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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

A Quasi-Laue Neutron Crystallographic Study of D-Xylose Isomerase

Hydrogen atom location and hydrogen bonding interaction determination are often critical to explain enzymatic mechanism. Whilst it is difficult to determine the position of hydrogen atoms using X-ray crystallography even with subatomic (less than 1.0 Angstrom) resolution data available, neutron crystallography provides an experimental tool to directly localise hydrogeddeuteriwn atoms in biological macromolecules at resolution of 1.5-2.0 Angstroms. Linearisation and isomerisation of xylose at the active site of D-xylose isomerase rely upon a complex hydrogen transfer. Neutron quasi-Laue data were collected on Streptomyces rubiginosus D-xylose isomerase crystal using the LADI instrument at ILL with the objective to provide insight into the enzymatic mechanism (Myles et al. 1998). The neutron structure unambiguously reveals the protonation state of His 53 in the active site, identifying the model for the enzymatic pathway.

Meilleur, Flora↗
DOE OSTI2021

A review of modelling techniques for floating offshore wind turbines

Modelling floating offshore wind turbines (FOWTs) is challenging due to the strong coupling between the aerodynamics of the turbine and the hydrodynamics of the floating platform. Physical testing at scale is faced with the additional challenge of the scaling mismatch between Froude number and Reynolds number due to working in the two fluid domains, air and water. In the drive for cost-reduction of floating wind energy, designers may be seeking to move towards high-fidelity numerical modelling as a substitute for physical testing. However, the numerical engineering tools typically used for FOWT modelling are considered as mid-fidelity to low-fidelity tools, and currently lack the level of accuracy required to do so. Furthermore, there is a lack of operational FOWT data available for further development and validation. High-fidelity tools, such as CFD, have greater accuracy but are cumbersome tools and still require validation. Physical scale model testing therefore continues to play an essential role in the development of FOWTs both as a source of validation data for numerical models and as an important development step along the path to commercialization of all platform concepts. The aim of this paper is to provide an overview of both numerical modelling and physical FOWT scale model testing approaches and to provide guidance on the selection of the most appropriate approach (or combination of approaches). The current state-of-the-art will be discussed along with current research trends and areas for further investigation.

17 WIND ENERGY↗
NASA NTRS1974

Venus cloud properties: Infrared opacity and mass mixing ratio

By using the Mariner 5 temperature profile and a homogeneous cloud model, and assuming that CO2 and cloud particles are the only opacity sources, the wavelength dependence of the Venus cloud opacity is inferred from the infrared spectrum of the planet between 450 and 1250/cm. Justification for applying the homogeneous cloud model is found in the fact that numerous polarization and infrared data are mutually consistent within the framework of such a model; on the other hand, dense cloud models are not satisfactory. Volume extinction coefficients varying from 0.000005 to 0.000015/cm depending on the wavelength, are determined at the tropopause level of 6110 km. By using all available data, a cloud mass mixing ratio of approximately 0.000005 and a particle concentration of about 900 particles per cu cm at this level are also inferred. The derived cloud opacity compares favorably with that expected for a haze of droplets of a 75% aqueous solution of sulfuric acid.

Samuelson, R. E.↗
NASA NTRS1992

A physical split window technique for the retrieval of precipitable water from satellite measurements

The split-window precipitable water algorithm developed by Jedlovec (1987) is described, and its advantages are examined. In particular, it is noted that the algorithm can be applied to multispectral imaging data, available half-hourly from VAS; it uses only limited spatial averaging and can be applied to instruments that lack sounding channels. Here, previous work is reviewed, and the procedure is extended to the Advanced Very High Resolution Radiometer (AVHRR) and GOES-NEXT using simulated data. Root mean square errors for the simulated and actual retrievals are below +/- 5.8 mm. Of the instruments examined, AVHRR appears to have the greatest potential, with the rms errors below +/- 2.3 mm.

Guillory, Anthony R.↗
NASA NTRS1944

Design of power-plant installations pressure-loss characteristics of duct components

A correlation of what are believed to be the most reliable data available on duct components of aircraft power-plant installations is presented. The information is given in a convenient form and is offered as an aid in designing duct systems and, subject to certain qualifications, as a guide in estimating their performance. The design and performance data include those for straight ducts; simple bends of square, circular, and elliptical cross sections; compound bends; diverging and converging bends; vaned bends; diffusers; branch ducts; internal inlets; and an angular placement of heat exchangers. Examples are included to illustrate methods of applying these data in analyzing duct systems. (author)

Henry, John R↗
NASA NTRS2012

Generating Ground Reference Data for a Global Impervious Surface Survey

We are engaged in a project to produce a 30m impervious cover data set of the entire Earth for the years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. The GLS data from Landsat provide an unprecedented opportunity to map global urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such as buildings, roads and parking lots. Finally, with GLS data available for the 1975, 1990, 2000, and 2005 time periods, and soon for the 2010 period, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. Our approach works across spatial scales using very high spatial resolution commercial satellite data to both produce and evaluate continental scale products at the 30m spatial resolution of Landsat data. We are developing continental scale training data at 1m or so resolution and aggregating these to 30m for training a regression tree algorithm. Because the quality of the input training data are critical, we have developed an interactive software tool, called HSegLearn, to facilitate the photo-interpretation of high resolution imagery data, such as Quickbird or Ikonos data, into an impervious versus non-impervious map. Previous work has shown that photo-interpretation of high resolution data at 1 meter resolution will generate an accurate 30m resolution ground reference when coarsened to that resolution. Since this process can be very time consuming when using standard clustering classification algorithms, we are looking at image segmentation as a potential avenue to not only improve the training process but also provide a semi-automated approach for generating the ground reference data. HSegLearn takes as its input a hierarchical set of image segmentations produced by the HSeg image segmentation program [1, 2]. HSegLearn lets an analyst specify pixel locations as being either positive or negative examples, and displays a classification of the study area based on these examples. For our study, the positive examples are examples of impervious surfaces and negative examples are examples of non-impervious surfaces. HSegLearn searches the hierarchical segmentation from HSeg for the coarsest level of segmentation at which selected positive example locations do not conflict with negative example locations and labels the image accordingly. The negative example regions are always defined at the finest level of segmentation detail. The resulting classification map can be then further edited at a region object level using the previously developed HSegViewer tool [3]. After providing an overview of the HSeg image segmentation program, we provide a detailed description of the HSegLearn software tool. We then give examples of using HSegLearn to generate ground reference data and conclude with comments on the effectiveness of the HSegLearn tool.

Tilton, James C.↗
NASA NTRS2018

Predicting the Operational Acceptance of Airborne Flight Reroute Requests Using Data Mining

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.

Operational Acceptability↗
NASA NTRS2017

Comparing Proxy and Model Estimates of Hydroclimate Variability and Change over the Common Era

Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited because of a paucity of modern instrumental observations that are distributed unevenly across the globe and only span parts of the 20th and 21st centuries. Such data coverage is insufficient for characterizing hydroclimate and its associated dynamics because of its multidecadal to centennial variability and highly regionalized spatial signature. High-resolution (seasonal to decadal) hydroclimatic proxies that span all or parts of the Common Era (CE) and paleoclimate simulations from climate models are therefore important tools for augmenting our understanding of hydroclimate variability. In particular, the comparison of the two sources of information is critical for addressing the uncertainties and limitations of both while enriching each of their interpretations. We review the principal proxy data available for hydroclimatic reconstructions over the CE and highlight the contemporary understanding of how these proxies are interpreted as hydroclimate indicators. We also review the available last-millennium simulations from fully coupled climate models and discuss several outstanding challenges associated with simulating hydroclimate variability and change over the CE. A specific review of simulated hydroclimatic changes forced by volcanic events is provided, as is a discussion of expected improvements in estimated radiative forcings, models, and their implementation in the future. Our review of hydroclimatic proxies and last-millennium model simulations is used as the basis for articulating a variety of considerations and best practices for how to perform proxy-model comparisons of CE hydroclimate. This discussion provides a framework for how best to evaluate hydroclimate variability and its associated dynamics using these comparisons and how they can better inform interpretations of both proxy data and model simulations.We subsequently explore means of using proxy-model comparisons to better constrain and characterize future hydroclimate risks. This is explored specifically in the context of several examples that demonstrate how proxy-model comparisons can be used to quantitatively constrain future hydroclimatic risks as estimated from climate model projections.

Common Era↗
DOE OSTI2023

Weather Data from BSEC Weather Stations

This dataset provides hourly measurements of temperature, humidity, rainfall, wind, and sunlight at Ambient Weather and OttHydro stations across Baltimore city. These surface weather stations were deployed by the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (UIFL) project, funded by the Department of Energy (DOE). This dataset will be periodically updated to include more stations and recent observations when available. Data File Information This dataset contains surface weather measurements data in comma-separated value (CSV) format and documents that describe the weather stations, locations, and measured parameters and units. data/BSEC-[STATIONID]_[SENSORTYPE]_hourly_[YEAR].csv Surface weather measurements data in CSV format, where STATIONID indicates the weather station, SENSORTYPE is the type of weather station ('AWS' = Ambient Weather Station and 'OTT' = 'OttHydro Station'), and YEAR indicate the year in which the measurements were made. Example data file name: BSEC-AAC_AWS_hourly_2023.csv. documents/Station_Locations.csv This CSV file provides location information and measurement start date for each surface weather station. documents/Weather_Station_Descriptions.pdf This document provides detailed description of the instruments along with their setup and accuracy of measurement. documents/File_Parameters.pdf This document describes the surface weather parameters and units of measurement.

Ambient Weather Stations↗
DOE OSTI2025

LASSO for CALPHAD Model Selection Enables Data-Efficient Thermodynamic Modeling: An Application in Thermochemical Hydrogen Production Materials

Phenomenological CALPHAD (CALculation of PHAse Diagrams) models, widely used for multicomponent materials, often contain a considerable number of parameters and require fitting using data from a relatively small number of experimental measurements or theoretical calculations. Sometimes these parameters are introduced for the purpose of improving model fits but without clear physical justification, which leads to overparametrized models with poor generalization performance. Automated approaches for optimal model selection based on the available data therefore become critical. Here, in this work, a least absolute shrinkage and selection operator (LASSO)-based approach is developed for model selection by leveraging the linearity of the CALPHAD model with respect to its parameters to convert the model selection and fitting to a LASSO minimization problem. We demonstrate its utility for thermodynamic modeling of thermochemical hydrogen (TCH) production materials using lanthanum strontium manganite (LSM) as an example. Various TCH-relevant properties, including oxygen stoichiometry as a function of oxygen partial pressure, enthalpy of reduction, and entropy of reduction, are successfully predicted with reasonable accuracy using a minimal set of model parameters. Importantly, the model selection and fitting involve minimal human decision; it can therefore be applied to high-throughput DFT defect calculations and yield efficient workflows for TCH material modeling and optimization.

CALPHAD↗
DOE OSTI2021

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Additive Manufacturing Flaw Assessment Methodology

An evolution fatigue data and flaw tolerance of components produced using the Powder Bed Fusion (PBF) Additive Manufacturing (AM) process is documented in this report. Initial differences in fatigue data for AM components compared to smooth bar fatigue data indicated a need for a more detailed analysis of AM data available in technical literature. The investigation was initiated to support the development of a fatigue analysis methodology for AM components to support the of codification of AM technology for pressure equipment. The project was initiated to collect and analyze stainless steel 304L and 316L AM fatigue test data and corresponding process and quality information to develop S-N and E-N based fatigue data representation. Additional AM fatigue test data including Inconel Ti-6-4 and aluminum alloys were also considered for comparison purposes Metallic AM parts tend to contain various forms of defects distributed throughout the part. If an AM part is subjected to fatigue loading in service, a fatigue analysis needs to be performed during the design process to ensure an acceptable service life for the part. Post-process machining and polishing do not to improve fatigue resistance in any significant degree. The low cycle fatigue regime is of particular interest to this project in support of flaw acceptance criteria currently under development by ASME’s BPTCS/BNCS. Internal defects become exposed as external surface defects during machining for the machining of the AM part to final dimensions. This implies that as long as inherent AM defects are within a controlled limit in terms of both size and distribution characteristics, the corresponding fatigue test data in terms of either S-N (stress life) or E-N (strain life) can be investigated and characterized to establish fatigue properties of AM parts for design and fatigue evaluation purposes. The resulting S-N or E-N curves and their scatter bands can be used to derive fatigue design allowable stress values by capturing the effects of distributed discontinuities within an acceptable limit.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Analyses of flight model spacecraft performance during thermal-vacuum tests

In 1968 a study of the thermal-vacuum test results from 11 flight model spacecraft was published in NASA Technical Note TN D-4908. A major interest then was the relationship of malfunctions to their times of occurrence in test. From that study conclusions were drawn with respect to the length of time required for an adequate thermal-vacuum test of flight model spacecraft. A serious limitation on the study was the small amount of documented data available. The purpose of the present report is to update and to extend the findings of that study. The test results of 39 spacecraft, compared with the test results of 11 spacecraft used in the earlier study, now provide a better basis for the analysis. The new data were developed for analysis in the same manner as for the 1968 report, in order to facilitate comparison of the two studies. However, the larger data base made additional types of analyses worthwhile, and the present results are a significant extension of the earlier work.

Heuser, R. E.↗
NASA NTRS1974

The use of remote sensing and natural indicators to delineate floodplains - Preliminary findings

Feasibility studies to delineate floodplains using aerial photography have been conducted by the Army Corps of Engineers. The objective of this project is to establish floodplain boundaries using natural indicators such as vegetation types, soil types, moisture differences, and geologic variations. Using aircraft multispectral data, an automated mapping routine should enable the Corps of Engineers to reduce costly field surveys and improve the accuracy of floodplain delineation in pristine areas. The purpose of this paper is to document the methodology utilized in this investigation while using one of a series of available data sets and test sites and to report preliminary findings.

Sollers, S. C.↗
NASA NTRS2011

Development of the Orion Crew Module Static Aerodynamic Database: Hypersonic - Part 1

The Orion aerodynamic database provides force and moment coefficients given the velocity, attitude, configuration, etc. of the Crew Exploration Vehicle (CEV). The database is developed and maintained by the NASA CEV Aerosciences Project team from computational and experimental aerodynamic simulations. The database is used primarily by the Guidance, Navigation, and Control (GNC) team to design vehicle trajectories and assess flight performance. The initial hypersonic re-entry portion of the Crew Module (CM) database was developed in 2006. Updates incorporating additional data and improvements to the database formulation and uncertainty methodologies have been made since then. This paper details the process used to develop the CM database, including nominal values and uncertainties, for Mach numbers greater than 8 and angles of attack between 140deg and 180deg. The primary available data are more than 1000 viscous, reacting gas chemistry computational simulations using both the Laura and Dplr codes, over a range of Mach numbers from 2 to 37 and a range of angles of attack from 147deg to 172deg. Uncertainties were based on grid convergence, laminar-turbulent solution variations, combined altitude and code-to-code variations, and expected heatshield asymmetry. A radial basis function response surface tool, NEAR-RS, was used to fit the coefficient data smoothly in a velocity-angle-of-attack space. The resulting database is presented and includes some data comparisons and a discussion of the predicted variation of trim angle of attack and lift-to-drag ratio. The database provides a variation in trim angle of attack on the order of +/-2deg, and a range in lift-to-drag ratio of +/-0.035 for typical vehicle flight conditions.

Bibb, Karen L.↗
NASA NTRS2016

Thermal Ablation Modeling for Silicate Materials

A thermal ablation model for silicates is proposed. The model includes the mass losses through the balance between evaporation and condensation, and through the moving molten layer driven by surface shear force and pressure gradient. This model can be applied in ablation simulations of the meteoroid or glassy Thermal Protection Systems for spacecraft. Time-dependent axi-symmetric computations are performed by coupling the fluid dynamics code, Data-Parallel Line Relaxation program, with the material response code, Two-dimensional Implicit Thermal Ablation simulation program, to predict the mass lost rates and shape change. For model validation, the surface recession of fused amorphous quartz rod is computed, and the recession predictions reasonably agree with available data. The present parametric studies for two groups of meteoroid earth entry conditions indicate that the mass loss through moving molten layer is negligibly small for heat-flux conditions at around 1 MW/cm(exp. 2).

Ablators↗
NASA NTRSDate not supplied

Transcribing Air Traffic Control System Command Center Planning Telecons Using Cloud-Based Automatic Speech Recognition

This paper addresses the challenge of using Automatic Speech Recognition (ASR) technology to transcribe regular teleconferences that happen between FAA Air Traffic Control System Command Center (ATCSCC) planners, stakeholders and air users. These planning teleconferences (aka telecons or planning webinars) are an integral part of managing air traffic in the U.S. National Airspace System (NAS). In particular, the meetings facilitate the creation and modification of various traffic management initiatives (TMIs), that are used to regulate the flow of air traffic. This is typically a human intensive process, requiring specialists to listen to the entire meeting audio (10-20 minutes duration) and inferring the state of the NAS (e.g., weather phenomenon) that was discussed. It would be advantageous to have digital transcripts of the audio and have useful information (e.g., related to TMIs) automatically extracted from the transcripts. In this regard, we are exploring the adoption of state-of-the-art speech to text and Natural Language Processing (NLP) tools that will achieve our objective of digitizing the webinar audio. Unfortunately, the highly technical phraseology present in the audio and limited data availability for model building make ASR difficult. To overcome this challenge, we have taken the critical first step in creating a human transcription dataset from ~20 hours of speech in the ATCSCC audio with the help of subject matter experts. A novelty of our work is the creation of a ground truth transcription dataset for ATCSCC teleconference webinars, which is particularly important for Aviation domain-specific NLP tasks. Using Microsoft Speech Studio, a cloud-based ASR platform, we have fine-tuned the English pre-trained ASR models (available in speech studio) and achieved an average word error rate (WER) of 6.81%. The baseline ASR also provides a digital version of each planning webinar, making it accessible and text-searchable for future references. Additionally, the transcriptions can serve as a bridge between raw audio data and a range of text-based NLP tasks, such as named entity recognition (NER) and intent classification, potentially enhancing the digital footprint of the webinars and other connected data sources. Our work has several potential applications. Firstly, the transcriptions can be analyzed to understand the complex decision process of creating, implementing and modifying TMIs and may also contribute to TMI prediction services. Secondly, our dataset and model can be used to develop more accurate ASR systems for aviation-specific language, which can bring about digital communication in the aviation industry (and aid current “voice only” communications, which are inherently error-prone). Lastly, the transcriptions themselves can be used as a valuable resource for training other NLP models.

Stephen S. B. Clarke↗