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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multivariate Regression of Pyrotechnic Igniter Output

Multivariate multiple regression models are applied to simplified pyrotechnic igniters for the first time to understand how changes in manufactured parameters can affect the output gas dynamic response and the timing of ignition events. The statistical modeling technique is applied to demonstrate quantification of the effects of a set of independent variables measured in the as-fabricated igniters on a set of responses experimentally measured from the functioned igniters. Two independent process variables were intentionally varied following a full factorial experimental design while several other independent variables varied within their normal manufacturing variability range. The four igniter performance responses consisted of the timing of sequential events during igniter function and visual gas dynamic output in the form of shock wave strength observed with high-speed schlieren imaging. Linear regression models built using the measurements throughout the manufacturing processes and the output variance provide insight into the critical device parameters that dominate performance

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

The MFiX Particle-in-Cell Method (MFiX-PIC) Theory Guide

MFiX (Multiphase Flow with Interphase eXchanges) is an open-source multiphase flow solver developed at the National Energy Technology Laboratory. Within the code, users have access to a single phase or interpenetrating continua-based multiphase two-fluid model (TFM), a discrete element model (DEM), and a particle-in-cell model (PIC). TFM, DEM, and PIC can all be used to create multiphase simulations that include hydrodynamics, chemical reactions, and heat transfer.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Impact of Climate Variabilities and Human Activities on Surface Water Extents in Reservoirs of Yongding River Basin, China, from 1985 to 2016 Based on Landsat Observations and Time Series Analysis

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has seen increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology introduced in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Data-scientific Noise-removal Method for Efficient Submillimeter Spectroscopy With Single-dish Telescopes

For submillimeter spectroscopy with ground-based single-dish telescopes, removing the noise contribution from the Earth’s atmosphere and the instrument is essential. For this purpose, here we propose a new method based on a data-scientific approach. The key technique is statistical matrix decomposition that automatically separates the signals of astronomical emission lines from the drift noise components in the fast-sampled (1–10 Hz) time-series spectra obtained by a position-switching (PSW) observation. Because the proposed method does not apply subtraction between two sets of noisy data (i.e., on-source and off-source spectra), it improves the observation sensitivity by a factor of √2. It also reduces artificial signals such as baseline ripples on a spectrum, which may also help to improve the effective sensitivity. We demonstrate this improvement by using the spectroscopic data of emission lines toward a high-redshift galaxy observed with a 2 mm receiver on the 50 m Large Millimeter Telescope. Since the proposed method is carried out offline and no additional measurements are required, it offers an instant improvement on the spectra reduced so far with the conventional method. It also enables efficient deep spectroscopy driven by the future 50 m class large submillimeter single-dish telescopes, where fast PSW observations by mechanical antenna or mirror drive are difficult to achieve.

47 OTHER INSTRUMENTATION↗
NASA NTRS1972

Planetary resonances, bi-stable oscillation modes, and solar activity cycles

The natural resonance structure of the planets in the solar system yields resonance periods of 11.08 and 180 years. The 11.08 year period is due to resonance of the sidereal periods of the three inner planets. The 180-year period is due to synodic resonances of the four major planets. These periods are also observed in the sunspot time series. The 11-year sunspot cycles from 1 to 19 are separated into categories of positive and negative cycles, Mode 1 and Mode 2 cycles, and typical and anomalous cycles. Each category has a characteristic shape, magnitude, or duration, so that statistical prediction techniques are improved when a cycle can be classified in a given category. These categories provide evidence for bistable modes of solar oscillation. The next minimum is expected in 1977 and the next maximum in 1981 or later. These epoch values are 2.5 years later than those based on typical cycle characteristics.

Sleeper, H. P., Jr.↗
NASA NTRS1971

Engineering experimental program on the effects of near-space radiation on lithium doped solar cells

The results of an experimental evaluation of the real-time degradation characteristics of lithium-diffused silicon solar cells are reported. A strontium-90 radioisotope was used for simulation of a typical earth-orbital electron environment. The experiment was performed in an ion pump vacuum chamber with samples maintained at -50, +20, +50, and +80 C. Samples were illuminated during the 6-month exposure run with solar cell 1-5 characteristics measured periodically in situ. This 6-month exposure corresponded to a 1 MeV equivalent fluence of approximately 10 to the 14th power electrons/sq cm. Several types of lithium cells were irradiatied and compared directly with conventional N/P cells. The best lithium cells compared favorably with N/P cells, particularly at the higher test temperatures. With a slight improvement of initial performance characteristics, lithium cells appear feasible for 5 to 10 year missions at synchronous altitude. Based on the reported results and those of other irradiation experiments, lithium cells would appear to be superior to N/P cells in proton-dominated earth-orbital environments. Another important conclusion of the effort was that illuminated/loaded cells degrade more rapidly than do dark/unloaded cells. The irradiation experiment provided data of high quality with a high degree of confidence because of the experimental and statistical analysis techniques utilized.

Source record↗
NASA NTRS1972

Goddard trajectory determination subsystem: Mathematical specifications

The mathematical specifications of the Goddard trajectory determination subsystem of the flight dynamics system are presented. These specifications include the mathematical description of the coordinate systems, dynamic and measurement model, numerical integration techniques, and statistical estimation concepts.

Wagner, W. E.↗
NASA NTRS1974

Implementation of ILLIAC 4 algorithms for multispectral image interpretation

Research has focused on the design and partial implementation of a comprehensive ILLIAC software system for computer-assisted interpretation of multispectral earth resources data such as that now collected by the Earth Resources Technology Satellite. Research suggests generally that the ILLIAC 4 should be as much as two orders of magnitude more cost effective than serial processing computers for digital interpretation of ERTS imagery via multivariate statistical classification techniques. The potential of the ARPA Network as a mechanism for interfacing geographically-dispersed users to an ILLIAC 4 image processing facility is discussed.

Ray, R. M.↗
NASA NTRS1975

Measurements of SO2 absorption coefficients using a tunable dye laser

High-resolution measurements of SO2 absorption coefficients in the UV are presented in the wavelength interval between 2962 and 3011 A using a frequency-doubled dye laser as the radiation source. These measurements have application to pollution monitoring of SO2 using the differential absorption scattering (DAS) technique. The spectral resolution of the measurements was 0.2 A (determined by the linewidth of the dye laser) and the generated spectra exhibited considerable rotational structure on the (000) double prime to (060) prime, (070) prime, and (080) prime electronic-vibrational transitions. The latter transitions correspond to center wavelengths of 3001.8, 2981.0, and 2962 A, respectively. The laser measurements were obtained with a signal-processing technique which eliminated calibration procedures associated with conventional optical-absorption measurements. Using this technique, the statistical uncertainty associated with measured absorption coefficients has been reduced to less than plus or minus 2% with a wavelength uncertainty of the laser source of plus or minus 0.1 A.

Thompson, R. T., Jr.↗
NASA NTRS1976

An investigation of correlation between pilot scanning behavior and workload using stepwise regression analysis

An electro-optical device called an oculometer which tracks a subject's lookpoint as a time function has been used to collect data in a real-time simulation study of instrument landing system (ILS) approaches. The data describing the scanning behavior of a pilot during the instrument approaches have been analyzed by use of a stepwise regression analysis technique. A statistically significant correlation between pilot workload, as indicated by pilot ratings, and scanning behavior has been established. In addition, it was demonstrated that parameters derived from the scanning behavior data can be combined in a mathematical equation to provide a good representation of pilot workload.

Waller, M. C.↗
NASA NTRS1977

Application of statistical inversion to ground-based microwave remote sensing of temperature and water vapor profiles

Surface-based observations of downwelling microwave thermal emission are related to temperature and humidity profiles via a standard integral equation of radiative transfer. Both in clear and in cloudy atmospheres, statistical inversion techniques are used to retrieve profiles from a data vector of brightness observations and surface meteorological constraints. For the clear case, accuracy predictions and profile retrievals are illustrated for: (1) single frequency angular scanned data; (2) multi-frequency angular scanned data; and (3) multi-frequency zenith data. For the last case predicted and achieved accuracies were compared in a recently conducted radiometric experiment. Retrievals of cloud contaminated radiometric data are elaborated.

Westwater, E. R.↗