SEARCH · Engineering Papers

Results for “physical processes”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

NASA NTRS2004

Cross-Scale Coupling in the Inner Magnetosphere

Magnetosphere-ionosphere (MI) coupling has interested scientists for decades and, in spite of experimental and theoretical research efforts, is still one of the least well-known dynamic processes in space plasma. The reason for this is that the numerous physical processes associated with MI coupling occur over multiple spatial lengths and temporal scales. One typical example of MI coupling is small- and large-scale ring current (RC) electrodynamic coupling. In this talk, we will address the two primary issues of RC electrodynamic coupling: (1) RC self-consistent coupling with electromagnetic ion cyclotron (EMIC) waves (small-scale electrodynamic coupling) and (2) RC self-consistent MI coupling that includes calculation of the magnetospheric electric field (large-scale electrodynamic coupling). We also will emphasize the role of the heavy ions in the number of wave-particle interaction magnetospheric processes. In particular, we will discuss some of the experimental and theoretical studies that have investigated the role of the heavy ions (mainly He(+) and O(+)) in generation and propagation of electromagnetic ion cyclotron waves and their contribution to the heating of magnetospheric electrons and ions. The more recent studies have also shown that the heavy ions can greatly contribute to a generation of lower hybrid waves, ring current precipitation phenomena, and the overall energy redistribution in the inner magnetosphere. Using newly developed 2.5-dimensional particle-in-cell simulations, we study the energization and nonlinear coupling of different plasma waves in the presence of the heavy ions. We have shown that the high frequency wave modes critically depend on the heavy ion density and irrespective of the driven wave modes, both the light and heavy ions undergo significant transverse acceleration. But for the large heavy-ion densities, even the electrons are significantly accelerated in the parallel direction by the waves below the LH frequency.

Khazanov, G. V.↗

NASA NTRS1974

Non-relativistic solar electrons

Summary of both the direct spacecraft observations of nonrelativistic solar electrons, and observations of the X-ray and radio emission generated by these particles at the sun and in the interplanetary medium. These observations bear on three physical processes basic to energetic particle phenomena: (1) the acceleration of particles in tenuous plasmas; (2) the propagation of energetic charged particles in a disordered magnetic field, and (3) the interaction of energetic charged particles with tenuous plasmas to produce electromagnetic radiation. Because these electrons are frequently accelerated and emitted by the sun, mostly in small and relatively simple flares, it is possible to define a detailed physical picture of these processes. In many small solar flares nonrelativistic electrons accelerated during flash phase constitute the bulk of the total flare energy. Thus the basic flare mechanism in these flares essentially converts the available flare energy into fast electrons. Nonrelativistic electrons exhibit a wide variety of propagation modes in the interplanetary medium, ranging from diffusive to essentially scatter-free. This variability in the propagation may be explained in terms of the distribution of interplanetary magnetic field fluctuations.

Lin, R. P.↗

DOE OSTI2024

Lightning Characteristics Associated With Storm Modes Observed During RELAMPAGO

Global satellite studies show a maximum in deep convection and lightning downstream of the Andes in subtropical South America. The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign was designed to investigate the physical processes that contribute to the rapid development of deep convection and mesoscale convective systems (MCSs) in Argentina. A lightning mapping array (LMA) was deployed to Argentina as part of RELAMPAGO to collect lightning observations from extreme storms in the region. This study combines lightning data from the LMA and the Geostationary Lightning Mapper onboard GOES-16 with 1-km gridded radar data to examine the electrical characteristics of a variety of convective storms throughout their life cycle observed during RELAMPAGO. Results from the full campaign show 48% of flashes are associated with deep convection that occurs along the eastern edge of the Sierras de Córdoba (SDC) overnight. These flashes are 65 km 2 smaller on average compared to stratiform flashes, which occur most frequently 50–100 km east of the SDC in the early morning hours, consistent with the upscale growth of MCSs off the terrain. Analysis of the 13–14 December MCS shows that sharp increases in flash rates correspond to deep and wide convective cores that have high graupel and hail mass, 35-dBZ volume, and ice water path. This work validates previous satellite studies of lightning in the region, but also provides higher spatial and temporal resolution information across the convective life cycle that has not been available in previous studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference

We discuss the challenge of developing observationally informed parameterizations of microphysics for use at a hierarchy of modeling scales. Our proposed approach is applicable to any domain that suffers from a two-fold parameterization problem, where physical processes are not resolved at the model scale (the first problem), and those processes are uncertain at any scale (the second problem). For such problems, a physical approach facilitates modeling across scales, as well as systematic observational inference accelerated by machine learning (ML) surrogate models, and quantification of physical uncertainties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces

The objective of this project is to develop, and integrate, high-performance simulation tools capable of predicting plasma-facing component (PFC) operating lifetime and the impact of the evolving surface morphology of tungsten-based PFCs on plasma contamination, including the dynamic recycling of fuel species and tritium retention, in future magnetic fusion devices. Establishing a fundamental physical understanding and developing predictive capabilities of plasma-surface interactions (PSI) requires simultaneously addressing complex and diverse physics occurring over a wide range of length (Angstroms to meters) and time (femtoseconds to years) scales, as well as integrating extensive physical processes across the plasma–surface interface. This requires development of not only detailed physics models and computational strategies at each scale, but also algorithms and methods to couple them effectively in a way that can be robustly validated. Deploying these tools requires the continued development and coupling of leadership-scale computational codes to describe the boundary plasma and the evolving PFC surface, as well as a host of simulations that bridge disparate scales to address complex physical and computational issues at the plasma–surface interface in multi-component materials systems for magnetic fusion energy development beyond ITER.

36 MATERIALS SCIENCE↗

NASA NTRS1989

Primitive bodies - Molecular abundances in Comet Halley as probes of cometary formation environments

The most recent results on abundances of molecules in Halley's comet are examined in the context of various models for the environment in which comets formed. These environments include molecular clouds associated with star-forming regions, the solar nebula, gaseous disks around proto-planets, and combinations of these. Of all constituents in a cometary nucleus, the highly volatile molecules such as methane, ammonia, molecular nitrogen, and carbon monoxide are most sensitive to the final episode of cometary grain formation and incorporation in the comet's nucleus; hence they likely reflect at least some chemical processing in the solar nebula. Proper interpretation requires modeling of a number of physical processes including gas phase chemistry, chemistry on grain surfaces, and fractionation effects resulting from preferential incorporation of certain gases in proto-cometary grains. The abundance of methane in Halley's comet could be a key indicator of where that comet formed, provided the methane abundance on grains in star-forming regions can be observationally constrained.

Lunine, Jonathan I.↗

NASA NTRS2014

The Primordial Inflation Explorer (PIXIE)

The Primordial Inflation Explorer is an Explorer-class mission to measure the gravity-wave signature of primordial inflation through its distinctive imprint on the linear polarization of the cosmic microwave background. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning 2.5 decades in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). Multi-moded non-imaging optics feed a polarizing Fourier Transform Spectrometer to produce a set of interference fringes, proportional to the difference spectrum between orthogonal linear polarizations from the two input beams. Multiple levels of symmetry and signal modulation combine to reduce the instrumental signature and confusion from unpolarized sources to negligible levels. PIXIE will map the full sky in Stokes I, Q, and U parameters with angular resolution 2.6 deg and sensitivity 0.2 μK per 1 deg square pixel. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r less than 10(exp −3) at 5 standard deviations. In addition, PIXIE will measure the absolute frequency spectrum to constrain physical processes ranging from inflation to the nature of the first stars to the physical conditions within the interstellar medium of the Galaxy. We describe the PIXIE instrument and mission architecture with an emphasis on the expected level of systematic error suppression.

Primordial Inflation Explorer is an Explorer-class↗

NASA NTRS2016

MAVEN Observations of Energy-Time Dispersed Electron Signatures in Martian Crustal Magnetic Fields

Energy-time dispersed electron signatures are observed by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission in the vicinity of strong Martian crustal magnetic fields. Analysis of pitch angle distributions indicates that these dispersed electrons are typically trapped on closed field lines formed above strong crustal magnetic sources. Most of the dispersed electron signatures are characterized by peak energies decreasing with time rather than increasing peak energies. These properties can be explained by impulsive and local injection of hot electrons into closed field lines and subsequent dispersion by magnetic drift of the trapped electrons. In addition, the dispersed flux enhancements are often bursty and sometimes exhibit clear periodicity, suggesting that the injection and trapping processes are intrinsically time dependent and dynamic. These MAVEN observations demonstrate that common physical processes can operate in both global intrinsic magnetospheres and local crustal magnetic fields.

martian↗

NASA NTRS2016

The Primordial Inflation Explorer (PIXIE)

The Primordial Inflation Explorer is an Explorer-class mission to open new windows on the early universe through measurements of the polarization and absolute frequency spectrum of the cosmic microwave background. PIXIE will measure the gravitational-wave signature of primordial inflation through its distinctive imprint in linear polarization, and characterize the thermal history of the universe through precision measurements of distortions in the blackbody spectrum. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning over 7 octaves in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). Multi-moded non-imaging optics feed a polarizing Fourier Transform Spectrometer to produce a set of interference fringes, proportional to the difference spectrum between orthogonal linear polarizations from the two input beams. Multiple levels of symmetry and signal modulation combine to reduce systematic errors to negligible levels. PIXIE will map the full sky in Stokes I, Q, and U parameters with angular resolution 2.6 degrees and sensitivity 70 nK per 1degree square pixel. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r < 10(exp. -3) at 5 standard deviations. The PIXIE mission complements anticipated ground-based polarization measurements such as CMBS4, providing a cosmic-variance-limited determination of the large-scale E-mode signal to measure the optical depth, constrain models of reionization, and provide a firm detection of the neutrino mass (the last unknown parameter in the Standard Model of particle physics). In addition, PIXIE will measure the absolute frequency spectrum to characterize deviations from a blackbody with sensitivity 3 orders of magnitude beyond the seminal COBE/FIRAS limits. The sky cannot be black at this level; the expected results will constrain physical processes ranging from inflation to the nature of the first stars and the physical conditions within the interstellar medium of the Galaxy. We describe the PIXIE instrument and mission architecture required to measure the CMB to the limits imposed by astrophysical foregrounds.

Kogut, Alan↗

DOE OSTI2020

A Moist Physics Parameterization Based on Deep Learning

Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Shallow Cumulus Properties as Captured by Adiabatic Fraction in High-Resolution LES Simulations

Abstract Shallow convective clouds are important players in Earth’s energy budget and hydrological cycle, and are abundant in the tropical and subtropical belts. They greatly contribute to the uncertainty in climate predictions due to their unresolved, complex processes that include coupling between the dynamics and microphysics. Analysis of cloud structure can be simplified by considering cloud motions as a combination of moist adiabatic motions like adiabatic updrafts and turbulent motions leading to deviation from adiabaticity. In this work, we study the sizes and occurrence of adiabatic regions in shallow cumulus clouds during their growth and mature stages, and use the adiabatic fraction (AF) as a continuous metric to describe cloud processes and properties from the core to the edge. To do so, we simulate isolated trade wind cumulus clouds of different sizes using the System of Atmospheric Modeling (SAM) model in high resolution (10 m) with the Hebrew University spectral bin microphysics (SBM). The fine features in the clouds’ dynamics and microphysics, including small near-adiabatic volumes and a thin transition zone at the edge of the cloud (∼20–40 m in width), are captured. The AF is shown to be an efficient measure for analyzing cloud properties and key processes determining the droplet-size distribution formation and shape during the cloud evolution. Physical processes governing the properties of droplet size distributions at different cloud regions (e.g., core, edge) are analyzed in relation to AF. Significance Statement 1) This study investigates the evolution of cumulus clouds (Cu) using a 10-m-resolution LES model with spectral bin microphysics. 2) The study improves the understanding of the mutual effects of adiabatic updrafts and lateral entrainment and mixing. 3) The study demonstrates the existence of an adiabatic core in nonprecipitating Cu. 4) Shapes of the droplet size distributions are closely related to the adiabatic fraction values. 5) Utilization of high resolution reveals the existence of physically significant small features in the cloud structure, such as a narrow cloud interface zone and small adiabatic volumes.

54 ENVIRONMENTAL SCIENCES↗

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 OSTI2024

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks

Accurate and realistic geological modeling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.

58 GEOSCIENCES↗

NASA NTRS1980

The stratospheric sulfate aerosol layer - Processes, models, observations, and simulations

After briefly reviewing the observational data on the stratospheric sulfate aerosol layer, the chemical and physical processes that are likely to fix the properties of the layer are discussed. We present appropriate continuity equations for aerosol particles, and show how to solve the equations on a digital computer. Simulations of the unperturbed aerosol layer by various published models are discussed and the sensitivity of layer characteristics to variations in several aerosol model parameters is studied. We discuss model applications to anthropogenic pollution problems and demonstrate that moderate levels of aerospace activity (supersonic transport and Space Shuttle operations) will probably have only a negligible effect on global climate. Finally, we evaluate the possible climatic effect of a ten-fold increase in the atmospheric abundance of carbonyl sulfide.

Whitten, R. C.↗

NASA NTRS1983

Computer simulation of surface and film processes

Adequate computer methods, based on interactions between discrete particles, provide information leading to an atomic level understanding of various physical processes. The success of these simulation methods, however, is related to the accuracy of the potential energy function representing the interactions among the particles. The development of a potential energy function for crystalline SiO2 forms that can be employed in lengthy computer modelling procedures was investigated. In many of the simulation methods which deal with discrete particles, semiempirical two body potentials were employed to analyze energy and structure related properties of the system. Many body interactions are required for a proper representation of the total energy for many systems. Many body interactions for simulations based on discrete particles are discussed.

Tiller, W. A.↗

NASA NTRS1992

On the physics of waves in the solar atmosphere: Wave heating and wind acceleration

In the area of solar physics, new calculations of the acoustic wave energy fluxes generated in the solar convective zone was performed. The original theory developed was corrected by including a new frequency factor describing temporal variations of the turbulent energy spectrum. We have modified the original Stein code by including this new frequency factor, and tested the code extensively. Another possible source of the mechanical energy generated in the solar convective zone is the excitation of magnetic flux tube waves which can carry energy along the tubes far away from the region. The problem as to how efficiently those waves are generated in the Sun was recently solved. The propagation of nonlinear magnetic tube waves in the solar atmosphere was calculated, and mode coupling, shock formation, and heating of the local medium was studied. The wave trapping problems and evaluation of critical frequencies for wave reflection in the solar atmosphere was studied. It was shown that the role played by Alfven waves in the wind accelerations and the coronal hole heating is dominant. Presently, we are performing calculations of wave energy fluxes generated in late-type dwarf stars and studying physical processes responsible for the heating of stellar chromospheres and coronae. In the area of physics of waves, a new analytical approach for studying linear Alfven waves in smoothly nonuniform media was recently developed. This approach is presently being extended to study the propagation of linear and nonlinear magnetohydrodynamic (MHD) waves in stratified, nonisothermal and solar atmosphere. The Lighthill theory of sound generation to nonisothermal media (with a special temperature distribution) was extended. Energy cascade by nonlinear MHD waves and possible chaos driven by these waves are presently considered.

Musielak, Z. E.↗

NASA NTRS1997

FAST Spacecraft Battery Design and Performance

The Fast Auroral Snapshot (FAST) Explorer spacecraft is to study the physical processes that produce the aurora borealis and aurora australis. It is a unique plasma physics experiment that will take fundamental measurements of the magnetic and electrical fields. This investigation will add significantly to our understanding of the near-earth space environments and its effect. The FAST has a 1 year requirement and 3-year goal for its mission life in low earth orbit. The FAST power power system topology is a Direct Energy Transfer (DET) system based on the SAMPEX design. The FAST flight battery supplies power to the satellite during pre-launch operations, the launch phase, the eclipse periods for all mission phases, and when the load is about 50 watts.

Jung, David S.↗

NASA NTRS2011

Testing the Standard Model with the Primordial Inflation Explorer

Kogut, Alan J.↗