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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Prediction of thermal acoustic oscillations (TAOs) in the CLAES solid CO2/neon system

Results are presented of a study initiated to investigate the possibility that the existence of thermal acoustic oscillations (TAOs) in the Cryogenic Limb Atmospheric Etalon Spectrometer (CLAES) neon plumbing system ground configuration could be the cause of higher-than-predicted heat rates measured during thermal ground testing. Tests were conducted between warm boundary temperatures ranging from 40 to 100 K, which simulated the actual test conditions of the CLAES CO2/neon system. TAOs were observed between 6 and 106 Torr, which agreed with the analytical predictions, and verified the possible existence of TAOs in the CLAES system during ground testing. The presence of TAOs was eventually confirmed in the CLAES system during a subsequent thermal test and were determined to have caused the higher heat rates measured during the prior thermal test.

Spradley, I. E.↗
NASA NTRS1998

Boreal Winter Predictions with the GEOS-2 GCM: The Role of Boundary Forcing and Initial Conditions

Ensembles of atmospheric General Circulation Model (GCM) seasonal forecasts and long-term simulations (1980-94) are analyzed to assess the controlling influences of boundary forcing and memory of the initial conditions. Both the forecasts and simulations are carried out with version 2 of the Goddard Earth Observing System (GEOS-2) GCM forced with observed sea surface temperatures (SSTs). While much of the focus is on the seasonal time scale (January- March) and the Pacific North American (PNA) region, we also present results for other regions, shorter time scales, and other known modes of variability in the northern hemisphere extratropics. Forecasts of indices of some of the key large-scale modes of variability show that there is considerable variability in skill between different regions of the Northern Hemisphere. The eastern North Atlantic region has the poorest long lead forecast skill showing no skill beyond about 10 days. Skillful seasonal forecasts are primarily confined to the wave-like ENSO response emanating from the tropical Pacific. In the Northern Hemisphere, this is associated with the well-known Pacific/North American (PNA) pattern. Memory of the initial conditions is the major factor leading to skillful extratropical forecasts of lead time less than one month, while SST forcing is the only factor at the seasonal time scale. SST forcing contributes to skillful forecasts at sub- seasonal time scales only over the PNA region. The GEOS-2 GCM produces average (1980-94) signal to noise ratios which are less than one everywhere in the extratropics, except for the subtropical Pacific where they approach 1.5. When confined to the ENSO years, the maximum signal to noise ratios occur in the PNA region where they exceed three. An assessment of the sampling distribution of the forecasts suggests the model's ENSO response is very likely too weak. These results show some sensitivity to the uncertainties in the estimates of the SST forcing fields. In the North Pacific region, the sensitivity to SST forcing manifests itself primarily as changes in the variability of the PNA response, underscoring the need for an ensemble approach to the seasonal prediction problem.

Chang, Yehui↗
NASA NTRS2014

Modifications of the Quasi-biennial Oscillation by a Geoengineering Perturbation of the Stratospheric Aerosol Layer

This paper examines the impact of geoengineering via stratospheric sulfate aerosol on the quasi-biennial oscillation (QBO) using the NASA Goddard Earth Observing System (GEOS-5) Chemistry Climate Model. We performed four 30-year simulations with a continuous injection of sulfur dioxide on the equator at 0 degree longitude. The four simulations differ by the amount of sulfur dioxide injected (5Tg per year and 2.5 Tg per year) and the altitude of the injection (16km-25km and 22km-25km). We find that such an injection dramatically alters the quasi-biennial oscillation, prolonging the phase of easterly shear with respect to the control simulation. In the case of maximum perturbation, i.e. highest stratospheric aerosol burden, the lower tropical stratosphere is locked into a permanent westerly QBO phase. This locked QBO westerly phase is caused by the increased aerosol heating and associated warming in the tropical lower stratosphere.

Geoengineering↗
NASA NTRS2005

Cloud Properties Simulated by a Single-Column Model. Part II: Evaluation of Cumulus Detrainment and Ice-phase Microphysics Using a Cloud Resolving Model

This paper is the second in a series in which kilometer-scale-resolving observations from the Atmospheric Radiation Measurement program and a cloud-resolving model (CRM) are used to evaluate the single-column model (SCM) version of the National Centers for Environmental Prediction Global Forecast System model. Part I demonstrated that kilometer-scale cirrus properties simulated by the SCM significantly differ from the cloud radar observations while the CRM simulation reproduced most of the cirrus properties as revealed by the observations. The present study describes an evaluation, through a comparison with the CRM, of the SCM's representation of detrainment from deep cumulus and ice-phase microphysics in an effort to better understand the findings of Part I. It is found that detrainment occurs too infrequently at a single level at a time in the SCM, although the detrainment rate averaged over the entire simulation period is somewhat comparable to that of the CRM simulation. Relatively too much detrained ice is sublimated when first detrained. Snow falls over too deep of a layer due to the assumption that snow source and sink terms exactly balance within one time step in the SCM. These characteristics in the SCM parameterizations may explain many of the differences in the cirrus properties between the SCM and the observations (or between the SCM and the CRM). A possible improvement for the SCM consists of the inclusion of multiple cumulus cloud types as in the original Arakawa-Schubert scheme, prognostically determining the stratiform cloud fraction and snow mixing ratio. This would allow better representation of the detrainment from deep convection, better coupling of the volume of detrained air with cloud fraction, and better representation of snow field.

Luo, Yali↗
NASA NTRS1993

Mesoscale simulations of the November 25-26 and December 5-6 cirrus cases using the RAMS model

The Regional Atmospheric Modeling System (RAMS), developed at Colorado State University, was used during the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) 2 (13 Nov. through 6 Dec. 1991) to provide real time forecasts of cirrus clouds. Forecasts were run once a day, initializing with the 0000 UTC dataset provided by NOAA (Forecast Systems Laboratory (FSL) Mesoscale Analysis and Prediction System (MAPS)). In order to obtain better agreement with observations, a second set of simulations were done for the FIRE 2 cases that occurred on 25-26 Nov. and 5-6 Dec. In this set of simulations, a more complex radiation scheme was used, the Chen/Cotton radiation scheme, along with the nucleation of ice occurring at ice supersaturations as opposed to nucleation occurring at water supersaturations that was done in the actual forecast version. The runs using these more complex schemes took longer wall clock time (7-9 hours for the actual forecasts as compared to 12-14 hrs for the runs using the more complex schemes) however, the final results of the simulations were definitely improved upon. Comparisons between these two sets of simulations are given. Now underway are simulations of these cases using a closed analytical solution for the auto-conversion of ice from a pristine ice class (sizes less than about 50 microns in effective diameter) to a snow class (effective diameters on the order of several hundred microns). This solution is employed along with a new scheme for the nucleation of ice crystals due to Meyers et al and Demott et al. The scheme is derived assuming complete gamma distributions for both the pristine and snow classes. The time rate of change of the number concentration and mass mixing-ratio of each distribution is found by calculating either the flux of crystals that grow beyond a certain critical diameter by vapor deposition in an ice supersaturated regime or by calculating the flux of crystals that evaporate to sizes below that same critical effective diameter.

Harrington, J. L.↗
NASA NTRS1972

Objective analysis of sea-level winds and pressures derived from simulated observations of a satellite radar-radiometer and actual conventional data.

A computer-based procedure is developed that combines a field of simulated satellite-derived wind speeds with a limited amount of conventional surface data so as to recover the surface pressure field and the vector wind field over the North Pacific Ocean. Wind speeds are determined from an objective analysis of high spatial density ship observations in order to simulate the anticipated output of a proposed satellite-mounted radar-radiometer system. The simulated speeds are combined with the conventional network for various spatial distributions of ship data. The average rms departure of sea-level pressure fields analyzed by deleting from 75-94% of the available ship observations from the maximum data analysis is from 3.0-4.0 mb. Comparison of the wind components implied by the isobaric patterns to those of the withheld ship observations yields average rms differences of from 8.7-9.4 kt for a range of 75-94% data deletion.

Druyan, L. M.↗
DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗
NASA NTRS1989

Aerosol backscatter studies supporting LAWS

Optimized Royal Signals and Radar Establishment (RSRE), Laser True Airspeed System (LATAS) algorithm for low backscatter conditions was developed. The algorithm converts backscatter intensity measurements from focused continuous-wave (CW) airborne Doppler lidar into backscatter coefficients. The performance of optimized algorithm under marginal backscatter signal conditions was evaluated. The 10.6 micron CO2 aerosol backscatter climatologies were statistically analyzed. Climatologies reveal clean background aerosol mode near 10(exp -10)/kg/sq m/sr (mixing ratio units) through middle and upper troposhere, convective mode associated with planetary boundary layer convective activity, and stratospheric mode associated with volcanically-generated aerosols. Properties of clean background mode are critical to design and simulation studies of Laser Atmospheric Wind Sounder (LAWS), a MSFC facility Instrument on the Earth Observing System (Eos). Previous intercomparisons suggested correlation between aerosol backscatter at CO2 wavelength and water vapor. Field measurements of backscatter profiles with MSFC ground-based Doppler lidar system (GBDLS) were initiated in late FY-88 to coincide with independent program of local rawinsonde releases and overflights by Multi-spectral Atmospheric Mapping Sensor (MAMS), a multi-channel infrared radiometer capable of measuring horizontal and vertical moisture distributions. Design and performance simulation studies for LAWS would benefit from the existence of a relationship between backscatter and water vapor.

Rothermel, Jeffry↗
DOE OSTI2024

Adaptive variational simulation for open quantum systems

Emerging quantum hardware provides new possibilities for quantum simulation. While much of the research has focused on simulating closed quantum systems, the real-world quantum systems are mostly open. Therefore, it is essential to develop quantum algorithms that can effectively simulate open quantum systems. Here we present an adaptive variational quantum algorithm for simulating open quantum system dynamics described by the Lindblad equation. The algorithm is designed to build resource-efficient ansatze through the dynamical addition of operators by maintaining the simulation accuracy. We validate the effectiveness of our algorithm on both noiseless simulators and IBM quantum processors and observe good quantitative and qualitative agreement with the exact solution. We also investigate the scaling of the required resources with system size and accuracy and find polynomial behavior. Our results demonstrate that near-future quantum processors are capable of simulating open quantum systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An Improved Convection Parameterization with Detailed Aerosol–Cloud Microphysics for a Global Model

Abstract A new microphysical treatment that includes aerosol–cloud interactions and secondary ice production (SIP) mechanisms is implemented in the convection scheme of the Community Atmosphere Model, version 6 (CAM6). The approach is to embed a 1D Lagrangian parcel model in the bulk convective plume of the existing deep convection parameterization. Aerosol activation, growth processes including collision/coalescence, and three processes of SIP mechanisms, two of which are normally overlooked in atmospheric models, are represented in this embedded parcel model. These microphysical processes are treated with a hybrid bin/bulk scheme and a high spatial and temporal resolution for the integration of the embedded parcel in 1D, allowing vertical velocity to determine the microphysical evolution following the in-cloud motion during ascent. Simulations of an observed case (Midlatitude Continental Convective Clouds Experiment) of a mesoscale convective system in Oklahoma, United States, with a single-column model (SCAM) version of CAM, are compared with aircraft in situ and ground-based observations of microphysical properties from the convection and precipitation. Results from the validation show the new microphysical scheme has a good representation of the ice initiation in the bulk convective plume, including the known and empirically quantified pathways of primary and secondary initiation, with benefits for the accuracy of properties of its supercooled cloud liquid. The sensitivity simulations and use of tagging tracers for the validated simulation confirm that the newly included SIP mechanisms are of paramount importance for convective microphysics and can be successfully treated in the global model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Hydrodynamic simulations of stellar wind disruption by a compact X-ray source

This paper presents two-dimensional numerical simulations of the gas flow in the orbital plane of a massive X-ray binary system, in which the mass accretion is fueled by a radiation-driven wind from an early-type companion star. These simulations are used to examine the role of the compact object (either a neutron star or a black hole) in disturbing the radiatively accelerating wind of the OB companion, with an emphasis on understanding the origin of the observed soft X-ray photoelectric absorption seen at late orbital phases in these systems. On the basis of these simulations, it is suggested that the phase-dependent photoelectric absorption seen in several of these systems can be explained by dense filaments of compressend gas formed in the nonsteady accreation bow shock and wake of the compact object.

Blondin, John M.↗
NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗
DOE OSTI2023

Collisional-energy-cascade model for nonthermal velocity distributions of neutral atoms in plasmas

Nonthermal velocity distributions with much greater tails than a Maxwellian have been observed for radical atoms in plasmas for a long time. Historically, such velocity distributions have been modeled by a two-temperature Maxwell distribution. In this paper, I propose a model based on collisional energy cascade, which has been studied in the field of granular materials. In the collisional energy cascade, a particle ensemble undergoes energy input at the high-energy region, entropy production by elastic collisions among particles, and energy dissipation. For radical atoms, energy input may be caused by the Franck-Condon energy of molecular dissociation or charge-exchange collision with hot ions, and the input energy is eventually dissipated by collisions with the walls. Here, I show that the steady-state velocity distribution in the collisional energy cascade is approximated by the generalized Mittag-Leffler distribution, which is a one-parameter extension of the Maxwell distribution. This parameter indicates the degree of the nonthermality and is related to the relative importance of energy dissipation over entropy production. This model is compared with a direct molecular dynamics simulation for simplified gaseous systems with energy input, as well as some experimentally observed velocity distributions of light radicals in plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1987

Remote sensing from space; Proceedings of Symposium 3, Workshop V, and Topical Meeting A2 of the Twenty-sixth COSPAR Plenary Meeting, Toulouse, France, June 30-July 11, 1986

Various papers on remote sensing from space are presented. The use of such remote sensing to study terrestrial patterns and processes is examined, including the relevant processes and theories, observed vegetation and surface climate parameters, derived terrestrial processes, atmospheric effects, and new sensors. Quantitative radar remote sensing of land and oceanic surface features is addressed, including systems, calibration, simulation, data evaluation for vegetational studies, altimetry, sea ice monitoring, and oil slick detection. The use of satellite observations for weather prediction is discussed.

Goward, S. N.↗
NASA NTRS2014

Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

Modeling↗
NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗