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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

An in-situ conductometric apparatus for physicochemical characterization of solutions and in-line monitoring of separation processes at elevated temperatures and pressures

Specific conductance and frequency-dependent resistance (impedance) data are widely utilized for understanding the physicochemical characteristics of aqueous and non-aqueous fluids and for evaluating the performance of chemical processes. However, the implementation of such an in-situ probe in high-temperature and high-pressure environments is not trivial. This work provides a description of both the hardware and software associated with implementing a parallel-type in-situ electrochemical sensor. The sensor can be used for in-line monitoring of thermal desalination processes and for impedance measurements in fluids at high temperature and pressure. Further, a comparison between the experimental measurements on the specific conductance in aqueous sodium chloride solutions and the conductance model demonstrate that the methodology yields reasonable agreement with both the model and literature data. A combination of hardware components, a software-based correction for experimental artifacts, and computational fluid dynamics (CFD) calculations used in this work provide a sound basis for implementing such in-situ electrochemical sensors to measure frequency-dependent resistance spectra.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Determination of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids and Deep Eutectic Solvents by Factorization-Machine-Based Neural Networks

Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. Here, the model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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.↗
NASA NTRS1980

Materials processing in space, 1980 science planning document

The scientific aspects of the Materials Processing in Space program are described with emphasis on the major categories of interest: (1) crystal growth; (2) solidification of metals, alloys, and composites; (3) fluids and chemical processes; (4) containerless processing, glasses, and refractories; (5) ultrahigh vacuum processes; and (6) bioprocessing. An index is provided for each of these areas. The possible contributions that materials science experiments in space can make to the various disciplines are summarized, and the necessity for performing experiments in space is justified. What has been learned from previous experiments relating to space processing, current investigations, and remaining issues that require resolution are discussed. Recommendations for the future direction of the program are included.

Naumann, R. J.↗
NASA NTRS1983

Theoretical performance of a vortex generator type of cloud droplet sampler

One of the goals of the NASA global tropospheric research program is understanding the chemical processes which influence the chemistry of the natural troposphere. An important element of this program is the study of the role of heterogenous chemistry in global tropospheric chemical cycles. Any study of the part which clouds play in the chemistry of tropospheric trace constituents requires a determination of cloud water chemical composition. There exists, therefore, a need for highly efficient collector devices which can obtain unbiased samples of cloud water in sufficient quantities aboard an aircraft platform. A promising concept which could meet the dual requirements of accurate sampling and efficient collection of cloud water droplets in the vortex generator. This concept involves an inertial separator which uses the centrifugal force of a rotating airstream. The separator is built into a commercial device which has been used for many years as a dust separator for air breathing engines. Attention is given to the optimization of the sampler design.

Miller, I. M.↗
NASA NTRS1990

Thermal structure of neutral winds from young stellar objects

The physical processes that control the thermal structure of lightly ionized winds from cool protostars are discussed. Attention is concentrated on the hydrogen gas, and the heating, cooling, and chemical processes that affect the neutral and ionic species of atomic and molecular hydrogen are examined. Warm silicate dust may condense out of the cooling wind and may heat the gas through collisions. Singly ionized sodium atoms, which do not recombine for the mass-loss rates considered, set a lower limit to the ionization fraction in the wind. Magnetic fields, which are presumed to accelerate the wind, couple directly to the ionic component of the gas and transfer momentum and energy to the neutral component through collisions. This process of ambipolar diffusion is found to be the dominant source of heat input to the gas.

Ruden, Steven P.↗
NASA NTRS1993

Containerless automated processing of intermetallic compounds and composites

An automated containerless processing system has been developed to directionally solidify high temperature materials, intermetallic compounds, and intermetallic/metallic composites. The system incorporates a wide range of ultra-high purity chemical processing conditions. The utilization of image processing for automated control negates the need for temperature measurements for process control. The list of recent systems that have been processed includes Cr, Mo, Mn, Nb, Ni, Ti, V, and Zr containing aluminides. Possible uses of the system, process control approaches, and properties and structures of recently processed intermetallics are reviewed.

Johnson, D. R.↗