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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Interpreting SBUV Smoothing Errors: an Example Using the Quasi-biennial Oscillation

The Solar Backscattered Ultraviolet (SBUV) observing system consists of a series of instruments that have been measuring both total ozone and the ozone profile since 1970. SBUV measures the profile in the upper stratosphere with a resolution that is adequate to resolve most of the important features of that region. In the lower stratosphere the limited vertical resolution of the SBUV system means that there are components of the profile variability that SBUV cannot measure. The smoothing error, as defined in the optimal estimation retrieval method, describes the components of the profile variability that the SBUV observing system cannot measure. In this paper we provide a simple visual interpretation of the SBUV smoothing error by comparing SBUV ozone anomalies in the lower tropical stratosphere associated with the quasi-biennial oscillation (QBO) to anomalies obtained from the Aura Microwave Limb Sounder (MLS). We describe a methodology for estimating the SBUV smoothing error for monthly zonal mean (mzm) profiles. We construct covariance matrices that describe the statistics of the inter-annual ozone variability using a 6 yr record of Aura MLS and ozonesonde data. We find that the smoothing error is of the order of 1percent between 10 and 1 hPa, increasing up to 15-20 percent in the troposphere and up to 5 percent in the mesosphere. The smoothing error for total ozone columns is small, mostly less than 0.5 percent. We demonstrate that by merging the partial ozone columns from several layers in the lower stratosphere/troposphere into one thick layer, we can minimize the smoothing error. We recommend using the following layer combinations to reduce the smoothing error to about 1 percent: surface to 25 hPa (16 hPa) outside (inside) of the narrow equatorial zone 20 S-20 N.

ozone profile retrievals↗
NASA NTRS2020

Inter-Calibration of Nine UV Sensing Instruments Over Antarctica and Greenland Since 1980

Nadir-viewed intensities (radiances) from nine UV sensing satellite instruments are calibrated over the East Antarctic Plateau and Greenland during summer. The calibrated radiances from these UV instruments ultimately will provide a global long-term record of cloud trends and cloud response from ENSO events since 1980. We first remove the strong solar zenith angle dependence from the intensities using an empirical approach rather than a radiative trans-fer model. Then small multiplicative adjustments are made to these solar zenith angle normalized intensities in order to minimize differences when two or more instruments tempo-rally overlap. While the calibrated intensities show a negligible long-term trend over Antarctica and a statistically in-significant UV albedo trend of−0.05 % per decade over the interior of Greenland, there are small episodic reductions in intensities which are often seen by multiple instruments. Three of these darkening events are explained by boreal forest. Other events are caused by surface melting or volcanoes. We estimate a 2-sigma uncertainty of 0.35% for the calibrated radiances

Nadir-viewed intensities (radiances) from nine UV ↗
DOE OSTI2022

Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference

Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will increase that number by 1-2 orders of magnitude. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. To leverage the increased statistical power of the lenses that will be discovered through upcoming surveys, automated lens analysis techniques are necessary. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate the successful application of Neural Posterior Estimation (NPE) to automate the inference of a 12-parameter lens mass model for DES-like ground-based imaging data. We compare our NPE constraints to a Bayesian Neural Network (BNN) and find that it outperforms the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1997

Development of Statistical Process Control Methodology for an Environmentally Compliant Surface Cleaning Process in a Bonding Laboratory

Bonding labs at both MSFC and the northern Utah production plant prepare bond test specimens which simulate or witness the production of NASA's Reusable Solid Rocket Motor (RSRM). The current process for preparing the bonding surfaces employs 1,1,1-trichloroethane vapor degreasing, which simulates the current RSRM process. Government regulations (e.g., the 1990 Amendments to the Clean Air Act) have mandated a production phase-out of a number of ozone depleting compounds (ODC) including 1,1,1-trichloroethane. In order to comply with these regulations, the RSRM Program is qualifying a spray-in-air (SIA) precision cleaning process using Brulin 1990, an aqueous blend of surfactants. Accordingly, surface preparation prior to bonding process simulation test specimens must reflect the new production cleaning process. The Bonding Lab Statistical Process Control (SPC) program monitors the progress of the lab and its capabilities, as well as certifies the bonding technicians, by periodically preparing D6AC steel tensile adhesion panels with EA-91 3NA epoxy adhesive using a standardized process. SPC methods are then used to ensure the process is statistically in control, thus producing reliable data for bonding studies, and identify any problems which might develop. Since the specimen cleaning process is being changed, new SPC limits must be established. This report summarizes side-by-side testing of D6AC steel tensile adhesion witness panels and tapered double cantilevered beams (TDCBs) using both the current baseline vapor degreasing process and a lab-scale spray-in-air process. A Proceco 26 inches Typhoon dishwasher cleaned both tensile adhesion witness panels and TDCBs in a process which simulates the new production process. The tests were performed six times during 1995, subsequent statistical analysis of the data established new upper control limits (UCL) and lower control limits (LCL). The data also demonstrated that the new process was equivalent to the vapor degreasing process.

Hutchens, Dale E.↗
DOE OSTI2022

Model reduction methods for nuclear emulators

The field of model order reduction (MOR) is growing in importance due to its ability to extract the key insights from complex simulations while discarding computationally burdensome and superfluous information. We provide an overview of MOR methods for the creation of fast & accurate emulators of memory- and compute-intensive nuclear systems, focusing on eigen-emulators and variational emulators. As an example, we describe how 'eigenvector continuation' is a special case of a much more general and well-studied MOR formalism for parameterized systems. We continue with an introduction to the Ritz and Galerkin projection methods that underpin many such emulators, while pointing to the relevant MOR theory and its successful applications along the way. Here, we believe that this guide will open the door to broader applications in nuclear physics and facilitate communication with practitioners in other fields.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

An Experimental Determination of Losses in a 3-Port Wave Rotor

Wave rotors, used in a gas turbine topping cycle, offer a potential route to higher specific power and lower specific fuel consumption. In order to exploit this potential properly, it is necessary to have some realistic means of calculating wave rotor performance, taking losses into account, so that wave rotors can be designed for good performance. This in turn requires a knowledge of the loss mechanisms. The experiment reported here was designed as a statistical experiment to identify the losses due to finite passage opening time, friction, and leakage. For simplicity, the experiment used a 3-port, flow divider, wave cycle, but the results should be applicable to other cycles. A 12 inch diameter rotor was used, with two different lengths, 9 inches and 18 inches, and two different passage widths, 0.25 inch and 0.54 inch, in order to vary friction and opening time. To vary leakage, moveable end-walls were provided so that the rotor to end-wall gap could be adjusted. The experiment is described, and the results are presented, together with a parametric fit to the data. The fit shows that there will be an optimum passage width for a given wave rotor, since, as the passage width increases, friction losses decrease, but opening-time losses increase, and vice-versa. Leakage losses can be made small at reasonable gap sizes.

Wilson, Jack↗
DOE OSTI2022

Supervised and unsupervised machine learning of structural phases of polymers adsorbed to nanowires

Here, we identify configurational phases and structural transitions in a polymer nanotube composite by means of machine learning. We employ various unsupervised dimensionality reduction methods, conventional neural networks, as well as the confusion method, an unsupervised neural-network-based approach. We find neural networks are able to reliably recognize all configurational phases that have been found previously in experiment and simulation. Furthermore, we locate the boundaries between configurational phases in a way that removes human intuition or bias. This could be done before only by relying on preconceived, ad hoc order parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1994

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Djorgovski, S. George↗
NASA NTRS2023

Solar Activity Relations in Energetic Electron Events Measured By the MESSENGER Mission

Aims. We perform a statistical study of the relations between the properties of solar energetic electron (SEE) events measured by the MESSENGER mission from 2010 to 2015 and the parameters of the respective parent solar activity phenomena in order to identify the potential correlations between them. During the time of analysis, the MESSENGER heliocentric distance varied between 0.31 and 0.47 au. Methods. We used a published list of 61 SEE events measured by MESSENGER, which includes information on the near-relativistic electron peak intensities, the peak-intensity energy spectral indices, and the measured X-ray peak intensity of the flares related to the SEE events. Taking advantage of multi-viewpoint remote-sensing observations, we reconstructed, whenever possible, the associated coronal mass ejections (CMEs) and shock waves; and we determined the three-dimensional (3D) properties (location, speed, and width) of the CMEs and the maximum speed of the 3D CME-driven shocks in the corona. We used different methods (Spearman, Pearson, and a Bayesian approach, namely the Kelly method to linear regression) to estimate the correlation coefficients between the flare intensity, maximum speed at the apex of the CME-driven shock, CME speed at the apex, and CME width with the electron peak intensities and with the energy spectral indices. In this statistical study, we considered and addressed the limitations of the particle instrument on board MESSENGER (elevated background intensity level, anti-Sun pointing). Results. There is an asymmetry to the east in the range of connection angles (CAs) for which the SEE events present the highest peak intensities, where the CA is the longitudinal separation between the footpoint of the magnetic field connecting to the spacecraft and the flare location. Based on this asymmetry, we define a subsample of well-connected events as when −65° ≤ CA ≤ +33°. For the well-connected sample, we find moderate to strong correlations between the near-relativistic electron peak intensity and the 3D CME-driven shock maximum speed at the apex (Spearman: cc = 0.53 ± 0.05; Pearson: cc = 0.65 ± 0.04; Kelly: cc = 0.87 ± 0.20), the flare peak intensity (Spearman: cc = 0.63 ± 0.03; Pearson: cc = 0.59 ± 0.03; Kelly: cc = 0.74 ± 0.30), and the 3D CME speed at the apex (Spearman: cc = 0.50 ± 0.04; Pearson: cc = 0.46 ± 0.03; Kelly: cc = 0.60 ± 0.39). When including poorly connected events (full sample), the relations between the peak intensities and the solar-activity phenomena are blurred, showing lower correlation coefficients. Conclusions. Based on the comparison of the correlation coefficients presented in this study using near 0.4 au data, (1) both flare and shock-related processes may contribute to the acceleration of near relativistic electrons in large SEE events, in agreement with previous studies based on near 1 au data; and (2) the maximum speed of the CME-driven shock is a better parameter to investigate particle-acceleration-related mechanisms than the average CME speed, as suggested by the stronger correlation with the SEE peak intensities.

Sun↗
DOE OSTI2022

Efficient computation of N -point correlation functions in D dimensions

We present efficient algorithms for computing the N-point correlation functions (NPCFs) of random fields in arbitrary D-dimensional homogeneous and isotropic spaces. Such statistics appear throughout the physical sciences and provide a natural tool to describe stochastic processes. Typically, algorithms for computing the NPCF components have $\mathscr O$(n N ) complexity (for a dataset containing n particles); their application is thus computationally infeasible unless N is small. By projecting the statistic onto a suitably defined angular basis, we show that the estimators can be written in a separable form, with complexity $\mathscr O$(n 2 ) or $\mathscr O$(n g log n g ) if evaluated using a Fast Fourier Transform on a grid of size n g . Our decomposition is built upon the D-dimensional hyperspherical harmonics; these form a complete basis on the (D – 1) sphere and are intrinsically related to angular momentum operators. Concatenation of (N – 1) such harmonics gives states of definite combined angular momentum, forming a natural separable basis for the NPCF. As N and D grow, the number of basis components quickly becomes large, providing a practical limitation to this (and all other) approaches: However, the dimensionality is greatly reduced in the presence of symmetries; for example, isotropic correlation functions require only states of zero combined angular momentum. We provide a Julia package implementing our estimators and show how they can be applied to a variety of scenarios within cosmology and fluid dynamics. The efficiency of such estimators will allow higher-order correlators to become a standard tool in the analysis of random fields.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗
DOE OSTI2023

Evolution of titanium particle combustion in potassium perchlorate and air

Understanding titanium particle combustion processes is critical not only for characterizing existing pyrotechnic systems but also for creating new igniter designs. In order to characterize titanium particle combustion processes, morphologies, and temperatures, simultaneous spatially-resolved electric field holography and imaging pyrometry techniques were used to capture post-ignition data at up to 7 kHz. Due to the phase and thermal distortions present in the combustion cloud, traditional digital in-line holography techniques fail to capture accurate data. Here in this work, electric field holography techniques are used in order to cancel distortions and capture the three-dimensional spatial locations and diameters of the particles. In order to estimate the projected surface temperatures of the titanium particles, an imaging pyrometry method that ratios emission at 750 and 850 nm is utilized. Using these diagnostics, joint statistics are collected for particle size, morphology, velocity, and temperature. Results show that, early in the combustion process, the titanium particles are primarily oxidized by potassium perchlorate inside the igniter cup, resulting in projected surface temperatures near 3000 K. Later in the process, the particles interact with ambient air, resulting in lower surface temperatures around 2400 K and the formation of flame zones. These results are consistent with adiabatic flame temperature predictions as well as particle morphology observations of a titanium core with a TiO 2 surface. Late stage particle expansion, star fragmentation, and molten droplet breakup events are also observed using the time-resolved morphology and temperature diagnostics. These results illustrate the different stages of titanium particle combustion in pyrotechnic environments, which can be used to inform improvements in next-generation igniters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A custom high-stability pulse generator for the test of a novel in-situ magnetic sensor developed to detect radiation damage in undulators

High-performance analog-to-digital converters (ADC) have been widely applied in many areas of science. For instance, magnetic field measurements based on the Faraday’s induction law require high-precision voltmeters to measure induced voltages. In this study in the context of free-electron lasers technology, the Magnetic Measurements Team at SLAC National Accelerator Laboratory proposed a novel in-situ radiation damage detection system (RDDS) for detecting small field variations in undulators. The system measures the flux change in a flexible printed-circuit coil attached to the magnet array during the undulator gap movement. The gap movement changes the magnetic field, which induces a voltage signal in the coil that is measured and integrated with an ADC. Although the system is capable of detecting relative flux changes better than 100 ppm, drift in the ADC’s gain or offset can cause apparent changes in the relative flux. This paper describes the first attempt to developed a high-precision verification circuit to perform ADC testing in the framework of the novel RDDS. The circuit generates a reference voltage pulse with a voltage–time integral relative precision better than 50 ppm for a few hundreds of mVs — the typical order of magnitude measured with the RDDS. The circuit’s design combines a fast and precise switch with a low-noise voltage reference. Long-term measurements allowed statistical analysis and showed that averaging the voltage–time integral of ten pulses gives the required 50 ppm stability. Moreover, reproducibility tests confirmed that the circuit’s output is invariable under small power supply instabilities and equipment shutdown. Instruments and applications designed to quantify the magnetic field by integrating voltage signals may use the pulse generator proposed in this paper for verification purposes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Manifold Learning: What, How, and Why

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to find the low-dimensional structure of data. Dimension reduction for large, high-dimensional data is not merely a way to reduce the data; the new representations and descriptors obtained by ML reveal the geometric shape of high-dimensional point clouds and allow one to visualize, denoise, and interpret them. This review presents the underlying principles of ML, its representative methods, and their statistical foundations, all from a practicing statistician's perspective. It describes the trade-offs and what theory tells us about the parameter and algorithmic choices we make in order to obtain reliable conclusions.

Mathematics↗
DOE OSTI2020

Automating Analysis of Neutron Scattering Time-of-Flight Single Crystal Phonon Data

This article introduces software called Phonon Explorer that implements a data mining workflow for large datasets of the neutron scattering function, S(Q, ω), measured on time-of-flight neutron spectrometers. This systematic approach takes advantage of all useful data contained in the dataset. It includes finding Brillouin zones where specific phonons have the highest scattering intensity, background subtraction, combining statistics in multiple Brillouin zones, and separating closely spaced phonon peaks. Using the software reduces the time needed to determine phonon dispersions, linewidths, and eigenvectors by more than an order of magnitude.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1984

Improvement of the basic knowledge of the climatology of the vertical ozone layer by enhanced balloon sounding

Balloon sounding of the ozone in the Earth atmosphere was performed in order to determine the natural behavior of ozone and its recognizable deviations. The importance of ozone in the Earth atmosphere and the orographic situation of observatories and ozone sounding statistics since 1966 are explained. The physical processes governing the total amount of ozone, and the behavior of stratospheric ozone are described. Measurements in the upper stratosphere show a decrease of the ozone partial pressure above 26 km altitude since 1977. The behavior of tropospheric ozone is discussed. Data since 1977 show increasing ozone values in the troposphere, up to 50% to 70%. This increase is independent of the solar radiation intensity and the reinforced transport of stratospheric ozone into the troposphere. The increase in the troposphere cannot compensate the stratospheric decrease.

Attmannspacher, W.↗