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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Origins of epitaxial macro-terraces and macro-steps on GaN substrates

Localized lattice distortions in GaN substrates can serve as nucleation sites for epitaxial macro-steps and macro-terraces. These detrimental macro-scale features give rise to optically hazy homoepitaxial GaN surfaces. After nucleating, these macro-features grow laterally along the surface and coalesce, leading to significant coverage of the wafer surface. Dot-core GaN substrates consisting of a periodic array of cores were used as a defect-engineered system, where dislocations are intentionally concentrated at the cores. The high density of threading dislocations at the cores induced localized lattice distortions. Here, these distortions are associated predominantly with lattice tilt on the order of hundreds of arcsec across ~0.5 mm laterally along the wafer surface. The resulting macro-features that nucleated at these localized distorted sites were made up of macro-terraces with lengths ranging ~30–~150 μm and macro-step heights ranging ~200–~400 nm. Another source of localized distortion was threading screw dislocations or GaN nanopipes that resulted in spiral growth and hillock formation. Based on x-ray topography and optical microscopy measurements, we speculate that the coalescence of hillocks evolves into macro-terraces and macro-steps. While previous studies focused on the substrate miscut as a means to control macro-feature formation, we show that localized lattice tilt from defects is another important contributor to macro-feature formation.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Structural and lithologic study of northern California Coast Range and Sacramento Valley, California

The author has identified the following significant results. Photgeologic examination of repetitive multispectral ERTS-1 imagery of Northern California has disclosed several systems of linear features which may be important for the interpretation of the structural history of California. They are separated from an orthogonal system of linears in the Klamath Mts. by a set of discontinuous southeast-trending linear features (the Mendocino system) which is traceable from the Pacific Coast, at Cape Mendocino, into the eastern foothills of the Sierra Nevada. Within the Sierra Nevada, the Mendocino system separates the north-trending Sierran system from a set of linears characteristic of the Modoc Plateau. With minor exception, little overlap exists among the systems which suggests a decipherable chronology and evolutionary history for the region. The San Andres system of linears appears to truncate or co-exist with most of the other systems in the northern Coast Ranges. The Mendocino system truncates the Klamath, Sierran, and Modoc systems. The Sierran system may represent fundamental and long-persisting pre-late Paleozoic zones of crustal weakness which have been reactivated from time to time. The Mendocino system was possibly developed in early Mesozoic and is important to the structural framework of Northern California.

Rich, E. I.↗
DOE OSTI2024

Feature selection with distance correlation

Choosing which properties of the data to use as input to multivariate decision algorithms—also known as feature selection—is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-called automated feature engineering), for many tasks in physics, sets of theoretically well-motivated and well-understood features already exist. Working with such features can bring many benefits, including greater interpretability, reduced training and run time, and enhanced stability and robustness. We develop a new feature selection method based on distance correlation, and demonstrate its effectiveness on the tasks of boosted top- and W -tagging. Using our method to select features from a set of over 7,000 energy flow polynomials, we show that we can match the performance of much deeper architectures, by using only ten features and two orders-of-magnitude fewer model parameters. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2026

A multi-backend autotuning study of feature selection on GPUs

Abstract Feature selection is an important step in machine learning that can benefit from GPU acceleration. As the number of GPU vendors increases, it is imperative to adapt algorithms such as the minimum Redundancy Maximum Relevance (mRMR) feature selection method to different backends that support several GPU architectures. This work presents a multi-backend implementation of mRMR across CUDA, HIP, and SYCL, and studies its performance when combined with Bayesian optimization and transfer learning to automatically tune execution parameters for different platforms and datasets. Our experimental results show that when tuned, CUDA and HIP achieve comparable performance on NVIDIA architectures, while SYCL exhibits a moderate performance gap. Overall, this work highlights the impact of backend choice and autotuning on GPU-accelerated feature selection and provides insights into deploying mRMR across heterogeneous environments.

Beceiro, Bieito (ORCID:0000000333014890)↗
DOE OSTI2025

Implications of Safety and Operational Features of Small, Advanced Reactors for the Evaluation of Important Human Actions

The design and operational characteristics of non-light water reactors are likely to change the role of human actions in safety function management and the types of human actions that are deemed important. The objectives of this report are to: • Identify the implications of small, advanced reactor design characteristics on human performance and the changing role of human actions in the management of safety functions. • Identify the methods that may be used to identify important human actions. • Identify how HFE safety reviewers can help ensure that the methods adequately model human actions to identify those that are important to safety. We identified the implications of small, advanced reactor characteristics on the role of personnel in safety function management. Then we addressed how designers can identify which human actions are important to safety using both probabilistic risk assessment (PRA) and deterministic analyses. PRA identifies important human actions using risk-importance criteria. Deterministically identified important human actions include those identified by analyses of situations such as transients and accidents and defense in depth. In all cases, the acceptability of the analyses is dependent on the modeling, quantification, and criterion selection to determine which human actions are important. How well the designers address these processes determines the acceptability of their methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

climate change↗
NASA NTRS1993

Computation of turbulent flows over backward and forward-facing steps using a near-wall Reynolds stress model

Separation and reattachment of turbulent shear layers is observed in many important engineering applications, yet it is poorly understood. This has motivated many studies on understanding and predicting the processes of separation and reattachment of turbulent shear layers. Both of the situations in which separation is induced by adverse pressure gradient, or by discontinuities of geometry, have attracted attention of turbulence model developers. Formulation of turbulence closure models to describe the essential features of separated turbulent flows accurately is still a formidable task. Computations of separated flows associated with sharp-edged bluff bodies are described. For the past two decades, the backward-facing step flow, the simplest separated flow, has been a popular test case for turbulence models. Detailed studies on the performance of many turbulence models, including two equation turbulence models and Reynolds stress models, for flows over steps can be found in the papers by Thangam & Speziale and Lasher & Taulbee). These studies indicate that almost all the existing turbulence models fail to accurately predict many important features of back step flow such as reattachment length, recovery rate of the redeveloping boundary layers downstream of the reattachment point, streamlines near the reattachment point, and the skin friction coefficient. The main objectives are to calculate flows over backward and forward-facing steps using the NRSM and to make use of the newest DNS data for detailed comparison. This will give insights for possible improvements of the turbulence model.

Ko, Sung HO↗
NASA NTRS1983

Analysis of the quality of image data acquired by the LANDSAT-4 thematic mapper and multispectral scanners

The geometric quality of the TM and MSS film products were evaluated by making selective photo measurements such as scale, linear and area determinations; and by measuring the coordinates of known features on both the film products and map products and then relating these paired observations using a standard linear least squares regression approach. Quantitative interpretation tests are described which evaluate the quality and utility of the TM film products and various band combinations for detecting and identifying important forest and agricultural features.

Colwell, R. N.↗
NASA NTRSDate not supplied

Airborne Observations of Aerosol Properties in Southeast Asia: Overview of Emissions, Cloud Processing, and Long Range Transport during NASA CAMP2Ex

The Southwest Monsoon represents an important synoptic scale meteorological feature that modulates precipitation throughout Southeast Asia. In addition, southwesterly flow can funnel large amounts of biomass burning aerosol into heavily populated areas throughout the Philippines, mixing with urban emissions and representing a significant air quality issue. From a climate perspective, understanding interactions between these particles and clouds has important societal ramifications through frequent extreme periods of flood and drought. Here we present airborne observations made in support of the NASA CAMP2Ex (Cloud and Aerosol Monsoonal Processes-Philippines Experiment) mission aboard the Wallops Flight Facility P-3 research aircraft, based at Clark International Airport in the Luzon region of Northern Philippines. Nineteen research flights were conducted in August-October of 2019. An extensive suite of aerosol microphysical, optical, and chemical instrumentation was deployed, along with trace gas and remote sensing measurements, to characterize aerosol emissions and their vertical/spatial variability associated with moderately convective marine cloud systems in the region. Analysis will focus on characterizing disparate aerosol sources with respect to derived chemical and optical properties relevant to global circulation modeling. For example, higher than expected mass scattering efficiencies for aged biomass burning emissions, derived from a combination of nephelometer and aerosol mass spectrometer measurements, are attributed to increased particle size and decreased density. Results will also illustrate the evolution of aerosols from Indonesian agricultural biomass burning activities into the Philippines region, in comparison with urban emissions from the megacity Manila and transported air masses from mainland Asia.

Luke David Ziemba↗
NASA NTRS1981

Spaceborne and airborne imaging radar observations of sand dunes

An assessment of the ability of orbital synthetic aperture radar (SAR) imaging systems to provide useful information about aeolian features, and to determine how such a system might be constrained by the need to image these features, is presented. Seasat and aircraft radar imagery of five areas of sand dunes are studied, and compared to Landsat imagery and air photos, for two wavelengths (3.0 and 23.5 cm) and incidence angles ranging from 0 to 70 deg. It is shown that the illumination direction of the radar beam is important, since directional dune features must be oriented within 60 deg of perpendicular to the radar illumination direction in order to be imaged. It is concluded that the availability of radar imagery for two directions greatly facilitates interpretation of dune morphology and derivation of conclusions about causative wind regimes.

Blom, R.↗
DOE OSTI2019

Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules

Herein, we report machine learning algorithms by training data sets from a set of both successful and failed experiments for studying the crystallization propensity of metal–organic nanocapsules (MONCs). Among a variety of studied machine learning algorithms, XGBoost affords the highest prediction accuracy of >90%. The derived chemical feature scores that determine importance of reaction parameters from the XGBoost model assist to identify synthesis parameters for successfully synthesizing new hierarchical structures of MONCs, showing superior performance to a well-trained chemist. This work demonstrates that the machine learning algorithms can assist the chemists to faster search for the optimal reaction parameters from many experimental variables, whose features are usually hidden in the high-dimensional space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Radiation driven winds of hot stars: Some remarks on stationary models and spectrum synthesis in time-dependent simulations

The basic concept of some stationary models and the way in which these models are used for and in how far they influence the determination of stellar and wind parameters of massive hot stars are discussed. However, as follows directly from the assumption of stationarity, these models are inherently incapable of describing a number of additional observational features, which immediately show that the nonstationary aspects of the wind must be of significant importance. A number of these features are discussed: the soft X-ray emission; the discrete absorption components; the temporal variability of the wind lines, where the blue edges are varying most significantly while the red emission part remains relatively constant; the electron scattering wings of recombination lines in Wolf-Rayet stars; the black troughs in saturated P-Cygni profiles; the variability of optical lines; and the nonthermal radio emission observed in massive stars. The basic picture resulting from the present dynamical models is discussed.

Puls, Joachim↗
NASA NTRS1993

Decision Boundary Feature Extraction for Nonparametric Classification

Feature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining 'discriminantly informative features' and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for non-parametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

Lee, Chulhee↗
DOE OSTI2022

Nanoscale chemistry and ion segregation in zirconia-based ceramic at grain boundaries by atom probe tomography

In this work nanoscale features that are of importance in the stability of yttria-stabilized tetragonal zirconia (Y-TZP) are quantified through atom probe tomography. In-depth analysis of grain boundary chemistry revealed preferential segregation of lighter and smaller ions towards specific grain boundaries. The relationship between elemental segregation and the local atomistic structure is investigated at the sub-nanometer level to gain insights on nanoscale features associated with the tetragonal-monoclinic phase transformation in Y-TZP through grain boundary characterization. Furthermore, principal component analysis was implemented to reveal any potential biases from varied field evaporation across stabilizer-rich grain boundaries. The observed variations in ion density across different grains suggested a variation in field which was attributed to potential variations in grain crystal orientation. In order to reveal the subtle depletion of oxygen atoms within grain boundaries a new methodology to map oxygen vacancies is proposed, utilizing the relative neighborhood chemistry of individual yttrium atoms.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Lunar rilles and Hawaiian volcanic features - Possible analogues.

Study of the origin and development of lava conduits in terrestrial volcanic regions and the possible association of two important classes of lunar morphological features - i.e., channels and the sinuous rilles on the lunar maria - with stages in the evolution of lava conduits. The study was motivated by observations of some Hawaiian active volcanic regions. The origin of apparent flow channels on steep slopes on the moon is also considered.

Cruikshank, D. P.↗
DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗