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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Urban Green Spaces, Greenness Exposure and Species Richness in Residential Environments and Relations with Physical Activity and BMI in Portuguese Adolescents

Environmental factors play an important role in obesity-related behaviors. Evidence indicates significant associations between weight and urban green spaces in adults, but it is not clear whether this relationship applies to adolescents. Therefore, our aim was to determine the associations between urban green spaces, greenness exposure and species richness in residential environments with physical activity and body mass index. Sixty-two adolescents between 12 and 18 years of age answered a self-administered questionnaire, providing information on height, weight, age, sex and home address. Data on socioeconomic deprivation were obtained from the European Index of Deprivation for Small Portuguese Areas. Physical activity levels were assessed using accelerometers. Urban green space counts and the normalized difference vegetation index values were measured using buffers along the roads with distances of 300, 500, 1000 and 1500 m from each participant’s residence. To quantify the species richness, the species richness index was used. Linear regression models were fitted to analyze whether urban green spaces, exposure to green spaces and species richness counts for each distance were associated with physical activity and self-reported body mass index. We did not find significant associations between the independent variables and the probability of overweight or obesity. The relationship between environmental variables, adolescents’ physical activity and body weight seems to be complex and further studies may contribute to better understanding of the topic.

Melo, Juliana (ORCID:0000000301275340)↗
DOE OSTI2025

Core Physics Characteristics of Extended Enrichment and High Burnup Boiling Water Reactor Fuel

This paper presents the highlights of boiling water reactor (BWR) core physics studies performed at Oak Ridge National Laboratory as part of a series of studies conducted to compare low-enriched uranium (LEU) with LEU+ fuel. The studies analyzed isotopic fuel content, lattice parameters (Phase 1), and core physics (Phase 2) to identify challenges in operation, storage, and transportation for BWRs and pressurized water reactors (PWRs). Because of a lack of publicly available lattice and core designs for modern BWR fuel assemblies and reactor cores, several optimized lattice designs were generated, and different core loading strategies were investigated. Twelve optimized lattice designs with 235 U enrichments ranging from 1.6% to 9% and gadolinia loadings ranging from 3 to 8 wt% were used to model axial enrichment and geometry variations in fuel assemblies for core designs. Each core shares a common set of approximations in design and analysis to allow for consistent comparisons between LEU and LEU+ fuel. The objective is to highlight anticipated changes in core behavior with respect to the reference LEU core. The results of this study show that the differences in LEU and LEU+ core reactor physics characteristics are less significant than the differences in lattice physics characteristics reported in the Phase 1 studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Applying Physics-Informed Neural Networks to Solve Navier–Stokes Equations for Laminar Flow around a Particle

In recent years, Physics-Informed Neural Networks (PINNs) have drawn great interest among researchers as a tool to solve computational physics problems. Unlike conventional neural networks, which are black-box models that “blindly” establish a correlation between input and output variables using a large quantity of labeled data, PINNs directly embed physical laws (primarily partial differential equations) within the loss function of neural networks. By minimizing the loss function, this approach allows the output variables to automatically satisfy physical equations without the need for labeled data. The Navier–Stokes equation is one of the most classic governing equations in thermal fluid engineering. This study constructs a PINN to solve the Navier–Stokes equations for a 2D incompressible laminar flow problem. Flows passing around a 2D circular particle are chosen as the benchmark case, and an elliptical particle is also examined to enrich the research. The velocity and pressure fields are predicted by the PINNs, and the results are compared with those derived from Computational Fluid Dynamics (CFD). Additionally, the particle drag force coefficient is calculated to quantify the discrepancy in the results of the PINNs as compared to CFD outcomes. The drag coefficient maintained an error within 10% across all test scenarios.

Hu, Beichao (ORCID:0009000163215151)↗
DOE OSTI2020

Physics-guided Deep Learning for Power System State Estimation

In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

PROBABILITY OF THE PHYSICAL ASSOCIATION OF 104 BLENDED COMPANIONS TO KEPLER OBJECTS OF INTEREST USING VISIBLE AND NEAR-INFRARED ADAPTIVE OPTICS PHOTOMETRY

We determine probabilities of physical association for stars in blended Kepler Objects of Interest (KOIs), and find that 14.5%{sub −3.4%}{sup +3.8%} of companions within ∼4″ are consistent with being physically unassociated with their primary. This produces a better understanding of potential false positives in the Kepler catalog and will guide models of planet formation in binary systems. Physical association is determined through two methods of calculating multi-band photometric parallax using visible and near-infrared adaptive optics observations of 84 KOI systems with 104 contaminating companions within ∼4″. We find no evidence that KOI companions with separations of less than 1″ are more likely to be physically associated than KOI companions generally. We also reinterpret transit depths for 94 planet candidates, and calculate that 2.6% ± 0.4% of transits have R>15R{sub ⊕}, which is consistent with prior modeling work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

GMOSS: ALL-SKY MODEL OF SPECTRAL RADIO BRIGHTNESS BASED ON PHYSICAL COMPONENTS AND ASSOCIATED RADIATIVE PROCESSES

We present the Global Model for the Radio Sky Spectrum (GMOSS), a novel, physically motivated model of the low-frequency radio sky from 22 MHz to 23 GHz. GMOSS invokes different physical components and associated radiative processes to describe the sky spectrum over 3072 pixels of 5° resolution. The spectra are allowed to be convex, concave, or of more complex form with contributions from synchrotron emission, thermal emission, and free–free absorption included. Physical parameters that describe the model are optimized to best fit four all-sky maps at 150 MHz, 408 MHz, 1420 MHz, and 23 GHz and two maps at 22 and 45 MHz generated using the Global Sky Model of de Oliveira-Costa et al. The fractional deviation of the model from data has a median value of 6% and is less than 17% for 99% of the pixels. Though aimed at the modeling of foregrounds for the global signal arising from the redshifted 21 cm line of hydrogen during the Cosmic Dawn and the Epoch of Reionization (EoR), over redshifts 150≲z≲6, GMOSS is well suited for any application that requires simulating spectra of the low-frequency radio sky as would be observed by the beam of any instrument. The complexity in spectral structure that naturally arises from the underlying physics of the model provides a useful expectation for departures from smoothness in EoR foreground spectra and hence may guide the development of algorithms for EoR signal detection. This aspect is further explored in a subsequent paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. Here, we present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Proposal from the NA61/SHINE Collaboration for update of European Strategy for Particle Physics

Building on the current program's success and driven by new physics challenges, the NA61/SHINE Collaboration proposes to continue measuring hadron production properties in reactions induced by hadron and ion beams after CERN Long Shutdown 3. These measurements are of significant interest to the heavy-ion, cosmic-ray, and neutrino physics communities and will focus on: - Investigating hadron production in the light-ion systems to explore the diagram of high-energy nuclear collisions, and to obtain new insight into the unexpected violation of isospin (flavor) symmetry recently observed by the experiment; - Measuring charm-anticharm correlations to gain unique insights into the production locality of charm and anticharm quark pairs; - Examining strangeness and multi-strangeness production to improve our understanding of the early Universe's evolution and neutron star formation; - Measuring cross sections relevant for cosmic-ray measurements, significantly boosting searches for new physics in our Galaxy; - Conducting hadron production measurements with proton, pion, and kaon beams for neutrino physics, enhancing the precision of hadron production data needed for initial neutrino flux predictions in neutrino oscillation experiments; - Measuring hadron production processes relevant for understanding the flux of atmospheric neutrinos, as well as neutrinos and muons from spallation sources. To achieve these objectives, a detector upgrade and a beam upgrade are required, with data-taking planned for the period 2029-2032 and beyond.

Adhikary, H. [Jan Kochanowski U.] (ORCID:000000025↗
DOE OSTI2019

Separating physical impacts from natural variability using piggybacking technique

Abstract. In a chaotic system, like moist convection, it is difficult to separate the impact of a physical process from effects of natural variability. This is because modifying even a small element of the system physics typically leads to a different system evolution and it is difficult to tell whether the difference comes from the physical impact or it merely represents a different flow realization. This paper discusses a relatively simple and computationally efficient modelling methodology that allows separation of the two. The methodology is referred to as the piggybacking approach. The idea is to use two sets of thermodynamic variables (the temperature, water vapor, and all aerosol, cloud, and precipitation variables) in a single cloud simulation. The two sets differ in a specific element of the physics, such as aerosol properties, microphysics parameterization, large-scale forcing, environmental profiles, etc. One thermodynamic set is coupled to the dynamics and drives the simulated flow, and the other set piggybacks the flow, that is, thermodynamic variables are carried by the flow but they do not affect it. By switching the two sets (i.e. the set driving the simulation becomes the piggybacking one, and vice versa), the impact on the cloud dynamics can be evaluated. This paper provides details of the method and reviews results of its application to such problems as the postulated deep convection invigoration in polluted environments, the impact of changes in environmental profiles (e.g., due to climate change) on convective dynamics, and the role of cloud-layer heterogeneities for shallow convective cloud field evolution. Prospects for applying piggybacking technique to other areas of atmospheric simulation (e.g., weather prediction or geoengineering) are also mentioned.

Grabowski, Wojciech W.↗
NASA NTRS1969

Methods of Achieving and Maintaining Physical Fitness for Prolonged Space Flight

This final summary report covers the five experiments that were conducted over a 24-month period beginning May 1, 1967 and ending April 30, 1969. Experiment I revealed that running and riding a bicycle ergometer produced similar gains in physical fitness variables. In Experiment I the subjects exercising at a 180 heart rate made a greater improvement in physical fitness than did those exercising a t a 140 or 160 heart rate. In Experiment II the subjects who exercised sixty minutes per day made greater gains on specified components of physical fitness than did those who exercised twenty or forty minutes per day, twelve times per week made greater gains on specified components of physical fitness than did those who exercised three or six times per week. In Experiment V, it was found that subjects could maintain a moderate level of fitness by exercising at a pulse rate of 160 beats per minute for twenty-minute periods three times per week, that subjects who "overtrained" by exercising twice daily to near exhaustion increased in fitness and that those subjects who discontinued training decreased in fitness.

Olree, Harry D.↗
NASA NTRS1972

Physics in perspective, volume 1

Progress in physics and its subfields is presented and the contribution of physics is related to progress in other scientific disciplines and the manner in which these have been utilized by our civilization. The future course of endeavor in the various subfields of physics and the extent to which these will require resources of manpower, facilities, and funding are discussed. The rational responses of the total research effort in physics to alternative funding levels are assayed.

Source record↗
NASA NTRS1972

Zero-gravity cloud physics.

The first results of an ongoing preliminary-concept and detailed-feasibility study of a zero-gravity earth-orbital cloud physics research facility are reviewed. Current planning and thinking are being shaped by two major conclusions of this study: (1) there is a strong requirement for and it is feasible to achieve important and significant research in a zero-gravity cloud physics facility; and (2) some very important experiments can be accomplished with 'off-the-shelf' type hardware by astronauts who have no cloud-physics background; the most complicated experiments may require sophisticated observation and motion subsystems and the astronaut may need graduate level cloud physics training; there is a large number of experiments whose complexity varies between these two extremes.

Hollinden, A. B.↗
NASA NTRS1976

Environmental parameters of the Tennessee River in Alabama. 2: Physical, chemical, and biological parameters

Physical, chemical and biological water quality data from five sites in the Tennessee River, two in Guntersville Reservoir and three in Wheeler Reservoir were correlated with climatological data for three annual cycles. Two of the annual cycles are for the years prior to the Browns Ferry Nuclear Power Plant operations and one is for the first 14 months of Plant operations. A comparison of the results of the annual cycles indicates that two distinct physical conditions in the reservoirs occur, one during the warm months when the reservoirs are at capacity and one during the colder winter months when the reservoirs have been drawn-down for water storage during the rainy months and for weed control. The wide variations of physical and chemical parameters to which the biological organisms are subjected on an annual basis control the biological organisms and their population levels. A comparison of the parameters of the site below the Power plant indicates that the heated effluent from the plant operating with two of the three reactors has not had any effect on the organisms at this site. Recommendations given include the development of prediction mathematical models (statistical analysis) for the physical and chemical parameters under specific climatological conditions which affect biological organisms. Tabulated data of chemical analysis of water and organism populations studied is given.

Rosing, L. M.↗
NASA NTRS1978

Report on monitoring and support instruments for solar physics research from Spacelab

The Quick Reaction and Special Purpose Facility Definition Team for Solar Physics Spacelab Payloads examined a variety of instruments to fulfill the following functions: (1) solar physics research appropriate to Spacelab, (2) correlative data for research in such fields as aeronomy, magnetospheric physics, ionospheric physics, meteorology and climatology, (3) target selection for activity alert monitoring and (4) pointing accuracy monitoring of Spacelab platforms. In this examination the team accepted a number of restrictions and qualifications: (1) the cost of such instruments must be low, so as not to adversely impact the development of new, research class instrumentation in the early Spacelab era; (2) the instruments should be of such a size that they each would occupy a small fraction of a pointing system, and (3) the weight and power consumption of the instruments should also be small. With these restrictions, the instruments chosen are: the visible light telescope and magnetograph, the extreme-ultraviolet telescope, and the solar irradiance monitor.

Source record↗
NASA NTRS1982

The effect of physical aging on the time-dependent properties of carbon-fiber-reinforced epoxy composites

Annealing in nitrogen below the glass transition temperature T(g) is found to affect the matrix-dominated physical properties of postcured, (+ and -45)2s specimens of Thornel 300 carbon fiber/Fiberite 934 epoxy composite as well as neat Fiberite 934 epoxy resin. The damping behavior, stress-relaxation rate and enthalpic state of the matrix material are found to decrease as functions of annealing time. The time-dependent change in physical properties is explained on the basis of free-volume collapse related to the physical aging of the nonequilibrium glassy network of epoxy. Such physical aging in network epoxy has been demonstrated to be thermoreversible through the monitoring of its kinetics by differential scanning calorimetry, stress relaxation tests, and dynamic mechanical analysis.

Kong, E. S. W.↗
NASA NTRS1985

The future of solar physics

Outstanding problems for the future of solar physics and stellar physics are examined. The physics of stellar interiors has been called into serious question by the very low measured neutrino flux from the sun. The Ga-71 neutrino detection experiment is the next step in unravelling this mystery. The new methods of helioseismology, for probing the interior of the sun, have already found the primordial rapid rotation of the central core. The forthcoming worldwide helioseismology observing network will permit fuller exploitation of the method, promising to provide the first direct sounding of the interior of a star, hitherto known to us only through theoretical inference and the discrepant neutrino emission. An essential step in developing the physics of stellar activity will be the Solar Optical Telescope (presently planned by NASA to be launched early in the next decade) to permit a 'microscopic' examination of the surface of the sun to study the source of the action. The activity and X-ray emission of other stars depend on much the same effects, so that the study of the sun is essential to determining the significance of the X-ray emission from other stars.

Parker, E. N.↗