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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Laboratory Studies of the Reactive Chemistry and Changing CCN Properties of Secondary Organic Aerosol, Including Model Development

The chemical evolution of secondary-organic-aerosol (SOA) particles and how this evolution alters their cloud-nucleating properties were studied. Simplified forms of full Koehler theory were targeted, specifically forms that contain only those aspects essential to describing the laboratory observations, because of the requirement to minimize computational burden for use in integrated climate and chemistry models. The associated data analysis and interpretation have therefore focused on model development in the framework of modified kappa-Koehler theory. Kappa is a single parameter describing effective hygroscopicity, grouping together several separate physicochemical parameters (e.g., molar volume, surface tension, and van't Hoff factor) that otherwise must be tracked and evaluated in an iterative full-Koehler equation in a large-scale model. A major finding of the project was that secondary organic materials produced by the oxidation of a range of biogenic volatile organic compounds for diverse conditions have kappa values bracketed in the range of 0.10 +/- 0.05. In these same experiments, somewhat incongruently there was significant chemical variation in the secondary organic material, especially oxidation state, as was indicated by changes in the particle mass spectra. Taken together, these findings then support the use of kappa as a simplified yet accurate general parameter to represent the CCN activation of secondary organic material in large-scale atmospheric and climate models, thereby greatly reducing the computational burden while simultaneously including the most recent mechanistic findings of laboratory studies.

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

GOES infrared study of tornadic storms

Enhanced convection-initiated gravity waves associated with an isolated tornado in the absence of a squall line are investigated. Ray tracing computations based on data observed on May 29, 1977 indicated that the wave sources were located in north central Oklahoma. Comparison with a radar echo map during the time period when the waves were excited showed that the waves were generated by an isolated cloud with enhanced convection. GOES infrared digital data during the time period from wave excitation to tornado touchdown were analyzed. Results showed that the cloud where the gravity waves were excited was characterized by both a very low temperature at the cloud top and a very high expansion rate of the cold cloud tops area. The lead time between the excitation of the gravity waves and the tornado touchdown is discussed in conjunction with the growth rate of the clouds associated with the tornado.

Hung, R. J.↗
NASA NTRS1990

Studies of stratospheric particulates

A sophisticated computer model of polar stratospheric clouds was developed and used to study the properties of ice clouds. The model has recently been extended to investigate nitric acid clouds and ice clouds as well as their interactions with stratospheric gases. The model is now being applied to interpret data collected during recent expeditions to the Antarctic and the Arctic. Some work has also been done to understand the properties of noctilucent clouds and their implications for the chemistry and dynamics of the upper stratosphere.

Toon, Owen B.↗
DOE OSTI2020

A Multi-Instrument Cloud Condensation Nuclei Spectrum Product (Final Technical Report)

A wealth of observational data exists on the characteristics of atmospheric particulate matter, over multiple years, at the DOE ARM Southern Great Plains (SGP) Central Facility site. This site is located in a region of the country that frequently experiences weather extremes, and that is removed from many local sources of pollution but is affected by transported smoke, dust, and urban emissions. The relationships between particulate matter, cloud formation and evolution, and precipitation are therefore of strong interest, and are being explored via modeling on a variety of scales. These models require as input detailed information on the characteristics of particles capable of serving as the nuclei for cloud formation. Sufficient data exist to be able to put together a picture of the nature of the total aerosol and the cloud condensation nuclei (CCN) subset, and their variability, through merged data products. This study was aimed at exploiting the multiple measurement types at SGP to develop the first such multi-year estimates. Further, the resulting data were analyzed to understand temporal patterns ranging from hourly to seasonal, thereby gaining insights into the particle sources affecting the atmosphere in this region. DOE-funded datasets that were analyzed in this study include total particle number concentrations, submicron aerosol scattering coefficients, dry aerosol size distributions, and more recently, time-resolved submicron aerosol chemical composition. Data are also available for the number concentrations of particles that are activated in a cloud condensation nucleus instrument at a series of setpoint supersaturations, providing direct observations of the number concentrations of “CCN”. This variable is the quantity that is generally desired for inclusion in numerical models that seek to represent and predict the impacts of varying aerosol characteristics on the formation and microphysical properties of clouds. One limitation of the use of direct CCN observations is that they are not available for supersaturations larger than about 1%, which is insufficient for deep convection and may be insufficient even for shallow convection, depending on the nature of the available CCN and the dynamics of the cloud. We developed a data-based approach to representing the full aerosol size spectrum with size-dependent hygroscopicity, and used this to extrapolate CCN spectra beyond the limited measurements. Five years of SGP aerosol data (2009 -2013) were analyzed. As a side product of our work, we identified and communicated several previously-unflagged data quality issues. The resulting merged aerosol distributions, along with fits for seasonal averages, were published and submitted to the ARM archive as a special value-added product (VAP; submitted as a PI product). CCN spectra were computed for the same data period and will similarly be published and submitted to the archive for use by the community. We also note that our methodologies and findings have been discussed at several Joint ARM User Facility/Atmospheric System Research (ASR) Principal Investigators Meetings and that recent ARM/ASR aerosol data reporting strategies have included similar ideas for data merging, indicating that this work has had a lasting impact on ARM aerosol data acquisition and reporting. The proposed work advances the science of the interactions of aerosols, clouds and precipitation, with direct application to improve representation of such interactions for clouds in regional and global climate models. The archived data will continue to serve research studies in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Scientific Data Management Center for Enabling Technologies

Managing scientific data has been identified by the scientific community as one of the most important emerging needs because of the sheer volume and increasing complexity of data being collected. Effectively generating, managing, and analyzing this information requires a comprehensive, end-to-end approach to data management that encompasses all of the stages from the initial data acquisition to the final analysis of the data. Fortunately, the data management problems encountered by most scientific domains are common enough to be addressed through shared technology solutions. Based on community input, we have identified three significant requirements. First, more efficient access to storage systems is needed. In particular, parallel file system and I/O system improvements are needed to write and read large volumes of data without slowing a simulation, analysis, or visualization engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
DOE OSTI2017

Computing and visualizing time-varying merge trees for high-dimensional data

We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Design of Distributed Cyber-Physical Systems for Connected and Automated Vehicles with Implementing Methodologies

With the development of communication and control technology, intelligent transportation systems have received increasing attention from both industry and academia. Intelligent transportation systems are supported by the Internet of Things, Cyber-Physical System, Artificial Intelligence, Cloud Computing and many other technologies, which supply fundamental information for connected and automated vehicles. Although plenty of studies have provided different formulations for intelligent transportation systems, many of them depend on Master Control Center. However, a centralized control mode requires a huge amount of data transmission and high level of hardware configuration and may cause communication delay and privacy leak. Some distributed architectures have been proposed to overcome the above problems but systematized technologies to collect and exchange information, process large amounts of data, model the dynamics of vehicles, and safely control the connected and automated vehicles are not explored in detail. In this paper, we proposed a novel distributed cyber-physical system for connected and automated vehicles in which every vehicle is modeled as a double-integrator using edge computing to analyze information collected from its nearest neighbors. The vehicles are supposed to travel along a desired trajectory and to maintain a rigid formation geometry. Related methodologies for the proposed system are illustrated and experiments are conducted showing that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CoreMS AutoQC Uploader

The invention is a self contained software utility that is deployed on the computer controlling a mass spectrometer. The purpose of the software is to monitor a given directory for files matching a user specified criteria and automatically upload matching files to a remote server as well as trigger a request that the data be processed by the cloud based CoreMS software

Rabus, Jordan [Pacific Northwest National Laborato↗
DOE OSTI2023

Evaluation of Historic Nuclear Cloud Measurement for Entrainment Parameter Determination

We evaluated the uncertainties associated with the entrainment parameters computed from nuclear cloud measurements taken during historic weapon testing campaigns. This evaluation found that variations in the way cloud heights and diameters were defined during the analysis of film records result in very large uncertainties in the entrainment parameters, severely limiting the utility of these data. For the measurements of atmospheric conditions at the time of the tests, the main sources of error are associated with the differences in the location and timing of the soundings.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1980

Gravity waves and GOES IR data study of an isolated tornadic storm on 29 May 1977

Enhanced convection-initiated gravity waves associated with an isolated tornado in the absence of a squall line are investigated. Ray-tracing computations based on data observed on May 29, 1977 indicated that the wave sources were located in north-central Oklahoma. Comparison with a radar echo map during the time period when the waves were excited showed that the waves were generated by an isolated cloud with enhanced convection. GOES infrared digital data during the time period from wave excitation to tornado touchdown were analyzed. Results showed that the cloud where the gravity waves were excited was characterized by both a very low temperature at the cloud top and a very high expansion rate of the cold cloud-top area. The lead time between the excitation of the gravity waves and the tornado touchdown is discussed in conjunction with the growth rate of the clouds associated with the tornado.

Hung, R. J.↗
NASA NTRS1990

Atmospheric radiative flux divergence from Clouds and Earth Radiant Energy System (CERES)

A major objective of the Clouds and Earth Radiant Energy System (CERES) is the computation of vertical profiles through the atmosphere of the divergence of radiation flux, with global coverage. This paper discusses the need for radiation divergence and presents some options for its inference from CERES measurements and other data from the Earth Observating System.

Smith, Louis G.↗
NASA NTRSDate not supplied

Use NASA Earthdata in the Cloud

With the impending arrival of new, high-data-volume Earth observation missions, NASA’s ability to effectively ingest, process, and archive large amounts of data requires the most cost-effective, flexible, and scalable data-management architectures and technologies. To meet these demands, NASA's Earth Science Data Systems (ESDS) Program is implementing a strategic vision to develop and operate multiple components of NASA's Earth Observing System Data and Information System (EOSDIS) in a commercial cloud environment. As more earth observing data is moved into the cloud, NASA EOSDIS wants to share our reasoning for moving data into the commercial cloud, as well as demonstrate how end-users can take advantage of both data in the cloud and cloud-deployed services. This presentation explains the enterprise reasons for moving data into the cloud, specifically the scalability of cloud systems, the flexibility to pivot to the most optimal language/system/architecture, and the reduced redundancy of a common architecture. Then we primarily focus on the user-driven reasons for moving to the cloud. Equal access to high performance computing helps all parties, especially users that don't have access to their own on-premise computing infrastructure. Rapid deployment allows users to spin up their own systems without needing the extensive platform previously required for large scale development. Cost effectiveness allows users a wide breadth of computing options, from super computer ability to small scale development, which can help underserved communities. Finally we link to internal resources that users can utilize to begin their cloud based development journey without having to be an expert in the field.

Nicholas Doty↗
DOE OSTI2017

Distributed wireless sensing for methane leak detection technology

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Edge-computing with enhanced standoff sensors

A system senses analytes through one or more sensors that detect or measure a physical characteristic. The one or more sensor generate a spectroscopic-data signal corresponding to the detection. An edge device communicatively couples the one or more sensors that communicatively couples a wide-area network coupling a cloud service. The edge device includes a data acquisition device that receives spectroscopic data signals from the one or more sensor and a processor that processes the spectroscopic-data signals to identify an analyte. The edge device also includes a transceiver that transmits data identifying the analytes to the cloud service.

Passian, Ali↗
DOE OSTI2020

Cloud based tool for analysis of chemical kinetic mechanisms

The size and complexity of chemical kinetic models used to simulate combustion of hydrocarbon fuels continue to increase with our improved understanding of the underlying physical mechanisms, along with the improved ability to utilize such models as the result of increased computational power and efficiency. As mechanisms grow beyond thousands of species and tens of thousands of reactions, it becomes increasingly difficult to manually check for errors and physical inconsistencies. In this work, we present several automated methods to check for such issues in the specification of chemical reaction models. First, we demonstrate how discontinuities in thermodynamic data can cause simulation difficulties manifested as long wall-clock times and convergence failures. To correct this type of problem, we describe an automated method for refitting thermodynamic parameters. We also outline several methods to check the timescales of reaction rate coefficients to ensure physical consistency. All the methods are made available through a web-based tool (https://combustiontools.llnl.gov) intended to aid mechanism developers and users to improve the accuracy and performance of fuel models used by the combustion community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Icing Physics Studies Using the 3D SIDRM Test Article: Aerodynamic and Supercooled Liquid Icing Analysis

In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation capability. Experimental data is required for development of icing physics models and simulation validation. To that end, this paper presents the analysis of the supercooled liquid icing data subset from tests conducted in 2022 at the NASA Icing Research Tunnel that studied both supercooled water and ice-crystal icing. The test article that was utilized replicated 3D geometrical features of an inter-compressor duct and strut region of a turbofan engine. The surfaces of the Simulated Inter-compressor Duct Research Model (SIDRM) can be heated to simulate the warm surfaces of the turbofan inter-compressor duct. The test article is instrumented with pressure taps, heaters, heat flux gauges, and thermocouples, while a 3D laser scanner, cameras, and a scale to measure ice mass were utilized to characterize the icing behavior. The aim of these tests was to generate ice accretions on the SIDRM test article under well-characterized supercooled liquid icing and ice crystal icing conditions. This paper discusses measurements related to aerodynamic testing and supercooled liquid icing tests that were conducted. Aerodynamic measurements were analyzed and compared to computational simulations and were found to be in good agreement for the range of airspeeds (50 to 230 knots) and angles of attack (0 to 4°) tested. Various parametric sweeps were conducted during the supercooled liquid icing portion of the test entry (cloud median volumetric diameter ranged from 15 to 90 µm, total air temperature from -3 to -17 °C, angle of attack from 0 to 4°, and accretion time from 5 to 20 min). These sweeps were performed to measure that parameter’s impact on ice accretion size, location (icing extent), characteristics (such as glaze/rime ice and shedding behavior), and test article surface temperature. Analysis of the test data showed that clouds composed of larger drops, colder air temperatures, smaller angles of attack, and longer spray times were the primary parameters that resulted in accretions with greater ice mass. Test article angle of attack and cloud droplet size influenced the location of ice accretion as these two parameters directly impact collection efficiency. With respect to icing characteristics, total air temperature dictated icing type, and smaller cloud drop size along with warmer air temperatures resulted in greater amounts of ice shedding. Surface temperature increased during ice accretion from the release of latent (fusion) heat, where total air temperature and cloud drop size impacted the amount of surface temperature change. The icing measurements collected during the SIDRM tests will be used to develop and validate 3D computational engine icing tools, such as GlennICE, that predictively assesses the onset and growth of ice. One of the goals of the sponsoring NASA project is to develop simulation models and tools that can assist in the design and certification of engines for flight in icing conditions in a cost effective way.

supercooled liquid icing↗
NASA NTRSDate not supplied

Icing Physics Studies Using the 3D SIDRM Test Article: Aerodynamic and Supercooled Liquid Icing Analysis

In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation capability. Experimental data is required for development of icing physics models and simulation validation. To that end, this paper discusses analyzed data from icing tests conducted with a 3D test article in 2022 at the NASA Icing Research Tunnel. The test article, whose geometry is representative of an inter-compressor duct and strut region of a turbofan engine, has been designed and constructed to study the physics of supercooled water icing and ice crystal icing. The surfaces of the Simulated Inter-compressor Duct Research Model (SIDRM) can be heated to simulate the warm surfaces of the turbofan inter-compressor duct. The test article is instrumented with pressure taps, heaters, heat flux gauges, and thermocouples, while a 3D laser scanner, cameras, and a scale to measure ice mass were utilized to characterize the icing behavior. The aim of these tests was to generate ice accretions on the SIDRM test article under well-characterized supercooled liquid icing and ice crystal icing conditions. This paper discusses measurements related to aerodynamic tests and supercooled liquid icing tests that were conducted. Aerodynamic measurements were analyzed and compared to computational simulations and were found to be in good agreement for the range of airspeeds (50 to 230 knots) and angles of attack (0 to 4°) tested. Various parametric sweeps were conducted during the supercooled liquid icing portion of the test entry (cloud median volumetric diameter ranged from 15 to 90 µm, total air temperature from -3 to -17 °C, angle of attack from 0 to 4°, and accretion time from 5 to 20 min). These sweeps were performed to measure that parameter’s impact on ice accretion size, location, characteristics (such as glaze/rime ice and shedding behavior), and test article surface temperature. Analysis of the test data showed that clouds composed of larger drops, colder air temperatures, smaller angles of attack, and longer spray times were the primary parameters that resulted in accretions with greater ice mass. Test article angle of attack and cloud droplet size influenced the location of ice accretion as these two parameters directly impact collection efficiency. With respect to icing characteristics, total air temperature dictated icing type, and smaller cloud drop size along with warmer air temperatures resulted in greater amounts of ice shedding. Surface temperature increased during ice accretion from the release of latent (fusion) heat, where total air temperature and cloud drop size impacted the amount of surface temperature change. The icing measurements collected during the SIDRM tests will be used to develop and validate 3D computational engine icing tools, such as GlennICE, that predictively assesses the onset and growth of ice. One of the goals of the sponsoring NASA project is to develop simulation models and tools that can assist in the design and certification of engines for flight in icing conditions in a cost‑effective way.

supercooled liquid icing↗