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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Particles above the tropopause - Measurements and models of stratospheric aerosols, meteoric debris, nacreous clouds, and noctilucent clouds

An outline is presented of the present status of knowledge of stratospheric aerosols, meteoric debris, nacreous clouds, and noctilucent clouds. Considerable progress has been made in studies of these particles during the previous decade and it is appropriate to synthesize the information to provide a background for studies planned for the 1980s. Numerical models of the formation, growth, and evolution are considered and a description is given of the physical processes involved, taking into account aspects of nucleation, coagulation, condensational growth, sedimentation, and questions of dynamical transport. A schematic outline of the physical and chemical processes included in a model of stratospheric aerosols is provided.

Toon, O. B.↗
NASA NTRS1987

On the global mean structure of the thermosphere

A self-consistent model of the global mean structure of the thermosphere is used to examine the ionospheric and thermospheric processes that control the structure of the thermosphere for solar minimum and maximum conditions during geomagnetic quiet periods. The model includes the physical and chemical processes believed to be dominant in the thermosphere and ionosphere. The total solar energy input available in the extreme ultraviolet and in the Schumann-Runge continuum portion of the solar spectrum is 14.1 x 10 to the 11th W and 21.5 x 10 to the 11th W for solar minumum and maximum, respectively. The calculated exospheric temperature is too small if maintained only by radiative processes, and an auroral heat source is necessary to bring the calculated global mean structure into agreement with the MSIS-83 empirical model for both solar minimum and maximum conditions. These results support the consensus that there is high-latitude heat source present at all times.

Roble, R. G.↗
NASA NTRS1979

Ionospheric chemistry

This report deals primarily with progress in the chemistry of minor neutrals and ionized constituents of the thermosphere. Significant progress was made over the last few years in quantitative studies of many chemical processes. This success was primarily due to the advent of multiparameter multisatellite programs which permitted accurate simultaneous measurements to be made of many important parameters. In many cases studies of chemical reactions were made with laboratory-like precision. Rate coefficients have been derived as functions of temperature for a number of important reactions. New information has been acquired on nearly every major process which occurs in the thermosphere, including the recombination rates of all major molecular ions, charge transfer reactions, ion atom interchange reactions, and reactions of neutral and ionized metastable atoms and molecules.

Torr, D. G.↗
NASA NTRS1983

Materials processing in space bibliography, 1983, revised

Flight experiments utilizing a low gravity environment to elucidate and control various processes, or ground based activities that provide supporting research are compiled. Six major categories: crystal growth; solidification of metals, alloys, and composites; fluids, transports, and chemical processes; glasses and ceramics; ultrahigh vacuum and containerless processing technologies; and combustion are included. A list of patents and appendices providing a compilation of anonymously authored collections and reports and a cross reference index are included.

Pentecost, E.↗
NASA NTRS1992

Modeling of hypersonic reacting flows

The effect of nonequilibrium thermochemical processes on the aerodynamic and heat transfer characteristics of a hypersonic vehicle is illustrated with examples. It is shown that the conventional method of predicting chemical reactions always predict that the flow is closer to equilibrium than it actually is, leading to incorrect predictions of aerodynamic characteristics of a vehicle. A method is presented for predicting chemical processes using a two-temperature kinetic model, which is developed on the basis of combining conservation equations for vibrational energy and for electron electronic energy, which are also derived. The validity of the two-temperature model is proven using two types of experimental data: radiation and shock shapes.

Park, Chul↗
NASA NTRS1994

The growth of zeolites A, X and mordenite in space

Zeolites are a class of crystalline aluminosilicate materials that form the backbone of the chemical process industry worldwide. They are used primarily as adsorbents and catalysts and support to a significant extent the positive balance of trade realized by the chemical industry in the United States (around $19 billion in 1991). The magnitude of their efforts can be appreciated when one realizes that since their introduction as 'cracking catalysts' in the early 1960's, they have saved the equivalent of 60 percent of the total oil production from Alaska's North Slope. Thus the performance of zeolite catalysts can have a profound effect on the U.S. economy. It is estimated that a 1 percent increase in yield of the gasoline fraction per barrel of oil would represent a savings of 22 million barrels of crude oil per year, representing a reduction of $400 million in the United States' balance of payments. Thus any activity that results in improvement in zeolite catalyst performance is of significant scientific and industrial interest. In addition, due to their 'stability,' uniformity, and, within limits, their 'engineerable' structures, zeolites are being tested as potential adsorbents to purify gases and liquids at the parts-per-billion levels needed in today's electronic, biomedical, and biotechnology industries and for the environment. Other exotic applications, such as host materials for quantum-confined semiconductor atomic arrays, are also being investigated. Because of the importance of this class of material, extensive efforts have been made to characterize their structures and to understand their nucleation and growth mechanisms, so as to be able to custom-make zeolites for a desired application. To date, both the nucleation mechanics and chemistry (such as what are the 'key' nutrients) are, as yet, still unknown for many, if not all, systems. The problem is compounded because there is usually a 'gel' phase present that is assumed to control the degree of supersaturation, and this gel undergoes a continuous 'polymerization' type reaction during nucleation and growth. Generally, for structure characterization and diffusion studies, which are useful in evaluating zeolites for improving yield in petroleum refining as well as for many of the proposed new applications (e.g., catalytic membranes, molecular electronics, chemical sensors) large zeolites (greater than 100 to 1000 times normal size) with minimum lattice defects are desired. Presently, the lack of understanding of zeolite nucleation and growth precludes the custom design of zeolites for these or other uses. It was hypothesized that the microgravity levels achieved in an orbiting spacecraft could help to isolate the possible effects of natural convection (which affects defect formation) and minimize sedimentation, which occurs since zeolites are twice as dense as the solution from which they are formed. This was expected to promote larger crystals by allowing growing crystals a longer residence time in a high-concentration nutrient field. Thus it was hypothesized that the microgravity environment of Earth orbit would allow the growth of large, more defect-free zeolite crystals in high yield.

Sacco, Albert, Jr.↗
NASA NTRSDate not supplied

How Well Satellite Remote Sensing Can Inform Surface Level Ozone Production Sensitivity to Precursor Trace Gas Emissions?

Surface-level ozone (O3) pollution mitigation strategies rely on a clear understanding of the atmospheric chemical processes involving O3, NOx (nitric oxide (NO) + nitrogen dioxide (NO2)) and volatile organic compounds (VOCs). A robust spatiotemporal classification of O3 photochemical regimes with relative chemical sensitivity of local O3 formation to emission reductions of NOx (NOx-limited regime) versus VOCs (radical-limited regime) is required to design anthropogenic emission reduction policies in order to improve surface air quality. Numerous studies found that the ratio of satellite-retrieved Vertical Column Densities (VCDs) of formaldehyde (HCHO) to NO2 can diagnose O3 production sensitivity. However, uncertainty still remains about the accuracy of such estimates using satellite-based retrievals. This study conducts an investigation to identify how well satellite data can inform surface-level O3 sensitivity by validating satellite-based estimates against aircraft-based retrievals taken during the Long Island Sound Tropospheric Ozone Study (LISTOS-2018) and Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaigns. For this study, we use HCHO and NO2 retrievals from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite, and high spatiotemporal resolution TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The Community Multiscale Air Quality (CMAQ) modelling system is used to provide trace gas vertical profiles to recalculate air mass factor for deriving consistent satellite-based VCDs. To demonstrate the ability of satellites to diagnose O3 production regimes, we validate satellite-derived O3 sensitivity regimes, along with the OMI and TROPOMI products applied, using HCHO and NO2 retrievals from the aircraft-based instruments: Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) spectrometer and Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instruments deployed during the LISTOS-2018 and OWLETS-2 campaigns. The results of this work will advance our fundamental knowledge of the spatiotemporal evolution of the complex O3-NOx-VOC chemistry, and will advance our understanding about the quality of remote-sensing, suborbital, and satellite data for accurately observing surface-level O3 production sensitivity.

Satellite↗
NASA NTRS1992

Remote Detection of Biological Particles and Chemical Plumes Using UV Fluorescence Lidar

A lidar system based on ultraviolet (UV) laser induced fluorescence (LIF) was developed for the remote detection of atmospherically dispersed biological particles and chemical vapors. This UV fluorescence lidar has many potential applications for monitoring environmental pollution, industrial waste emission, agricultural insect control, illicit chemical processing, and military defense operations. The general goal of this work is to investigate the research issues associated with the long range detection and identification of chemicals, e.g. aromatic solvents and chemical precursors, and biological materials, e.g. bacillus thuringiensis (BT) and bacillus globiggi (BG). In the detection of biological particulates, we are particularly interested in extending the detection range of an existing solar-blind 248-nm lidar system. We are investigating the use of longer excitation laser wavelengths (i.e. lambda greater than 280-nm to have more favorable atmospheric light transmission characteristics) for improving detection range to better than 10 km. In the detection of chemical plumes, our main research objectives are to determine how accurately and sensitively a chemical plume can be located at range, and how well spectrally the chemical species can be measured to allow their identification.

J J Tiee↗
NASA NTRS1993

Theoretical studies in interstellar cloud chemistry

This final report represents the completion of the three tasks under the purchase order no. SCPDE5620,1,2F. Chemical composition of gravitationally contracting, but otherwise quiescent, interstellar clouds and of interstellar clouds traversed by high velocity shocks, were modeled in a comprehensive manner that represents a significant progress in modeling these objects. The evolutionary chemical modeling, done under this NASA contract, represents a notable advance over the 'classical' fixed condition equilibrium models because the evolutionary models consider not only the chemical processes but also the dynamical processes by which the dark interstellar clouds may have assumed their present state. The shock calculations, being reported here, are important because they extend the limited chemical composition derivable from dynamical calculations for the total density and temperature structures behind the shock front. In order to be tractable, the dynamical calculations must severely simplify the chemistry. The present shock calculations take the shock profiles from the dynamical calculations and derive chemical composition in a comprehensive manner. The results of the present modeling study are still to be analyzed with reference to astronomical observational data and other contemporary model predictions. As far as humanly possible, this analysis will be continued with CRE's (Creative Research Enterprises's) IR&D resources, until a sponsor is found.

Chiu, Y. T.↗
NASA NTRS1997

Shuttle Fire Tests Are Radiant

Flame spreading is a phenomenon familiar to everyone who has witnessed an accidental fire. Yet, because of the complexity of the physical and chemical processes that are involved, the theoretical understanding of fires and flame spreading is a relatively new science. Flames spread along solid materials in a process where heat from the flames vaporizes the fuel just ahead of the moving flame. The vaporized fuel mixes with oxygen from the air and reacts chemically with it, producing the flame. On Earth, the spread rate of the flame is directly affected by the rate at which the fuel and oxygen are mixed with the help of buoyant convection.

Olson, Sandra L.↗
NASA NTRS2012

Alternative Green Solvents Project

Necessary for safe and proper functioning of equipment. Mainly halogenated solvents. Tetrachloride, Trichloroethylene (TCE), CFC-113. No longer used due to regulatory/safety concerns. Precision Cleaning at KSC: Small % of total parts. Used for liquid oxygen (LOX) systems. Dual solvent process. Vertrel MCA (decafluoropentane (DFP) and trons-dichloroethylene) HFE-7100. DFP has long term environmental concerns. Project Goals: a) Identify potential replacements. b) 22 wet chemical processes. c) 3 alternative processes. d) Develop test procedures. e) Contamination and cleaning. f) Analysis. g) Use results to recommend alternative processes. Conclusions: a) No alternative matched Vertrel in this study. b) No clear second place solvent. c) Hydrocarbons- easy; Fluorinated greases- difficult. d) Fluorinated component may be needed in replacement solvent. e) Process may need to make up for shortcoming of the solvent. f) Plasma and SCC02 warrant further testing.

Maloney, Phillip R.↗
NASA NTRS2013

Numerical Prediction of Dust

Covers the whole breadth of mineral dust research, from a scientific perspective Presents interdisciplinary work including results from field campaigns, satellite observations, laboratory studies, computer modelling and theoretical studies Explores the role of dust as a player and recorder of environmental change This volume presents state-of-the-art research about mineral dust, including results from field campaigns, satellite observations, laboratory studies, computer modelling and theoretical studies. Dust research is a new, dynamic and fast-growing area of science and due to its multiple roles in the Earth system, dust has become a fascinating topic for many scientific disciplines. Aspects of dust research covered in this book reach from timescales of minutes (as with dust devils, cloud processes, and radiation) to millennia (as with loess formation and oceanic sediments), making dust both a player and recorder of environmental change. The book is structured in four main parts that explore characteristics of dust, the global dust cycle, impacts of dust on the Earth system, and dust as a climate indicator. The chapters in these parts provide a comprehensive, detailed overview of this highly interdisciplinary subject. The contributions presented here cover dust from source to sink and describe all the processes dust particles undergo while travelling through the atmosphere. Chapters explore how dust is lifted and transported, how it affects radiation, clouds, regional circulations, precipitation and chemical processes in the atmosphere, and how it deteriorates air quality. The book explores how dust is removed from the atmosphere by gravitational settling, turbulence or precipitation, how iron contained in dust fertilizes terrestrial and marine ecosystems, and about the role that dust plays in human health. We learn how dust is observed, simulated using computer models and forecast. The book also details the role of dust deposits for climate reconstructions. Scientific observations and results are presented, along with numerous illustrations. This work has an interdisciplinary appeal and will engage scholars in geology, geography, chemistry, meteorology and physics, amongst others with an interest in the Earth system and environmental change.

dust↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS1983

Reduction, partial evaporation, and spattering - Possible chemical and physical processes in fluid drop chondrule formation

The major chemical differences between fluid drop chondrules and their probable parent materials may have resulted from the loss of volatiles such as S, H2O, Fe, and volatile siderophile elements by partial evaporation during the chondrule-forming process. Vertical access solar furnace experiments in vacuum and hydrogen have demonstrated such chemical fractionation trends using standard rock samples. The formation of immiscible iron droplets and spherules by in situ reduction of iron from silicate melt and the subsequent evaporation of the iron have been observed directly. During the time that the main sample bead is molten, many small spatter spherules are thrown off the main bead, thereby producing many additional chondrule-like melt spherules that cool rapidly and generate a population of spherules with size frequency distribution characteristics that closely approximate some populations of fluid drop chondrules in chondrites. It is possible that spatter-produced fluid drop chondrules dominate the meteoritic fluid drop chondrule populations. Such meteoritic chondrule populations should be chemically related by various relative amounts of iron and other volatile loss by vapor fractionation.

King, E. A.↗