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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Impact of CGNS on CFD Workflow

CFD tools are an integral part of industrial and research processes, for which the amount of data is increasing at a high rate. These data are used in a multi-disciplinary fluid dynamics environment, including structural, thermal, chemical or even electrical topics. We show that the data specification is an important challenge that must be tackled to achieve an efficient workflow for use in this environment. We compare the process with other software techniques, such as network or database type, where past experiences showed how difficult it was to bridge the gap between completely general specifications and dedicated specific applications. We show two aspects of the use of CFD General Notation System (CGNS) that impact CFD workflow: as a data specification framework and as a data storage means. Then, we give examples of projects involving CFD workflows where the use of the CGNS standard leads to a useful method either for data specification, exchange, or storage.

Poinot, M.↗
NASA NTRS2007

Cutting Edge Technologies Presentation: An Overview of Developing Sensor Technology Directions and Possible Barriers to New Technology Implementation

The aerospace industry requires the development of a range of chemical sensor technologies for such applications as leak detection, emission monitoring, fuel leak detection, environmental monitoring, and fire detection. A range of chemical sensors are being developed based on micromachining and microfabrication technology to fabricate microsensors with minimal size, weight, and power consumption; and the use of nanomaterials and structures to develop sensors with improved stability combined with higher sensitivity, However, individual sensors are limited in the amount of information that they can provide in environments that contain multiple chemical species. Thus, sensor arrays are being developed to address detection needs in such multi-species environments. These technologies and technical approaches have direct relevance to breath monitoring for clinical applications. This presentation gives an overview of developing cutting-edge sensor technology and possible barriers to new technology implementation. This includes lessons learned from previous microsensor development, recent work in development of a breath monitoring system, and future directions in the implementation of cutting edge sensor technology.

Hunter, Gary W.↗
NASA NTRS2005

Development of GaN-based microchemical sensor nodes

Sensors based III-N technology are gaining significant interest due to their potential for monolithic integration of RF transceivers and light sources and the capability of high temperature operations. We are developing a GaN-based micro chemical sensor node for remote detection of chemical toxins, and present electrical responses of AlGaN/GaN HEMT (High Electron Mobility Transistor) sensors to chemical toxins as well as other common gases.

extreme environment↗
DOE OSTI2021

Simultaneous ambient pressure x-ray photoelectron spectroscopy and grazing incidence x-ray scattering in gas environments

We have developed an experimental system to simultaneously measure surface structure, morphology, composition, chemical state, and chemical activity for samples in gas phase environments. This is accomplished by simultaneously measuring x-ray photoelectron spectroscopy (XPS) and grazing incidence x-ray scattering in gas pressures as high as the multi-Torr regime while also recording mass spectrometry. Scattering patterns reflect near-surface sample structures from the nano-scale to the meso-scale, and the grazing incidence geometry provides tunable depth sensitivity of structural measurements. Scattered x rays are detected across a broad range of angles using a newly designed pivoting-UHV-manipulator for detector positioning. At the same time, XPS and mass spectrometry can be measured, all from the same sample spot and under ambient conditions. To demonstrate the capabilities of this system, we measured the chemical state, composition, and structure of Ag-behenate on a Si(001) wafer in vacuum and in O 2 atmosphere at various temperatures. These simultaneous structural, chemical, and gas phase product probes enable detailed insights into the interplay between the structure and chemical state for samples in gas phase environments. The compact size of our pivoting-UHV-manipulator makes it possible to retrofit this technique into existing spectroscopic instruments installed at synchrotron beamlines. Because many synchrotron facilities are planning or undergoing upgrades to diffraction limited storage rings with transversely coherent beams, a newly emerging set of coherent x-ray scattering experiments can greatly benefit from the concepts we present here.

47 OTHER INSTRUMENTATION↗
NASA NTRS1988

Environment assisted degradation mechanisms in advanced light metals

The general goals of the research program are to characterize alloy behavior quantitatively and to develop predictive mechanisms for environmental failure modes. Successes in this regard will provide the basis for metallurgical optimization of alloy performance, for chemical control of aggressive environments, and for engineering life prediction with damage tolerance and long term reliability.

Gangloff, Richard P.↗
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.↗
DOE OSTI2024

Microbial Reduction of Manganese Oxides From AMD Solids for the Biomining of Critical Minerals / Rare Earth Elements

Abandoned Coal-Mine Drainage (AMD) is a source of critical minerals (CMs) that can be biomined to increase domestic CM production for the growing tech industry while mitigating current AMD hazardous waste. Pennsylvania has ~11,000 abandoned mines, ~500 of which are currently being treated with passive remediation systems (PRS), designed to increase the pH and precipitate AMD waste onsite. Manganese (Mn) that precipitates from AMD waste can co-precipitate with CMs, and the Mn and CMs will accumulate on the PRS solids over the treatment period. This results in high Mn/CM solids that could produce a valuable leachate if resolubilized. Mn reduction and solubilization can occur through microbial driven geochemical changes such as the acidification of the environment, and it is expected that Mn resolubilization will lead to a co-resolubilization of the CMs. Investigation and stimulation of this microbial resolubilization could result in an affordable CM release process that does not require chemical additives into the environment. However the microbial mechanisms that contribute to Mn resolubilization are poorly understood. Here, we have isolated bacteria capable of resolubilizing Mn from AMD PRS. Five of the bacteria isolates have been identified as Bacillus spp. and two as Corynebacterium spp. that ferment glucose to reduce/solubilize Mn MnO2 by acidogenesis. The bacterial isolates are also capable of solubilizing Mn and CMs (Y, Zr, Sb, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu) from AMD PRS precipitated solids through their production of organic acids. Determining the microbial metabolism and genes involved in the resolubilization of manganese is crucial to optimize the biomining of CMs from AMD solid precipitants. This work addresses the growing need to develop novel CM recovery methods from domestic sources.

critical minerals↗
DOE OSTI2023

Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry

ABSTRACT Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to 1 m in depth from irrigated and planted field treatments and was analyzed using a suite of omics and chemical analyses. The soil microbial community composition was impacted more by irrigation and plant cover treatments than by soil depth. By contrast, metabolomes, lipidomes, and proteomes differed more with soil depth than treatments. Deep soil (>50 cm) had higher soil pH and calcium concentrations and higher levels of organic acids, bicarbonate, and triacylglycerides. By contrast, surface soil (0–5 cm) had higher concentrations of soil organic matter, organic carbon, oxidizable carbon, and total nitrogen. Surface soils also had higher amounts of sugars, sugar alcohols, phosphocholines, and proteins that reflect osmotic and oxidative stress responses. The lipidome was more responsive to perennial tall wheatgrass treatments compared to the metabolome or proteome, with a striking change in diacylglyceride composition. Permanganate oxidizable carbon was more consistently correlated to metabolites and proteins than soil organic and inorganic carbon and soil organic matter. This study reveals specific compounds that reflect differences in organic, inorganic, and oxidizable soil carbon fractions that are impacted by interactions between irrigation-supplied moisture and plant cover in calcareous soil profiles. IMPORTANCE Carbon is cycled through the air, plants, and belowground environment. Understanding soil carbon cycling in deep soil profiles will be important to mitigate climate change. Soil carbon cycling is impacted by water, plants, and soil microorganisms, in addition to soil mineralogy. Measuring biotic and abiotic soil properties provides a perspective of how soil microorganisms interact with the surrounding chemical environment. This study emphasizes the importance of considering biotic interactions with inorganic and oxidizable soil carbon in addition to total organic carbon in carbonate-containing soils for better informing soil carbon management decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Plant Microbiome: From Ecology to Reductionism and Beyond

Methodological advances over the past two decades have propelled plant microbiome research, allowing the field to comprehensively test ideas proposed over a century ago and generate many new hypotheses. Studying the distribution of microbial taxa and genes across plant habitats has revealed the importance of various ecological and evolutionary forces shaping plant microbiota. In particular, selection imposed by plant habitats strongly shapes the diversity and composition of microbiota and leads to microbial adaptation associated with navigating the plant immune system and utilizing plant-derived resources. Reductionist approaches have demonstrated that the interaction between plant immunity and the plant microbiome is, in fact, bidirectional and that plants, microbiota, and the environment shape a complex chemical dialogue that collectively orchestrates the plantmicrobiome. Finally, the next stage in plant microbiome research will require the integration of ecological and reductionist approaches to establish a general understanding of the assembly and function in both natural and managed environments.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Metastable Eutectic Equilibrium in Natural Environments: Recent Development and Research Opportunities

Chemical ordering at metastable eutectics was recognized in non-equilibrium gas-to- solid condensation experiments to constrain 'silicate' dust formation in O-rich circumstellar environments. The predictable metastable eutectic behavior successfully predicted the observed ferromagnesiosilica compositions of circumstellar dust presolar and solar nebula grains in the matrix of the collected aggregate IDPs (Interplanetary Dust Particles). Many of the experimentally determined metastable eutectic solids match the fundamental building blocks of common rock-forming layer silicates: this could have implications for the origin of Life. The physical conditions conducive to metastable eutectic behavior, i.e. high temperature and (ultra) fast quenching, lead to unique amorphous, typically nano- to micrometer-sized, materials. The new paradigm of metastable eutectic behavior opens the door to new and exciting research opportunities in uncovering the many implications of these unique amorphous, and typically nano-to micrometer-sized, metastable eutectic materials.

Rietmeijer, Frans J. M.↗
NASA NTRS2000

Metastable Eutectic Equilibrium in Natural Environments: Recent Developments and Research Opportunities

Chemical ordering at metastable eutectics was recognized in non-equilibrium gas-to- solid condensation experiments to constrain 'silicate' dust formation in O-rich circumstellar environments. The predictable metastable eutectic behavior successfully predicted the observed ferromagnesiosilica, compositions of circumstellar dust, presolar and solar nebula grains in the matrix of the collected aggregate IDPs. Many of the experimentally determined metastable eutectic solids match the fundamental building blocks of common rock-forming layer silicates: this could have implications for the origin of Life. The physical conditions conducive to metastable eutectic behavior, i.e. high temperature and (ultra)fast quenching, lead to unique amorphous, typically nano- to micrometer-sized, materials. The new paradigm of metastable eutectic behavior opens the door to new and exciting research opportunities in uncovering the many implications of these unique amorphous and typically nano- to micrometer-sized, metastable eutectic materials.

Rietmeijer, Fans J. M.↗
NASA NTRS1989

Solar nebula condensates and the composition of comets

Interpretation of the volatile abundances in Halley's comet in terms of models for chemical and physical processes in the solar nebula are discussed. Key ratios of the oxidized and reduced species of nitrogen and carbon are identified which tell something of the chemical history of the environment in which cometary grains accreted to form the nucleus. Isotopic abundances are also applied to this problem. It will be shown that the abundances of methane and carbon monoxide are consistent both with models of solar nebula chemistry and chemical processing on grains in star-forming regions. Ultimately, limitations of the current data set on molecular abundances in comets and star-forming regions prevent a definitive choice between the two. Processes important to the composition of outer solar system bodies are: (1) gas phase chemistry in the solar nebula; (2) imperfect mixing in the solar nebula; (3) condensation; (4) clathration; (5) adsorption; and (6) processing of interstellar material.

Lunine, J. I.↗
NASA NTRS1989

Environment assisted degradation mechanisms in advanced light metals

A multifaceted research program on the performance of advanced light metallic alloys in aggressive aerospace environments, and associated environmental failure mechanisms was initiated. The general goal is to characterize alloy behavior quantitatively and to develop predictive mechanisms for environmental failure modes. Successes in this regard will provide the basis for metallurgical optimization of alloy performance, for chemical control of aggressive environments, and for engineering life prediction with damage tolerance and long term reliability.

Gangloff, R. P.↗
NASA NTRS2002

Development of Microfabricated Chemical Gas Sensors and Sensor Arrays for Aerospace Applications

Aerospace applications require the development of chemical sensors with capabilities beyond those of commercially available sensors. In particular, factors such as minimal sensor size, weight, and power consumption are particularly important. Development areas which have potential aerospace applications include launch vehicle leak detection, engine health monitoring, fire detection, and environmental monitoring. Sensor development for these applications is based on progress in three types of technology: 1) Micromachining and microfabrication (Microsystem) technology to fabricate miniaturized sensors. 2) The use of nanocrystalline materials to develop sensors with improved stability combined with higher sensitivity. 3) The development of high temperature semiconductors, especially silicon carbide. However, due to issues of selectivity and cross-sensitivity, individual sensors are limited in the amount of information that they can provide in environments that contain multiple chemical species. Thus, sensor arrays are being developed to address detection needs in such multi-species environments. This paper discusses the needs of space applications as well as the point-contact sensor technology and sensor arrays being developed to address these needs. Sensors to measure hydrogen, hydrocarbons, hydrazine, nitrogen oxides (NO,), carbon monoxide, oxygen, and carbon dioxide are being developed as well as arrays for leak, fire, and emissions detection. Demonstrations of the technology will also be discussed. It is concluded that microfabricated sensor technology has significant potential for use in a range of aerospace applications.

Hunter, G. W.↗
DOE OSTI2020

The Sensitivity of Chemical Kinetic Models on Flame Transfer Functions in Acoustic Fluctuation Environments with Varying Equivalence Ratios

Various factors affect the heat release response of flames – thermodynamics, hydrodynamics, acoustics, etc. The coupling of heat release rate and acoustics, in particular, is essential to the design of combustion devices so as to avoid adverse effects during their operation. Modeling flames in acoustic environments requires an accurate understanding of the fundamental physics that occurs when flames interact with a fluctuating pressure field. Over the past couple of decades, researchers have been quantifying the impact of this fluctuating pressure field on the unsteady heat release rate. The flame transfer functions computed have also been shown to depend on the level of detail in the chemical kinetic model. This paper attempts to bridge the gap between these studies to understand how sensitive the flame transfer function is to various kinetic models. The problem is studied by performing multiple one-dimensional simulations using the low-Mach solver NGA. The detailed GRI-Mech 3.0 will be used for comparison. Also, the pressure amplitude and reactant equivalence ratio are varied to understand how the environment affects this sensitivity.

Girdhar, Anant↗
DOE OSTI2023

Carbon-Assisted, Continuous Syngas Production in a Chemical Looping Scheme

In the current energy and environment scenario, it is imperative to develop energy efficient routes for chemical manufacturing that also pave the way for mitigation of greenhouse gas emissions. This work presents an efficient pathway for continuous syngas production via a chemical looping conversion of the two most potent greenhouse gases—CH 4 , and CO 2 . The well-known dry-reforming process of converting CH 4 , and CO 2 to syngas is energy-intensive and suffers from catalyst deactivation. The chemical looping approach, on the other hand, provides avenues for mitigating catalyst deactivation and enabling improved energy efficiency. The key to such process enhancements lies in the intricate structure–function relationships of the catalyst and its correlation to the process variables. We present the reduction and oxidation characteristics of 5 wt.% Ni/Ce 1-x Zr x O 2 -based catalysts (x = 0, 0.4, and 0.625). We demonstrate low temperature CH4 activation over Ni-promoted samples as opposed to pure Ce 1-x Zr x O 2 . Moreover, our results depict an optimum regeneration of these catalysts when oxidized by CO 2 , and H 2 O, which allows for chemical looping operation of steam reforming of methane as well. Process variables were tuned to optimize the CH 4 conversion (over 80%), and H 2 /CO ratio at 650 °C. The critical surface reactions—carbon accumulation and gasification, and thermocatalytic CO 2 splitting were investigated to elucidate the dynamic nature of the catalyst surface. In conclusion, the impact of this work lies in showcasing the opportunities to design chemical looping reactors for energy efficient syngas production from waste greenhouse gases.

25 ENERGY STORAGE↗