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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Short-term nodal load forecasting based on machine learning techniques

This paper introduces an advanced Short-term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low-dimensional space compared to the original high-dimensional load space where features are barely possible to depict. Additionally, Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state-of-the-art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day-ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1986

Orbital spacecraft resupply technology

The resupplying of orbital spacecraft using the Space Shuttle, Orbital Maneuvering Vehicle, Orbital Transfer Vehicle or a depot supply at a Space Station is studied. The governing factor in fluid resupply designs is the system size with respect to fluid resupply quantities. Spacecraft propellant management for tankage via diaphragm or surface tension configurations is examined. The capabilities, operation, and application of adiabatic ullage compression, ullage exchange, vent/fill/repressurize, and drain/vent/no-vent fill/repressurize, which are proposed transfer methods for spacecraft utilizing tankage configurations, are described. Selection of the appropriate resupply method is dependent on the spacecraft design features. Hydrazine adiabatic compression/detonation, liquid-free vapor venting to prevent freezing, and a method for no-vent liquid filling are analyzed. Various procedures for accurate measurements of propellant mass in low gravity are evaluated; a system of flowmeters with a PVT system was selected as the pressurant solubility and quantity gaging technique. Monopropellant and bipropellant orbital spacecraft consumable resupply system tanks which resupply 3000 lb of hydrazine and 7000 lb of MMH/NTO to spacecraft on orbit are presented.

Eberhardt, R. N.↗
NASA NTRS1988

The ultraviolet spectrum of noncoronal late-type stars - The Gamma Crucis (M3.4 III) reference spectrum

A guide is presented to the UV spectrum of M-type giants and supergiants whose outer atmospheres contain warm chromospheres but not coronae. The M3 giant Gamma Crucis is taken as the archetype of the cooler, oxygen-rich, noncoronal stars. Line identifications and integrated line flux measurements of the chromospheric emission features seen in the 1200-3200 A range of IUE high-resolution spectra are presented. The major fluorescence processes operating in the outer atmosphere of Gamma Crucis, including eight previously unknown pumping processes and 21 new fluorescent line products, are summarized, and the enhancements of selected line strengths by 'line leakage' is discussed. A set of absorption features toward the longer wavelength end of this range is identified which can be used to characterize the radial velocity of the stellar photospheres. The applicability of the results to the spectra of noncoronal stars with different effective temperatures and gravities is discussed.

Carpenter, Kenneth G.↗
DOE OSTI2026

Selective CO 2 Reduction by Bis(bipyridine)cobalt(II) Catalysts: The Role of Pendant Pyridine as a Proton Acceptor

Electrochemical CO 2 reduction reaction (CO 2 RR) catalyzed by molecular earth-abundant metal catalysts is a promising strategy to convert CO 2 into value-added products. One recent trend in this field has been focusing on the rational design of catalysts by incorporating redox-active ligands and modifying the secondary coordination sphere (SCS) to achieve efficient and selective CO 2 RR. Herein, we report a series of Co bis­(bipyridine) catalysts featuring various dangling groups, such as pyridine, tertiary amine, or butyl, in the secondary coordination sphere (Co-PyMe, Co-Py, Co-PrN, and Co-Bu). Efficient, selective electrocatalytic CO 2 RR was achieved by the complexes after the generation of triply reduced intermediate consisting of a Co I center and a dianionic ligand, producing CO as the major product and trace amount of H 2 . Strong correlations with the identity of dangling groups and turnover frequency (TOF) have been observed, in which Co-PyMe displayed the highest TOF (1086 s –1 in MeCN/H 2 O). Mechanistic studies indicated that the acceleration of CO 2 RR with pyridine-functionalized catalysts were derived from the protonation of pyridine dangling groups which participated as weak acids in the H-bonding network with exogenous proton sources, stabilizing CO 2 -bound intermediates and facilitating proton transfer. In addition, precatalytic CO 2 binding and activation at the third reduction (−2.1 V) was revealed by CV and SEC-IR studies. The resultant doubly reduced CO-bound species acted as a trapping state which inhibited CO 2 RR electrocatalysis. Regeneration of active species was accessed via reductive dissociation of CO at a more negative potential. In conclusion, this study highlights the combined effects of redox-active ligands and pyridine/pyridinium as SCS groups on CO 2 RR catalysis and provides design principles for future development of CO 2 RR catalysts utilizing pyridine/pyridiniums as SCS functional groups to fine-tune the catalytic activity.

CO2 reduction↗
NASA NTRSDate not supplied

Virtual Fit Assessment: Validation using Historical Spacesuit Fit Data

Virtual fit tests using 3D body scans have provided a cost-effective means to predictively assess spacesuit fit for the current and future astronaut population. However, fit is a complex issue influenced by physical interferences, subjective preference, and many other factors. Fit can substantially differ between the type of hardware, environmental conditions, and tasks being performed. Namely, fit changes across different contexts, such as 3D printed mockup evaluations, pressurized one-g suited test events, neutral buoyancy and other training events, and flight extravehicular activities (EVA). These challenges make it difficult to validate virtual fit frameworks to physical suit sizing. This study proposes a new method of validation using the wealth of historically archived suit fit data. Physically Assessed Fit (PAF) data was assessed from astronauts and test volunteers who wore the legacy Extravehicular Mobility Unit (EMU). The data was collected during the past decades at NASA and considered to be most reliable and dependable. Each participant was 3D scanned for body shape and spacesuit-critical dimensions were measured per NASA guidelines. The participants’ size preference for hard upper torso (HUT) assembly was retrieved from their pressurized suit fit check records. Fit assessments were also updated after one-g EVA training, neutral buoyancy training, and EVA flights. Virtually Assessed Fit (VAF) was done by overlaying the CAD model of a HUT with the 3D body scans of the PAF participants. Each body scan was iteratively adjusted for the position inside the HUT to minimize the suit-to-body contact interference while satisfying a set of prescribed requirements. Then the residual contact area, depth, and volume were quantified as VAF metrics. This process was repeated for the different HUT sizes, including medium, large, and extra-large. Statistical modeling is currently in progress and will be presented at the conference. A statistical classifier will be developed to predict a fit probability for different HUT sizes as a function of the corresponding person’s VAF metrics. The HUT size with the highest fit probability will constitute the most likely size selection for the person, and the prediction will be compared against the corresponding PAF data. The participant data will be randomly pre-grouped into either a model development or validation subset. While the model development subset will be used to build the probability model, the validation subset will be used to assess the model accuracy. Also, the locations and magnitudes of suit-body-contacts will be estimated from VAF. This information can identify the critical suit geometry and body shape features that influence suit fit. The variations found in PAF size selections by subjects with similar VAF metrics will allow for investigations into subjective preferences, for example, tight versus loose fit. Overall, this study is expected to provide a structured validation of a virtual suit fit framework, which has not been possible in the past. The outcome can also provide useful insights and potential limitations for interpreting virtual fit tests for future spacesuit designs and population accommodation.

Han Kim↗
NASA NTRS1997

Photonic Switching Devices Using Light Bullets

The present invention is directed toward a unique ultra-fast, all-optical switching device or switch made with readily available, relatively inexpensive, highly nonlinear photonic glasses. These photonic glasses have a sufficiently negative group velocity dispersion and high nonlinear index of refraction to support stable light bullets. The light bullets counterpropagate through, and interact within the waveguide to selectively change each others' directions of propagation into predetermined channels. In one embodiment, the switch utilizes a rectangularly planar slab waveguide, and further includes two central channels and a plurality of lateral channels for guiding the light bullets into and out of the waveguide. One advantage presented by the present all-optical switching device lies in its practical use of light bullets, thus preventing the degeneration of the pulses due to dispersion and diffraction at the front and back of the pulses. Another feature of the switching device is the relative insensitivity of the collision process to the time difference in which the counter-propagating pulses enter the waveguide. since. contrary to conventional co-propagating spatial solitons, the relative phase of the colliding pulses does not affect the interaction of these pulses. Yet another feature of the present all-optical switching device is the selection of the light pulse parameters which enables the generation of light bullets in highly nonlinear glasses.

Goorjian, Peter M.↗
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2021

Widespread misregulation of inter-species hybrid transcriptomes due to sex-specific and sex-chromosome regulatory evolution

When gene regulatory networks diverge between species, their dysfunctional expression in inter-species hybrid individuals can create genetic incompatibilities that generate the developmental defects responsible for intrinsic post-zygotic reproductive isolation. Both cis- and trans-acting regulatory divergence can be hastened by directional selection through adaptation, sexual selection, and inter-sexual conflict, in addition to cryptic evolution under stabilizing selection. Dysfunctional sex-biased gene expression, in particular, may provide an important source of sexually-dimorphic genetic incompatibilities. Here, we characterize and compare male and female/hermaphrodite transcriptome profiles for sibling nematode species Caenorhabditis briggsae and C. nigoni, along with allele-specific expression in their F 1 hybrids, to deconvolve features of expression divergence and regulatory dysfunction. Despite evidence of widespread stabilizing selection on gene expression, misexpression of sex-biased genes pervades F 1 hybrids of both sexes. This finding implicates greater fragility of male genetic networks to produce dysfunctional organismal phenotypes. Spermatogenesis genes are especially prone to high divergence in both expression and coding sequences, consistent with a “faster male” model for Haldane’s rule and elevated sterility of hybrid males. Moreover, underdominant expression pervades male-biased genes compared to female-biased and sex-neutral genes and an excess of cis-trans compensatory regulatory divergence for X-linked genes underscores a “large-X effect” for hybrid male expression dysfunction. Extensive regulatory divergence in sex determination pathway genes likely contributes to demasculinization of XX hybrids. The evolution of genetic incompatibilities due to regulatory versus coding sequence divergence, however, are expected to arise in an uncorrelated fashion. This study identifies important differences between the sexes in how regulatory networks diverge to contribute to sex-biases in how genetic incompatibilities manifest during the speciation process.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Acoustic design of the QCSEE propulsion systems

Acoustic design features and techniques employed in the Quiet Clean Short-Haul Experimental Engine (QCSEE) Program are described. The role of jet/flap noise in selecting the engine fan pressure ratio for powered lift propulsion systems is discussed. The QCSEE acoustic design features include a hybrid inlet (near-sonic throat velocity with acoustic treatment); low fan and core pressure ratios; low fan tip speeds; gear-driven fans; high and low frequency stacked core noise treatment; multiple-thickness treatment; bulk absorber treatment; and treatment on the stator vanes. The QCSEE designs represent and anticipated acoustic technology improvement of 12 to 16 PNdb relative to the noise levels of the low-noise engines used on current wide-body commercial jet transport aircraft.

Loeffler, I. J.↗
NASA NTRS1973

Application of ERTS-1 imagery to detecting and mapping modern erosion features and to monitoring erosional changes, in southern Arizona

The author has identified the following significant results. The chief results during the reporting period were three 1:1,000,000 scale maps made from one ERTS-1 frame (1085-17330, 16 October 1972) showing: (1) the three most important types of materials in terms of the modern erosion problem: the readily erodible soils, gravel piedmonts and basin-fill areas, and consolidated rocks; (2) alluvial fans (dissected and relatively undissected); and (3) (as an additional bonus) linear structural features. Eight key areas (small parts of the whole study area) were selected for detailed study, and mapping was started in two of them, by interpretation of ultrahigh (U-2 and RB-57) airphotos, supplemented by field studies. In these areas detailed mapping was done not only on the modern erosion phenomena (arroyos, gullies, modern flood plains and terraces, and areas of sheet erosion and deposition), but also other features pertinent to the erosion problem, such as slope-local relief, landforms rock units, soil particle size and erodibility, and classes of vegetative cover.

Morrison, R. B.↗
DOE OSTI2020

Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Multivariate Bayesian analysis can bring significant benefits to seismic hazard analysis: Its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure; and Its updating feature enables a seamless integration of new ground motion data into the hazard results. Here, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault-type, magnitude, and distance dependencies, and also the linear and the rock intensity dependent site response. We use a hybrid Markov Chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis-Hastings step. We then perform several checks on the model and note that its performance is satisfactory. Finally, we illustrate the merits of this multivariate Bayesian analysis, which include: ground motion model updating with ground motion data recorded in the last four years not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.

58 GEOSCIENCES↗
NASA NTRS1972

Fatigue and fail-safe design features of the DC-10 airplane

The philosophy and methods used in the design of the DC-10 aircraft to assure structural reliability against cracks under repeated service loads are described in detail. The approach consists of three complementary parts: (1) the structure is designed to be fatigue resistant for a crack-free life of 60,000 flight hours; (2) inasmuch as small undetected cracks could develop from other sources, such as material flaws and manufacturing preloads, the structure also is designed to arrest and control cracks within a reasonable service-inspection interval; and (3) a meaningful service-inspection program has been defined on the basis of analysis and test experience from the design development program. This service-inspection program closes the loop to assure the structural integrity of the DC-10 airframe. Selected materials, fasteners, and structural arrangements are used to achieve these design features with minimum structural weight and with economy in manufacturing and maintenance. Extensive analyses and testing were performed to develop and verify the design. The basic design considerations for fatigue-resistant structure are illustrated in terms of material selection, design loads spectra, methods for accurate stress and fatigue damage analysis, and proven concepts for efficient detail design.

Stone, M. E.↗
DOE OSTI2023

Energy-Efficient and Water-Saving Sorbent Regeneration at Near Room Temperature for Direct Air Capture

Here this study reports energy-efficient and water-saving microwave-accelerated regeneration of sorbent (MARS) for dilute carbon capture from the ambient air. The experimental studies indicated that the CO 2 desorption rate from the chemisorbents increased with microwave output under near-isothermal conditions at near room temperature. The reduced activation energy of MARS, i.e., 20–28 kJ/mol, indicated enhanced CO 2 desorption kinetics by microwave-induced rotational–vibrational (rovibrational) coupling transitions, primarily due to the highly polarized feature of the carbamate (CO 2 -PEI). The instant and selective delivery of microwave energy to the targeted polarized C–N bonds at room temperature is particularly advantageous for energy-efficient direct air capture. The experimental results also demonstrated a good working capacity of 0.6–1.4 mmol of CO 2 /g and a promising rapid MARS-DAC process with microwave swing. As it does not require steam regeneration and heat exchanger, a simple MARS process is attractive for CO 2 capture in water-stressed regions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A novel hardmask-to-substrate pattern transfer method for creating 3D, multi-level, hierarchical, high aspect-ratio structures for applications in microfluidics and cooling technologies

Abstract This letter solves a major hurdle that mars photolithography-based fabrication of micro-mesoscale structures in silicon. Conventional photolithography is usually performed on smooth, flat wafer surfaces to lay a 2D design and subsequently etch it to create single-level features. It is, however, unable to process non-flat surfaces or already etched wafers and create more than one level in the structure. In this study, we have described a novel cleanroom-based process flow that allows for easy creation of such multi-level, hierarchical 3D structures in a substrate. This is achieved by introducing an ultra-thin sacrificial silicon dioxide hardmask layer on the substrate which is first 3D patterned via multiple rounds of lithography. This 3D pattern is then scaled vertically by a factor of 200–300 and transferred to the substrate underneath via a single shot deep etching step. The proposed method is also easily characterizable—using features of different topographies and dimensions, the etch rates and selectivities were quantified; this characterization information was later used while fabricating specific target structures. Furthermore, this study comprehensively compares the novel pattern transfer technique to already existing methods of creating multi-level structures, like grayscale lithography and chip stacking. The proposed process was found to be cheaper, faster, and easier to standardize compared to other methods—this made the overall process more reliable and repeatable. We hope it will encourage more research into hybrid structures that hold the key to dramatic performance improvements in several micro-mesoscale devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Modeling of the metal–insulator transition temperature in alio-valently doped VO 2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO 2 ) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO 2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

36 MATERIALS SCIENCE↗
DOE OSTI2022

RF-Net 2: fast inference of virus reassortment and hybridization networks

Abstract Motivation A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g. hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens. Results Here, we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. In addition, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical IAV evolutionary analyses. Availability and implementation RF-Net 2 is available at https://github.com/flu-crew/rf-net-2. Supplementary information Supplementary data are available at Bioinformatics online.

Markin, Alexey (ORCID:0000000332809050)↗
DOE OSTI2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

97 MATHEMATICS AND COMPUTING↗