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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Lagrangian large eddy simulations via physics-informed machine learning

High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. In this work, we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Mário Schenberg: Physicist, politician and art critic

Mário Schenberg is considered one of the greatest theoretical physicists of Brazil. He worked in different fields of physics including thermodynamics, quantum mechanics, statistical mechanics, general relativity, astrophysics and mathematics. He was assistant of the Ukrainian naturalized Italian physicist Gleb Wataghin and worked with prestigious physicists like as the Brazilians José Leite Lopes and César Lattes, the Russian-born American George Gamow and the Indian astrophysicist Subrahmanyan Chandrasekhar. Besides, he was also an active politician and critic of art.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator

This study develops a nonparametric multivariate drought index, namely, the nonparametric multivariate standardized drought index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing nonparametric multivariate drought index, the nonparametric kernel estimator is used to derive the joint distribution of precipitation and streamflow, thus providing additional insights into drought-index development. The proposed NMSDI is applied in the Wei River basin (WRB), on the basis of which the drought-evolution characteristics are investigated. Three main results were found: 1) In general, NMSDI captures drought onset in a way that is similar to that of the standardized precipitation index and captures drought termination and persistence in a way that is similar to that of the standardized streamflow index. We report the drought events identified by NMSDI match well with historical drought records in the WRB. Performance is also consistent with that of an existing multivariate standardized drought index at various time scales, confirming the validity of the newly constructed NMSDI in drought detections. 2) An increasing risk of drought has been detected for past decades and will persist to a certain extent in the future in most areas of the WRB. 3) The identified changepoints of annual NMSDI are mainly concentrated in the early 1970s and mid-1990s, coincident with extensive water use and soil conservation practices. In summary, this study highlights a nonparametric multivariable drought index that can efficiently and comprehensively be used for drought detections and predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity

This paper clarifies the causes of intermodel differences in the global-average temperature response to doubled CO 2 , commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate and longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Finally, defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparison of point cloud and image-based models for calorimeter fast simulation

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Furthermore, point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

47 OTHER INSTRUMENTATION↗
NASA NTRS1988

Results of a joint NOAA/NASA sounder simulation study

This paper presents the results of a joint NOAA and NASA sounder simulation study in which the accuracies of atmospheric temperature profiles and surface skin temperature measuremnents retrieved from two sounders were compared: (1) the currently used IR temperature sounder HIRS2 (High-resolution Infrared Radiation Sounder 2); and (2) the recently proposed high-spectral-resolution IR sounder AMTS (Advanced Moisture and Temperature Sounder). Simulations were conducted for both clear and partial cloud conditions. Data were analyzed at NASA using a physical inversion technique and at NOAA using a statistical technique. Results show significant improvement of AMTS compared to HIRS2 for both clear and cloudy conditions. The improvements are indicated by both methods of data analysis, but the physical retrievals outperform the statistical retrievals.

Phillips, N.↗
DOE OSTI2014

LONG-TERM X-RAY STABILITY AND ULTRAVIOLET VARIABILITY OF THE IONIZED ABSORPTION IN NGC 3783

We present the results of recent Chandra High-Energy Transmission Grating Spectrometer and Hubble Space Telescope Cosmic Origins Spectrograph observations of the nearby Seyfert 1 galaxy NGC 3783, which show a strong, nonvarying X-ray warm absorber and physically related and kinematically varying UV absorption. We compare our new observations to high-resolution, high signal-to-noise archival data from 2001, allowing a unique investigation into the long-term variations of the absorption over a 12 yr period. We find no statistically significant changes in the physical properties of the X-ray absorber, but there is a significant drop of ∼40% in the UV and X-ray flux and a significant flattening of the underlying X-ray power-law slope. Large kinematic changes are seen in the UV absorbers, possibly due to radial deceleration of the material. Similar behavior is not observed in the X-ray data, likely due to its lower-velocity resolution, which shows an outflow velocity of v {sub out} ∼ –655 km s{sup –1} in both epochs. The narrow iron Kα emission line at 6.4 keV shows no variation between epochs, and its measured width places the material producing the line at a radial distance of ∼0.03 pc from the central black hole.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2022

Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to modeling uncertainty

Here, this work and its companion paper, Amon et al. [Phys. Rev. D 105, 023514 (2022)], present cosmic shear measurements and cosmological constraints from over 100 million source galaxies in the Dark Energy Survey (DES) Year 3 data. We constrain the lensing amplitude parameter 𝑆 8 ≡𝜎 8 ⁢$\sqrt{Ω_{m}/0.3}$ at the 3% level in Λ⁢ CDM: 𝑆 8 =0.75⁢9$^{+0.025}_{−0.023}$ (68% CL). Our constraint is at the 2% level when using angular scale cuts that are optimized for the Λ⁢ CDM analysis: 𝑆 8 =0.77⁢2$^{+0.018}_{−0.017}$ (68% CL). With cosmic shear alone, we find no statistically significant constraint on the dark energy equation-of-state parameter at our present statistical power. We carry out our analysis blind, and compare our measurement with constraints from two other contemporary weak lensing experiments: the Kilo-Degree Survey (KiDS) and Hyper-Suprime Camera Subaru Strategic Program (HSC). We additionally quantify the agreement between our data and external constraints from the Cosmic Microwave Background (CMB). Our DES Y3 result under the assumption of Λ⁢ CDM is found to be in statistical agreement with Planck 2018, although favors a lower 𝑆 8 than the CMB-inferred value by 2.3⁢𝜎 (a 𝑝-value of 0.02). This paper explores the robustness of these cosmic shear results to modeling of intrinsic alignments, the matter power spectrum and baryonic physics. We additionally explore the statistical preference of our data for intrinsic alignment models of different complexity. The fiducial cosmic shear model is tested using synthetic data, and we report no biases greater than 0.3⁢𝜎 in the plane of 𝑆 8 ×Ω m caused by uncertainties in the theoretical models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Code for the manuscript "Lagrangian Attention Tensor Networks for Velocity Gradient Statistical Mode

We disclose a python/pytorch implementation of the physics-informed machine learning algorithm described in "Lagrangian Attention Tensor Networks for Velocity Gradient Statistical Modeling", LA-UR-24-30678. Direct numerical simulation (DNS) of ubiquitous turbulence phenomena is computationally infeasible for realistic flows. As a result, reduced modeling for turbulent flows aim to reduce the number of resolved scales while retaining accurate representations of the small-scale physics. The dynamics of the velocity gradient tensor (VGT) is a key ingredient in reduced or subgrid turbulence models. The evolution equation for the VGT involves nonlocal terms, requiring closure modeling. This implementation of the novel methodology of Lagrangian Attention Tensor Networks (LATN), utilizes a structured representation of the history of the VGT to inform a physics-informed machine learning algorithm. This addition of structured memory terms is shown to outperform previous models when trained and evaluated on DNS data.

Livescu, Daniel [LANL]↗
DOE OSTI2021

Turbulence theories and statistical closure approaches

When discussing research in physics and in science more generally, it is common to ascribe equal importance to the three components of the scientific trinity: theoretical, experimental, and computational studies. This review will explore the future of modern turbulence theory by tracing its history, which began in earnest with Kolmogorov’s 1941 analysis of turbulence cascade and inertial range [A.N. Kolmogorov, Dokl. Akad. Nauk SSSR, 30, 299, (1941); 32, 19, (1941)]. The 80th Anniversary of Kolmogorov’s landmark study is a welcome opportunity to survey the achievements and evaluate the future of the theoretical approach of turbulence research. Over the years, turbulence theories have been critically important in laying the foundation of our understanding of the nature of turbulent flows. In particular, the Direct Interaction Approximation (DIA) [R.H. Kraichnan, J. Fluid Mech., 5, 497 (1959)] and its subsequent development, known as the statistical closure approach, can be identified as perhaps the most profound single advancement. The remarkable success of the statistical closure has furnished a platform to study such essential concepts as the energy transfer process and interacting scales, and the roles of the straining and sweeping motions. More recently, the quasi-Lagrangian formulation of V. L’vov & I. Procaccia and Kraichnan’s solvable passive scalar model provided powerful ways to explore another fundamental aspect of turbulent flows, the phenomena of intermittency, and the associated anomalous scaling exponents. In the meantime, the theory of fluid equilibria has been developed to describe the large-scale structures that can emerge from turbulent cascades of two-dimensional and geophysical flows at a later time. And yet, despite all these successes, analytical treatments suffer from mathematical complexities. As a result, the utility of theoretical approaches has been limited to relatively idealized flows. On the other hand, in recent decades, computational abilities and experimental facilities have reached an unprecedented scale. Looking beyond the horizon, the imminent deployment of exascale supercomputers will generate complete datasets of the entire flow field of key benchmark flows, allowing researchers to extract additional measurements concerning fully developed, complex turbulent flow fields far beyond those available from the statistical closure theories. Some other developments that could potentially influence the future course of turbulence theories include the advancement of machine learning, artificial intelligence, and data science; likely disruptions arising from the advent of quantum computation; and the increasingly prominent role of turbulence research in providing more accurate climate scientific data. Finally, turbulence theorists can leverage these developments by asking the right questions and developing advanced, sophisticated frameworks that will be able to predict and correlate vast amounts of data from the other two components of the trinity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Research in Theoretical High Energy Nuclear Physics at the University of Arizona

In the past decade (2004-2015) we addressed the quest for the understanding of how quark confinement works, how it can be dissolved in a limited space-time domain, and what this means: i) for the paradigm of the laws of physics of present day; and, ii) for our understanding of cosmology. The focus of our in laboratory matter formation work has been centered on the understanding of the less frequently produced hadronic particles (e.g. strange antibaryons, charmed and beauty hadrons, massive resonances, charmonium, B c ). We have developed a public analysis tool, SHARE (Statistical HAdronization with REsonances) which allows a precise model description of experimental particle yield and fluctuation data. We have developed a charm recombination model to allow for off-equilibrium rate of charmonium production. We have developed methods and techniques which allowed us to study the hadron resonance yield evolution by kinetic theory. We explored entropy, strangeness and charm as signature of QGP addressing the wide range of reaction energy for AGS, SPS, RHIC and LHC energy range. In analysis of experimental data, we obtained both statistical parameters as well as physical properties of the hadron source. The following pages present listings of our primary writing on these questions. The abstracts are included in lieu of more detailed discussion of our research accomplishments in each of the publications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Extension of the eigenstate thermalization hypothesis to nonequilibrium steady states

We extend the notion of the eigenstate thermalization hypothesis (ETH) to open quantum systems governed by the Gorini-Kossakowski-Lindblad-Sudarshan (GKLS) master equation. We present evidence that the eigenstates of nonequilibrium steady-state (NESS) density matrices obey a generalization of ETH in boundary-driven systems when the bulk Hamiltonian is nonintegrable, just as eigenstates of Gibbs density matrices are conjectured to do in equilibrium. Furthermore, this generalized ETH, which we call NESS ETH, can be used to obtain representative pure states that reproduce the expectation values of few-body operators in the NESS. The density matrices of these representative pure states can be further interpreted as weak solutions of the GKLS master equation. Additionally, we explore the validity and breakdown of NESS-ETH in the presence of symmetries, integrability, and many-body localization in the bulk Hamiltonian.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics for the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Measuring the Muon Anomalous Magnetic Moment to High Precision [PowerPoint]

The anomalous magnetic moment of the muon, characterized as the anomaly $a_{\mu} \equiv (g_{\mu}-2)/2$, remains a compelling hint of physics beyond the Standard Model (BSM) in light of the Brookhaven E821 measurement from the early 2000s, which differs with theoretical predictions by $>$3\,$\sigma$. To address this discrepancy, the Muon $g-2$ Experiment at Fermilab (E989) has been designed to determine $a_{\mu}$ to a precision of 140 parts per billion, a four-fold improvement over the E821 measurement. This improved precision presents an excellent opportunity to uncover BSM physics, and at minimum constrain BSM prescriptions. To date, the E989 experiment has completed its first two physics runs, recording more than 3 times the statistics recorded in the E821 experiment. The collaboration is preparing for a third physics run starting in October, with the goal of accumulating a factor of three more data than the previous runs combined, resulting in an order of magnitude more statistics than the Brookhaven measurement. An overview of the experimental technique will be discussed, along with a summary of Run 1 and 2. An update of the analysis of the Run 1 data will be presented; additionally, Run 3 progress will be summarized.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗