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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 1,513 records · Page 84
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 OSTI2013

DID THE INFANT R136 AND NGC 3603 CLUSTERS UNDERGO RESIDUAL GAS EXPULSION?

Based on kinematic data observed for very young, massive clusters that appear to be in dynamical equilibrium, it has recently been argued that such young systems are examples of where the early residual gas expulsion did not happen or had no dynamical effect. The intriguing scenario of a star cluster forming through a single starburst has thereby been challenged. Choosing the case of the R136 cluster of the Large Magellanic Cloud, the most cited one in this context, we perform direct N-body computations that mimic the early evolution of this cluster including the gas-removal phase (on a thermal timescale). Our calculations show that under plausible initial conditions which are consistent with observational data, a large fraction (>60%) of a gas-expelled, expanding R136-like cluster is bound to regain dynamical equilibrium by its current age. Therefore, the recent measurements of velocity dispersion in the inner regions of R136, which indicate that the cluster is in dynamical equilibrium, are consistent with an earlier substantial gas expulsion of R136 followed by a rapid re-virialization (in Almost-Equal-To 1 Myr). Additionally, we find that the less massive Galactic NGC 3603 Young Cluster (NYC), with a substantially longer re-virialization time, is likely to be found to have deviated from dynamical equilibrium at its present age ( Almost-Equal-To 1 Myr). The recently obtained stellar proper motions in the central part of the NYC indeed suggest this and are consistent with the computed models. This work significantly extends previous models of the Orion Nebula Cluster which already demonstrated that the re-virialization time of young post-gas-expulsion clusters decreases with increasing pre-expulsion density.

79 ASTRONOMY AND ASTROPHYSICS↗
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 OSTI2019

Putting the Cloud to Work for Seismology

The cloud infrastructure developed in the business community has enabled access to cluster computing possible for even the smallest research groups, enabling new kinds of research workflows in geophysics.

58 GEOSCIENCES↗
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 OSTI2013

LINE PROFILES OF CORES WITHIN CLUSTERS. II. SIGNATURES OF DYNAMICAL COLLAPSE DURING HIGH-MASS STAR FORMATION

Observations of atomic or molecular lines can provide important information about the physical state of star-forming regions. In order to investigate the line profiles from dynamical collapsing massive star-forming regions (MSFRs), we model the emission from hydrodynamic simulations of a collapsing cloud in the absence of outflows. By performing radiative transfer calculations, we compute the optically thick HCO{sup +} and optically thin N{sub 2}H{sup +} line profiles from two collapsing regions at different epochs. Due to large-scale collapse, the MSFRs have large velocity gradients, reaching up to 20 km s{sup -1} pc{sup -1} across the central core. The optically thin lines typically contain multiple velocity components resulting from the superposition of numerous density peaks along the line of sight. The optically thick lines are only marginally shifted to the blue side of the optically thin line profiles, and frequently do not have a central depression in their profiles due to self-absorption. As the regions evolve, the lines become brighter and the optically thick lines become broader. The lower-order HCO{sup +} (1-0) transitions are better indicators of collapse than the higher-order (4-3) transitions. We also investigate how the beam sizes affect profile shapes. Smaller beams lead to brighter and narrower lines that are more skewed to the blue in HCO{sup +} relative to the true core velocity, but show multiple components in N{sub 2}H{sup +}. High-resolution observations (e.g., with Atacama Large Millimeter Array) can test these predictions and provide insights into the nature of MSFRs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

H - photodetachment and radiative attachment for astrophysical applications

We combine R-matrix calculations, asymptotic relations, and comparison to available experimental data to construct an H- photodetachment cross section reliable over a large range of photon energies and take into account the series of auto-detaching shape and Feshbach resonances between 10.92 and 14.35 eV. The accuracy of the cross section is controlled by ensuring that it satisfies all known oscillator strength sum rules, including contributions from the resonances and single-photon double-electron photodetachment. From the resulting recommended cross section, spontaneous and stimulated radiative attachment rate coefficients are obtained. Photodetachment rates are also computed for the standard interstellar radiation field, in diffuse and dense interstellar clouds, for blackbody radiation, and for high redshift distortion photons in the recombination epoch. Implications are investigated for these astrophysical radiation fields and epochs.

Optics↗
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↗
DOE OSTI2015

Towards Transparent Throughput Elasticity for IaaS Cloud Storage: Exploring the Benefits of Adaptive Block-Level Caching

Storage elasticity on IaaS clouds is a crucial feature in the age of data-intensive computing, especially when considering fluctuations of I/O throughput. This paper provides a transparent solution that automatically boosts I/O bandwidth during peaks for underlying virtual disks, effectively avoiding over-provisioning without performance loss. The authors' proposal relies on the idea of leveraging short-lived virtual disks of better performance characteristics (and thus more expensive) to act during peaks as a caching layer for the persistent virtual disks where the application data is stored. Furthermore, they introduce a performance and cost prediction methodology that can be used both independently to estimate in advance what trade-off between performance and cost is possible, as well as an optimization technique that enables better cache size selection to meet the desired performance level with minimal cost. The authors demonstrate the benefits of their proposal both for microbenchmarks and for two real-life applications using large-scale experiments.

Nicolae, Bogdan↗
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.↗