Search NASA⌕ Search

SEARCH · Search NASA

Results for “variable order”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Probabilistic Reasoning for Plan Robustness

A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.

autonomous planning↗
DOE OSTI2020

Stockpile Assessment (Quad Chart)

Our goal was to characterize certain aspects of shaped charges. In order to determine the pressure field created by the jet and the jet velocity, I worked with and modified simulations using CTH, a code developed at Sandia. I was able to manipulate and modify certain variables in order to observe their effects and measure the pressure and velocities at different points in the simulation.

42 ENGINEERING↗
DOE OSTI2021

Physical, resource supply, and biological controls on nutrient processing along the river continuum

Nutrient impairment has led to damages to US surface and groundwater systems in excess of 100 billion dollars per year. Therefore, there is a strong need to develop methods to predict the transport, uptake, and export of nutrients along fluvial networks. We present results that are based on a data-driven mechanistic understanding of three factors that largely control nutrient uptake and export: 1) interactions between transport-related processes (mass transfer to metabolically active zones), 2) resource supply dynamics (nutrient concentration, stoichiometric constraints, etc.), and 3) biological controls (microbial community structure and function). Our results were generated from column experiments conducted along the Jemez River-Rio Grande continuum, which spans four orders of magnitude in mean annual discharge, more than 2000 m in altitude, and more than 500 km of stream longitude. Two resource supply injections were performed on each of the columns, i.e., a nitrate only addition, followed by a stoichiometrically ‘balanced’ 106Carbon:16Nitrogen:1Phosphorus addition. We quantified NO3-N uptake kinetics while constraining three variables: stream order, sediment type and type of injection (N vs stoichiometrically ‘balanced’ C:N:P). Following the laboratory nutrient uptake experiments, the columns were destructively sampled and the contents were homogenized to collect subsamples for DNA sequencing. Amplicon analysis was carried out as described by the Earth Microbiome Protocol for 16s and ITS sequencing.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Space sickness predictors suggest fluid shift involvement and possible countermeasures

Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity.

Simanonok, K. E.↗
DOE OSTI2018

Observation of multiple-muon seasonal variations in the NOνA Near Detector

The interaction of a cosmic ray particle with an element of the atmosphere results in a cascade of particles, known as extensive air showers, which can be divided into three main branches, known as the hadronic, the electromagnetic, and the muonic component. As for the latter, since muons can reach high depths underground, they are generally used to study cosmic rays at different underground depths. The dynamics of extensive air showers is directly connected to the density of the atmosphere, as it defines the probability of particles to decay or interact. As muons are mainly produced from the decay of pions and kaons, and a warmer atmosphere results in higher number of meson decays, such particles are prone to suffer a seasonality effect that is directly correlated to the yearly seasonal variations of the atmosphere, an effect that has been verified by a large number of experiments over the past six decades. In 2015 the MINOS experiment presented an anti-correlation between the effective temperature of the atmosphere and the seasonality of the muon flux for multiplicities higher than one (i.e. more than one muon track per cosmic ray event). Said anti-correlation is not yet fully understood, counting with only a qualitative hypothesis as a probable mechanism. As such, the main goals of this study are to verify the MINOS anti-correlation effect and extend the study to verify the seasonality of the effect as a function of different variables in order to improve the understanding of the phenomenon and possible corroborations with the known hypothesis. Two full years of the NO$\nu$A Near Detector, ranging from April 2015 to April 2017, were used as the dataset for the analysis. The anti-correlation between the multiple muon flux and the effective temperature of the atmosphere is confirmed by the NO$\nu$A Near Detector, being in full agreement with the results presented by the MINOS Collaboration. The seasonal effect is also broken down by different variables: i) track separation, ii) zenith angle, iii) track angular separation, and iv) multiplicity. Different regions of these variables represent different energy ranges for the detected underground muons, their hadron parents or the primary particles that originated the cosmic ray shower, being a way to verify any particular dependency with energy. The results show that there are no clear trends in any of the studied variables, except for the multiplicity, in which the intensity of the seasonal variation increases for higher multiplicities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1996

Effects of Shocks on Emission from Central Engines of Active Galactic Nuclei. I

In this paper we show that perturbations of the accretion flow within the central engines of some active galactic nuclei (AGNS) are likely to form shock waves in the accreting plasma. Such shocks, which may be either collisional or collisionless, can contribute to the observed high-energy temporal and spectral variability. Our rationale is the following: Observations show that the continuum emission probably originates in an optically thin, hot plasma in the AGN central engine. The flux and spectrum from this hot plasma varies significantly over light crossing timescales. Several authors have suggested that macroscopic perturbations contained within this plasma are the sources of this variability. In order to produce the observed emission the perturbations must be radiatively coupled with the optically thin hot matter and must also move with high velocities. We suggest that shocks, which can be very effective in randomizing the bulk motion of the perturbations, are responsible for this coupling. Shocks should form in the central engine, because the temperatures and magnetic fields are probably reduced below their virial values by radiative dissipation. Perturbations moving at Keplerian speeds, or strong non-linear excitations, result in supersonic and super-Alfvenic velocities leading to shock waves within the hot plasma. We show that even a perturbation smaller than the emitting region can form a shock that significantly modifies the continuum emission in an AGN, and that the spectral and temporal variability from such a shock generally resembles those of radio-quiet AGNS. As an example, the shock inducing perturbation in our model is a small main-sequence star, the capturing and eventual accretion of which are known to be a plausible process. We argue that shocks in the central engine may also provide a natural triggering mechanism for the "cold" component of Guilbert & Rees two-phase medium and an efficient mecha- nism for angular momentum transfer. Current and future missions, such as ASCA, XTE, XMM, AXAF, and ASTRO-E may determine the importance of shock-related emission from the central engines of AGNS.

Sivron, R.↗
DOE OSTI2017

Uncertainty Quantification in Scale-Dependent Models of Flow in Porous Media

Equations governing flow and transport in heterogeneous porous media are scale-dependent. We demonstrate that it is possible to identify a support scale $\eta^*$, such that the typically employed approximate formulations of Moment Equations (ME) yield accurate (statistical) moments of a target environmental state variable. Under these circumstances, the ME approach can be used as an alternative to the Monte Carlo (MC) method for Uncertainty Quantification in diverse fields of Earth and environmental sciences. MEs are directly satisfied by the leading moments of the quantities of interest and are defined on the same support scale as the governing stochastic partial differential equations (PDEs). Computable approximations of the otherwise exact MEs can be obtained through perturbation expansion of moments of the state variables in orders of the standard deviation of the random model parameters. As such, their convergence is guaranteed only for the standard deviation smaller than one. Furthermore, we demonstrate our approach in the context of steady-state groundwater flow in a porous medium with a spatially random hydraulic conductivity.

42 ENGINEERING↗
DOE OSTI2019

Inclusive search for supersymmetry in pp collisions at $ \sqrt{s}=13 $ TeV using razor variables and boosted object identification in zero and one lepton final states

An inclusive search for supersymmetry (SUSY) using the razor variables is performed using a data sample of proton-proton collisions corresponding to an integrated luminosity of 35.9 fb$^{-1}$, collected with the CMS experiment in 2016 at a center-of-mass energy of $\sqrt{s} =$ 13 TeV. The search looks for an excess of events with large transverse energy, large jet multiplicity, and large missing transverse momentum. The razor kinematic variables are sensitive to large mass differences between the parent particle and the invisible particles of a decay chain and help to identify the presence of SUSY particles. The search covers final states with zero or one charged lepton and features event categories divided according to the presence of a high transverse momentum hadronically decaying W boson or top quark, the number of jets, the number of b-tagged jets, and the values of the razor kinematic variables, in order to separate signal from background for a broad range of SUSY signatures. The addition of the boosted W boson and top quark categories within the analysis further increases the sensitivity of the search, particularly to signal models with large mass splitting between the produced gluino or squark and the lightest SUSY particle. Furthermore, the analysis is interpreted using simplified models of $R$-parity conserving SUSY, focusing on gluino pair production and top squark pair production. Limits on the gluino mass extend to 2.0 TeV, while limits on top squark mass reach 1.14 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists’ reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists’ reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Weighted Distances in Scale-Free Configuration Models

In this paper we study first-passage percolation in the configuration model with empirical degree distribution that follows a power-law with exponent τ∈(2,3). We assign independent and identically distributed (i.i.d.) weights to the edges of the graph. We investigate the weighted distance (the length of the shortest weighted path) between two uniformly chosen vertices, called typical distances. When the underlying age-dependent branching process approximating the local neighborhoods of vertices is found to produce infinitely many individuals in finite time—called explosive branching process—Baroni, Hofstad and the second author showed in Baroni et al. (J Appl Probab 54(1):146–164, 2017) that typical distances converge in distribution to a bounded random variable. The order of magnitude of typical distances remained open for the τ∈(2,3) case when the underlying branching process is not explosive. We close this gap by determining the first order of magnitude of typical distances in this regime for arbitrary, not necessary continuous edge-weight distributions that produce a non-explosive age-dependent branching process with infinite mean power-law offspring distributions. This sequence tends to infinity with the amount of vertices, and, by choosing an appropriate weight distribution, can be tuned to be any growing function that is O(loglog n), where n is the number of vertices in the graph. We show that the result remains valid for the the erased configuration model as well, where we delete loops and any second and further edges between two vertices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Comparative study of air-conditioning energy use of four office buildings in China and USA

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. Here, this study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. In conclusion, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

High frequency (4th order) sequence stratigraphy of Early Miocene deltaic shorelines, offshore Texas and Louisiana

Well logs and 3D seismic reflection data are integrated to image Lower Miocene depocenters of the upper Texas and westernmost Louisiana coastal and offshore areas. Although previously interpreted at a large scale (3rd order cycles), the detailed stratigraphy and depositional history of the early Miocene succession has not been fully developed. The upper lower Miocene interval from Robulus L (MFS10) to Amphistegina B (MFS9) has been further subdivided into five 4th order cycles (each 200 to 300 m thick) to provide finer scale stratigraphic detail and interpretation of depositional environments. Results of the finer scale correlations are presented in a series of sandstone percent maps. Overall, the maps display a strike-elongate (sub-parallel to the present day coastline) orientation for the sandstone bodies which thin to the southeast (basinward). Net sandstone thickness is relatively low in older cycles and increases in younger cycles suggesting an overall progradation of the deltaic system into the mapped area through time. Continued marine regression and southward movement of the shoreline are also confirmed by seismic amplitude 2 maps. After a major transgression associated with Robulus L deltaic progradation occurred under rising sea level conditions. Deltaic sediments prograded southward and sandstone brought to the upper Texas coast was distributed laterally by longshore currents to form strandplain coast lines. Maximum regression occurred during the interval MFS9_2 to MFS9_3 when a deltaic depocenter formed offshore Texas in east High Island area. However, sandstone thickness progressively decreased laterally to the east (offshore Louisiana) where more shale was present and marine processes reworked deltaic derived sandstones into shore parallel bars. There was a clear retreat of the shoreline during the youngest cycle marking the beginning of transgression associated with Amphistegina B . Detailed stratigraphic interpretation (at a 4th order scale) shows more variability in the dominant shoreline processes during early Miocene than previously thought. Recognition of sedimentary systems variability at a 4th order scale is critical for improved hydrocarbon exploration and in understanding potential future CO 2 storage in the area.

58 GEOSCIENCES↗
DOE OSTI2017

Surface Mechanoengineering of a Zr-based Bulk Metallic Glass via Ar-Nanobubble Doping to Probe Cell Sensitivity to Rigid Materials

In this paper, a new materials platform, utilizing the amorphous microstructure of bulk metallic glasses (BMGs) and the versatility of ion implantation, was developed for the fundamental investigation of cell responses to substrate-rigidity variations in the gigapascal modulus range, which was previously unattainable with polymeric materials. The surface rigidity of a Zr-Al- Ni-Cu-Y BMG was modulated with low-energy Ar-ion implantation owing to the impartment of Ar nanobubbles into the amorphous matrix. Surface softening was achieved due to the formation of nanobubble-doped transitional zones in the Zrbased BMG substrate. Bone-forming cell studies on this newly designed platform demonstrated that mechanical cues, accompanied with the potential effects of other surface properties (i.e. roughness, morphology, and chemistry), contributed to modulating cell behaviors. Cell adhesion and actin filaments were found to be less established on less stiff surfaces, especially on the surface with an elastic modulus of 51 GPa. Cell growth appeared to be affected by surface mechanical properties. A lower stiffness was generally related to a higher growth rate. Findings in this study broadened our fundamental understanding concerning the mechanosensing of bone cells on stiff substrates. It also suggests that surface mechano-engineering of metallic materials could be a potential strategy to promote osseointegration of such materials for bone-implant applications. Further investigations are proposed to fine tune the ion implantation variables in order to further distinguish the surface-mechanical effect on bone-forming cell activities from the contributions of other surface properties.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Standards and Protocols for Data Acquisition and Reporting for Studies of the Electrochemical Reduction of Carbon Dioxide

Objective evaluation of the performance of electrocatalysts for CO 2 reduction has been complicated by a lack of standardized methods for measuring and reporting activity data. In this perspective, we advocate that standardizing these practices can aid in advancing research efforts toward the development of efficient and selective CO 2 reduction electrocatalysts. Using information taken from experimental studies, we identify variables that influence the measured activity of CO 2 reduction electrocatalysts and propose procedures to account for these variables in order to improve the accuracy and reproducibility of reported data. We recommend that catalysts be measured under conditions which do not introduce artifacts from impurities, from either the electrolyte or counter electrode, and advocate the acquisition of data measured in the absence of mass transport effects. Furthermore, measured rates of electrochemical reactions should be normalized to both the geometric electrode area as well as the electrochemically active surface area to facilitate the comparison of reported catalysts with those previously known. We demonstrate that, when these factors are accounted for, the CO 2 reduction activities of Ag and Cu measured in different laboratories exhibit little difference. Furthermore, adoption of the recommendations presented in this perspective would greatly facilitate the identification of superior catalysts for CO 2 reduction arising solely from changes in their composition and pretreatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Enhanced jet stream waviness induced by suppressed tropical Pacific convection during boreal summer

Abstract Consensus on the cause of recent midlatitude circulation changes toward a wavier manner in the Northern Hemisphere has not been reached, albeit a number of studies collectively suggest that this phenomenon is driven by global warming and associated Arctic amplification. Here, through a fingerprint analysis of various global simulations and a tropical heating-imposed experiment, we suggest that the suppression of tropical convection along the Inter Tropical Convergence Zone induced by sea surface temperature (SST) cooling trends over the tropical Eastern Pacific contributed to the increased summertime midlatitude waviness in the past 40 years through the generation of a Rossby-wave-train propagating within the jet waveguide and the reduced north-south temperature gradient. This perspective indicates less of an influence from the Arctic amplification on the observed mid-latitude wave amplification than what was previously estimated. This study also emphasizes the need to better predict the tropical Pacific SST variability in order to project the summer jet waviness and consequent weather extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity

Abstract Earth is currently undergoing a global increase in atmospheric vapor pressure deficit (VPD), a trend which is expected to continue as climate warms. This phenomenon has been associated with productivity decreases in ecosystems and yield penalties in crops, with these losses attributed to photosynthetic limitations arising from decreased stomatal conductance. Such VPD increases, however, have occurred over decades, which raises the possibility that stomatal acclimation to VPD plays an important role in determining plant productivity under high VPD. Furthermore, evidence points to more far‐ranging and complex effects of elevated VPD on plant physiology, extending to the anatomical, biochemical, and developmental levels, which could vary substantially across species. Because these complex effects are typically not considered in modeling frameworks, we conducted a quantitative literature review documenting temperature‐independent VPD effects on 112 species and 59 traits and physiological variables, in order to develop an integrated and mechanistic physiological framework. We found that VPD increase reduced yield and primary productivity, an effect that was partially mediated by stomatal acclimation, and also linked with changes in leaf anatomy, nutrient, and hormonal status. The productivity decrease was also associated with negative effects on reproductive development, and changes in architecture and growth rates that could decrease the evaporative surface or minimize embolism risk. Cross‐species quantitative relationships were found between levels of VPD increase and trait responses, and we found differences across plant groups, indicating that future VPD impacts will depend on community assembly and crop functional diversity. Our analysis confirms predictions arising from the hydraulic corollary to Darcy's law, outlines a systemic physiological framework of plant responses to rising VPD, and provides recommendations for future research to better understand and mitigate VPD‐mediated climate change effects on ecosystems and agro‐systems.

54 ENVIRONMENTAL SCIENCES↗