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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Turbomachine Sealing and Secondary Flows - Part 3: Review of Power-Stream Support, Unsteady Flow Systems, Seal and Disk Cavity Flows, Engine Externals, and Life and Reliability Issues - Part 3

The issues and components supporting the engine power stream are reviewed. It is essential that companies pay close attention to engine sealing issues, particularly on the high-pressure spool or high-pressure pumps. Small changes in these systems are reflected throughout the entire engine. Although cavity, platform, and tip sealing are complex and have a significant effect on component and engine performance, computational tools (e.g., NASA-developed INDSEAL, SCISEAL, and ADPAC) are available to help guide the designer and the experimenter. Gas turbine engine and rocket engine externals must all function efficiently with a high degree of reliability in order for the engine to run but often receive little attention until they malfunction. Within the open literature statistically significant data for critical engine components are virtually nonexistent; the classic approach is deterministic. Studies show that variations with loading can have a significant effect on component performance and life. Without validation data they are just studies. These variations and deficits in statistical databases require immediate attention.

Hendricks, R. C.↗
NASA NTRS2005

Aeroheating Characteristics for a Two-Stage-To-Orbit Concept During Separation at Mach 6

An experimental study was conducted to determine the proximity aeroheating characteristics for a two-stage-to-orbit concept in close proximity in the NASA Langley 20-Inch Mach 6 Air Tunnel. A new hybrid discrete thin-film resistance gauge technique was evaluated in this study and used to measure experimental interference heating levels between the booster and the orbiter at a constant freestream Reynolds number of 8.25 x 10(exp 6)/m and a variety of separation and axial offset distances. It was found that, as the orbiter separates from the booster and the booster falls away, the windward centerline heating increased on the orbiter by as much as 13-times over the baseline, single model heating distribution, and on the booster by as much as 6-times. The aeroheating database developed can be used for computational fluid dynamic code validation.

Liechty, Derek S.↗
DOE OSTI2022

Combustion characteristics and detailed simulations of surrogates for a Tier II gasoline certification fuel

An experimental and numerical study of combustion of a gasoline certification fuel (‘indolene’), and four (S4) and five (S5) component surrogates for it, is reported for the configurations of an isolated droplet burning with near spherical symmetry in the standard atmosphere, and a single cylinder engine designed for advanced compression ignition of pre-vaporized fuel. The intent was to compare performance of the surrogate for these different combustion configurations and to assess the broader applicability of the kinetic mechanism and property database for the simulations. A kinetic mechanism comprised of 297 species and 16797 reactions was used in the simulations that included soot chemistry and accounted for unsteady transport, liquid diffusion inside the droplet, radiative heat transfer, and variable properties. The droplet data showed a clear preference for the S5 surrogate in terms of burning rate. The simulations showed generally very good agreement with measured droplet, flame, and soot shell diameters. Measurements of combustion timing, in-cylinder pressure, and mass-averaged gas temperature were also well predicted with a slight preference for the S5 surrogate. Preferential vaporization was not evidenced from the evolution of droplet diameter but was clearly revealed in simulations of the evolution of mixture fractions inside the droplets. The influence of initial droplet diameter (Do) on droplet burning was strong, with S5 burning rates decreasing with increasing Do due to increasing radiation losses from the flame. Flame extinction was predicted for Do =3.0 mm as a radiative loss mechanism but not predicted for smaller Do for the conditions of the simulations.

Guo, Songtao↗
DOE OSTI2016

Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A

Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

ESTIMATING PHOTOMETRIC REDSHIFTS OF QUASARS VIA THE k-NEAREST NEIGHBOR APPROACH BASED ON LARGE SURVEY DATABASES

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Nuclear Data Sheets for A=59☆

-- Abstract: Evaluated spectroscopic data and level schemes from radioactive decay and nuclear reaction studies are presented for {sup 59}Sc, {sup 59}Ti, {sup 59}V, {sup 59}Cr, {sup 59}Mn, {sup 59}Fe, {sup 59}Co, {sup 59}Ni, {sup 59}Cu, {sup 59}Zn, {sup 59}Ga, and {sup 59}Ge. This evaluation for A=59 supersedes the earlier one by C. M. Baglin (2002Ba42) and updates for some nuclides in the ENSDF database. Highlights: of this evaluation are the following: In {sup 59}Fe, a new level at 568.45 keV (3/2{sup −}) has been added. This resolves a doublet at 570 keV, which was reflected in earlier evaluations (1983An22, 1993Ba85, 2002Ba42) and literature as well. Conflicting spin assignments for 2153.62 keV level of {sup 59}Co [9/2,13/2 in (α,pγ); (13/2) in ({sup 15}N,4nγ); 15/2 in (p,p′γ), (7/2{sup −}) in (n,n′γ)] exist in the literature and additional experimental work is needed to resolve the issue.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Seventy-Five Years of Nuclear Criticality Safety Documents - A Bibliography

A collection of nuclear criticality documents was begun in the 1970s at the University of California’s Lawrence Livermore Laboratory (now Lawrence Livermore National Laboratory). Documents in this bibliography are dated as early as 1940. The early documents described experimental work that was of vital importance to the safe use of fissile materials at nuclear reactors, the facilities for preparing or transporting nuclear fuel, or its eventual storage. The experiments were of direct value to support safe operations with fissile material, but they were also needed for validating early physics data and calculational methods and then for validating computational methods used in criticality safety evaluations. Bibliographic information from the past seventy-five years have been entered into a computerized bibliographic database. The database continues to expand. Publications were issued several decades ago based on the information collected at that time. Our new publication is based on a current “snapshot” of the present status of the database. Contributions of information or corrections to this bibliography will be appreciated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Status Report on the Molten Salt Thermodynamic Database (MSTDB) Development (FY20)

The current status of the Molten Salt Thermodynamic Database (MSTDB) is reported. While a series of informational exchange meetings with Moten Salt Reactor (MSR) developers has given the NEAMS program insight into the systems of interest, the current effort is devoted to mining data already available in the literature for continued development of MSTDB. Many relevant systems have not been previously studied. Therefore, once the data from the literature is completely reviewed, curated, and used as inputs for MSTDB, new thermodynamic values will need to be generated using computational and experimental approaches.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1996

Hydroacoustic forcing function modeling using DNS database

A wall pressure frequency spectrum model (Blake 1971 ) has been evaluated using databases from Direct Numerical Simulations (DNS) of a turbulent boundary layer (Na & Moin 1996). Good agreement is found for moderate to strong adverse pressure gradient flows in the absence of separation. In the separated flow region, the model underpredicts the directly calculated spectra by an order of magnitude. The discrepancy is attributed to the violation of the model assumptions in that part of the flow domain. DNS computed coherence length scales and the normalized wall pressure cross-spectra are compared with experimental data. The DNS results are consistent with experimental observations.

Zawadzki, I.↗
DOE OSTI2021

Can machine learning predict fuel properties accurately?

High-potential molecules derived from biomass sources may suitably replace or supplement traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing cost. Experimental property testing of these bioproducts is usually conducted years after initial bench-scale experiments, due to high experimental costs and/or high volume requirements. However, neglecting to conduct property testing early in the pathway development cycle can lead to investments spent on scaling-up production of bioproducts and biofuels that do not perform as expected. Instead, machine-learning techniques can be used to develop quantitative structure–property relationships for molecules using a relatively large training set of molecular descriptor data. For this study, we compiled measured properties, IR spectra, and molecular descriptors of bio-based molecules from databases and published studies for training models of bioproduct properties. We trained regression models with molecular descriptors and will compare results of different estimators. This study describes the first steps towards a performance prediction tool for bio-based alternative fuels. Keywords: Machine learning, biofuels, jet fuels, fuel properties

Mayer, Morgan A.↗
DOE OSTI2013

Evaluation of L-shell X-ray production cross sections by impact of {sup 4}He{sup +} ions

A database of published L-subshell X-ray production cross sections due to the impact of {sup 4}He{sup +} ions on several elements is compiled. In order to evaluate the data, they are compared with theoretical calculations using the ECPSSR theory (which modifies the Plane Wave Born Approximation) and its corrections, such as Multiple Ionization of the target atom (MI), United Atom (UA), and Intra-shell coupling (IS) models. It was found that the theory does not predict reliably the experimental results for all elements and energy ranges. Furthermore, a possible universal scaling for the xray production cross sections of the L{sub {alpha}}(L{sub 3}M{sub 4}+L{sub 3}M{sub 5}) and the L{sub {gamma}}(L{sub 2}N{sub 4}+L{sub 1}N{sub 2}+L{sub 1}N{sub 3}+L{sub 1}O{sub 3}+L{sub 1}O{sub 2}+L{sub 2}N{sub 1}+L{sub 2}O{sub 4}) lines is proposed, based on reduced velocity parameters {xi}{sub L}{sup R} and {xi}{sub L1,2}{sup R}, respectively. This scaling seems to be useful for the L{sub {alpha}} experimental points and not so good for the L{sub {gamma}} line, suggesting that more extensive theoretical studies in this direction should be followed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Advances in prediction of tokamak experiments with theory-based models

The successful validation of theory-based models of transport, MHD stability, heating and current drive, with tokamak measurements over the last 20 years, has laid the foundation for a new era where these models can be routinely used in a "predict first" approach to design and predict the outcomes of experiments on tokamaks today. The capability to predict the plasma confinement and core profiles with a quantified uncertainty, based on a multi-machine, international, database of experience, will provide confidence that a proposed discharge will remain within the operational limits of the tokamak. Developing this predictive capability for the first generation of burning plasma devices, beginning with ITER, and progressing to tokamak demonstration reactors, is a critical mission of fusion energy research. Major advances have been made implementing this predict first methodology on today's tokamaks. In this work, an overview of several of these recent advances will be presented, providing the integrated modeling foundations of the experimental successes. The first steps to include boundary plasmas, and tokamak control systems, have been made. A commitment to predicting experiments as part of the planning process is needed in order to collect predictive accuracy data and evolve the models and software into a robust whole discharge pulse design simulator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

On the prediction of critical heat flux using a physics-informed machine learning-aided framework

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the abundance of predictive tools available to the thermal engineering community, the path for an accurate, robust CHF model remains elusive due to lack of consensus on the DNB triggering mechanism. This work aims to apply a physics-informed machine learning (ML)-aided hybrid framework to achieve superior predictive capabilities. Such a hybrid approach takes advantage of existing understanding in the field of interest (i.e., domain knowledge) and uses ML to capture undiscovered information from the mismatch between the actual and domain knowledge-predicted target. A detailed case study is carried out with an extensive DNB-specific CHF database to demonstrate (1) the improved performance of the hybrid approach as compared to traditional domain knowledge-based models, and (2) the hybrid model’s superior generalization capabilities over standalone ML methods across a wide range of flow conditions. The hybrid framework could also readily extend its applicability domain and complexity on the fly, showing an elevated level of flexibility and robustness. Based on the case study conclusions, the window-type extrapolation mapping methodology is further proposed to better inform high-cost experimental work.

42 ENGINEERING↗
NASA NTRS2007

Noise Spectra and Directivity For a Scale-Model Landing Gear

An extensive experimental study has been conducted to acquire detailed noise spectra and directivity data for a high-fidelity, 6.3%-scale, Boeing 777 main landing gear. The measurements were conducted in the NASA Langley Quiet Flow Facility using a 41-microphone directional array system positioned at a range of polar and azimuthal observer angles with respect to the model. DAMAS (Deconvolution Approach for the Mapping of Acoustic Sources) array processing as well as straightforward individual microphone processing were employed to compile unique flyover and sideline directivity databases for a range of freestream Mach numbers (0.11 - 0.17) covering typical approach conditions. Comprehensive corrections were applied to the test data to account for shear layer ray path and amplitude variations. This allowed proper beamforming at different measurement orientations, as well as directivity presentation in free-field emission coordinates. Four different configurations of the landing gear were tested: a baseline configuration with and without an attached side door, and a noise reduction concept "toboggan" truck fairing with and without side door. DAMAS noise source distributions were determined. Spectral analyses demonstrated that individual microphones could establish model spectra. This finding permitted the determination of unique, spatially-detailed directivity contours of spectral band levels over a hemispherical surface. Spectral scaling for the baseline model confirmed that the acoustic intensity scaled with the expected sixth-power of the Mach number. Finally, comparison of spectra and directivity between the baseline gear and the gear with an attached toboggan indicated that the toboggan fairing may be of some value in reducing gear noise over particular frequency ranges.

Humphreys, William M., Jr.↗
NASA NTRS2018

Turbulent Aeroheating Measurements on a 7-deg Half-Angle Sphere-Cone in a High-Enthalpy CO2 Expansion Tunnel

A database of heating and pressure measurements on a 7-deg half-angle cone in a highenthalpy expansion tunnel in CO2 has been generated to support development and validation of computational models to be employed in the design of future Mars missions. Laminar, transitional, and turbulent simulations were performed at the test conditions for comparisons with the data. Close agreement was obtained for both fully-laminar and fully turbulent conditions. For the remaining transitional/turbulent conditions, agreement to within, or slightly more than, the estimated experimental uncertainty was demonstrated. The influence of transition intermittency and transition length models on predicted heating levels was demonstrated, as were differences in turbulent heating predictions generated using various algebraic, one-equation, and two-equation turbulence models. These comparisons provide some measure of confidence in turbulent simulation capabilities; however, because the data were not obtained on a relevant entry vehicle geometry, it is not possible to fully quantify computational uncertainties for the definition of Mars mission aerothermodynamic environments at this time

Brian R Hollis↗
NASA NTRS1994

Automatic cataloguing and characterization of Earth science data using SE-trees

In the future, NASA's Earth Observing System (EOS) platforms will produce enormous amounts of remote sensing image data that will be stored in the EOS Data Information System. For the past several years, the Intelligent Data Management group at Goddard's Information Science and Technology Office has been researching techniques for automatically cataloguing and characterizing image data (ADCC) from EOS into a distributed database. At the core of the approach, scientists will be able to retrieve data based upon the contents of the imagery. The ability to automatically classify imagery is key to the success of contents-based search. We report results from experiments applying a novel machine learning framework, based on Set-Enumeration (SE) trees, to the ADCC domain. We experiment with two images: one taken from the Blackhills region in South Dakota; and the other from the Washington DC area. In a classical machine learning experimentation approach, an image's pixels are randomly partitioned into training (i.e. including ground truth or survey data) and testing sets. The prediction model is built using the pixels in the training set, and its performance is estimated using the testing set. With the first Blackhills image, we perform various experiments achieving an accuracy level of 83.2 percent, compared to 72.7 percent using a Back Propagation Neural Network (BPNN) and 65.3 percent using a Gaussain Maximum Likelihood Classifier (GMLC). However, with the Washington DC image, we were only able to achieve 71.4 percent, compared with 67.7 percent reported for the BPNN model and 62.3 percent for the GMLC.

Rymon, Ron↗