high-redshift QSO spectra are L-alpha lines of hydrogen. The likely existence of a metagalactic flux of ionizing radiation indicates that hydrogen clouds at high redshifts must be thoroughly ionized. Observations of PHL 957 show that the suggested clouds may be low-mass protogalaxies. A simple model for the clouds' collapse and for the flux of ionizing radiation is used to compute the distribution of expected L-alpha lines in the spectrum of a QSO as a function of wavelength and H I column density. The computed distribution is in accord with observations of the two most distant QSOs, PHL 957 and 4C 05.34. Crudities in the theory and stronger tests of its validity are also discussed.

Arons, J.↗
NASA NTRS1972

Lidar probing of the atmosphere: Some quantitative aspects

Lidar uses laser energy in radar fashion to observe atmospheric backscattering as a function of range. Because of the short optical and near-optical wavelengths used, very small particles and even the gaseous molecules cause significant scattering. This can complicate the evaluation of the observations by introducing attenuation along the path as a second unknown into the lidar equation. In many cases, however, the observations may be interpreted directly on a qualitative basis and show the distribution of particulate matter in clear air or enable the dimensions of visible cloud to be measured accurately. In other cases, particularly where additional data are available, quantitative solutions can provide useful information on remote targets such as tenuous smoke clouds or haze layers. Examples of such observations are given, illustrating the computational approach to the evaluation of the volume concentration of natural dust and haze layers in the lower atmosphere and the mass concentration of a smoke plume. In both cases lidar data are related to independently obtained data on the particulate concentrations involved.

Collis, R. T. H.↗
NASA NTRS1973

A theory of microwave apparent temperature over the ocean

In the microwave region combined active (scatterometer) and passive (radiometer) remote sensors over the ocean show promise of providing surface wind speeds and weather information to the oceanographer and meteorologist. This has aroused great interest in the investigation of the scattering of waves from the sea surface. A composite surface scattering theory is investigated. The two-scale scattering theory proposed by Semyonov was specifically extended to compute the emmision and scattering characteristics of ocean surfaces. The effects of clouds and rain on the radiometer and scatterometer observations are also investigated using horizontally stratified model atmospheres with rough sea surfaces underneath. Various cloud and rain models proposed by meteorologist were employed to determine the rise in the microwave temperature when viewing downward through these model atmospheres. For heavy rain-fall rates the effects of scattering on the radiative transfer process are included.

Wu, S. T.↗
NASA NTRS1974

Impact of combustion products from Space Shuttle launches on ambient air quality

The present work describes some multilayer diffusion models and a computer program for these models developed to predict the impact of ground clouds formed during Space Shuttle launches on ambient air quality. The diffusion models are based on the Gaussian plume equation for an instantaneous volume source. Cloud growth is estimated on the basis of measurable meteorological parameters: standard deviation of the wind azimuth angle, standard deviation of wind elevation angle, vertical wind-speed shear, vertical wind-direction shear, and depth of the surface mixing layer. Calculations using these models indicate that Space Shuttle launches under a variety of meteorological regimes at Kennedy Space Center and Vandenberg AFB are unlikely to endanger the exposure standards for HCl; similar results have been obtained for CO and Al2O3. However, the possibility that precipitation scavenging of the ground cloud might result in an acidic rain that could damage vegetation has not been investigated.

Dumbauld, R. K.↗
NASA NTRS1976

Calcium abundance variations in diffuse interstellar clouds

Observations are discussed which show that the Na I/H2 column-density ratio is relatively constant among different clouds where H2 is optically thin, but the Na I/Ca II column-density ratio varies. This implies that the gas-phase abundance of calcium is variable. It is proposed that the wide variation in the gas-phase calcium abundance can be explained by small variations in the calcium content of grains. The observed enhancement of the Ca II/Na I column-density ratio with cloud velocity is attributed to grain destruction in grain-grain collisions within the same cloud. The structure of shock waves propagating through the interstellar gas is computed, and the grain-destruction model is compared with observational data.

Jura, M.↗
NASA NTRS1981

On the measurement of precipitation frequencies by passive microwave radiometery

Over most of the microwave spectrum, raindrops both absorb and scatter radiation producing large changes in brightness temperatures relative to clear or cloudy conditions. Since the structure of rain varies substantially for different rain rates and climatological backgrounds, the raindrop size distribution, the rain layer thickness and the ice clouds above the rain layer are all important inputs to the model computations. The subsequent modeling involves applying the Mie theory to derive the absorption and scattering effects and the radiative transfer calculation is based upon a variational iterative approach which takes account of the multiple scattering effect of the rain layer. Results over both ocean and land backgrounds are demonstrated. It is also demonstrated that by using discrimination tests of the radiometric data, the rain/no rain decision can be made and the rainfall rate can be retrieved from a statistical inversion technique.

Kidder, S. Q.↗