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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Impact of GW170817 for the nuclear physics of the EOS and the r-process

The recent LIGO/Virgo detection of gravitational radiation from GW170817 bears the clear signature of a binary neutron star merger and has validated a number of theoretical predictions. High-energy radiation in the form of a short gamma-ray burst was observed 1.7 s following the merger, and an extended optical/infrared afterglow lasting weeks was observed by dozens of observatories. Short gamma-ray bursts had been thought to be produced by mergers, and the afterglow is the predicted signal of radioactive decays from decompressing neutron-rich matter catastrophically ejected from the merging stars. The afterglow provided solid evidence that neutron star mergers are a major, if not primary, source of r-process nuclei, that half of all nuclei heavier than iron formed from the rapid capture of neutrons. Although this idea was proposed nearly 45 years ago, it was largely ignored in favor of a supernova mechanism. Over the last decade, however, evidence has been accumulating that supernovae are not the primary r-process source. GW170817 may have finally settled this question, which has been one of the thorniest problems in nuclear physics and astrophysics. This article presents my personal perspective of this paradigm shift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

An assessment of coupling algorithms for nuclear reactor core physics simulations

This paper evaluates the performance of multiphysics coupling algorithms applied to a light water nuclear reactor core simulation. The simulation couples the k-eigenvalue form of the neutron transport equation with heat conduction and subchannel flow equations. We compare Picard iteration (block Gauss–Seidel) to Anderson acceleration and multiple variants of preconditioned Jacobian-free Newton–Krylov (JFNK). The performance of the methods are evaluated over a range of energy group structures and core power levels. A novel physics-based approximation to a Jacobian-vector product has been developed to mitigate the impact of expensive on-line cross section processing steps. Numerical simulations demonstrating the efficiency of JFNK and Anderson acceleration relative to standard Picard iteration are performed on a 3D model of a nuclear fuel assembly. Both criticality (k-eigenvalue) and critical boron search problems are considered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Foreword to special issue: Papers from the 57th Annual Meeting of the APS Division of Plasma Physics, November 16–20, 2015, Savannah, Georgia, USA

The 57th annual meeting of the APS Division of Plasma Physics (DPP) was held November 16–20, 2015 in Savannah, Georgia. The meeting brings together researchers (undergraduate students through retirees) from all areas of plasma physics. 1887 abstracts were included in the program, approximately 200 more than the previous year. The presentations included five invited review talks, 97 invited talks, three invited postdeadline talks, and four tutorials. Furthermore, there were approximately 1780 contributed presentations, with about 40% oral and 60% poster. Three mini-conferences were held concurrently.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Learning compact physics‐aware delayed photocurrent models using dynamic mode decomposition

Abstract Radiation‐induced photocurrent in semiconductor devices can be simulated using complex physics‐based models, which are accurate, but computationally expensive. This presents a challenge for implementing device characteristics in high‐level circuit simulations where it is computationally infeasible to evaluate detailed models for multiple individual circuit elements. In this work we demonstrate a procedure for learning compact delayed photocurrent models that are efficient enough to implement in large‐scale circuit simulations, but remain faithful to the underlying physics. Our approach utilizes dynamic mode decomposition (DMD), a system identification technique for learning reduced‐order discrete‐time dynamical systems from time series data based on singular value decomposition. To obtain physics‐aware device models, we simulate the excess carrier density induced by radiation pulses by solving numerically the ambipolar diffusion equation, then use the simulated internal state as training data for the DMD algorithm. Our results show that the significantly reduced‐order delayed photocurrent models obtained via this method accurately approximate the dynamics of the internal excess carrier density—which can be used to calculate the induced current at the device boundaries—while remaining compact enough to incorporate into larger circuit simulations.

Hanson, Joshua↗
DOE OSTI2019

Insights into the physical and chemical properties of a cement-polymer composite developed for geothermal wellbore applications

To isolate injection and production zones from overlying formations and aquifers during geothermal operations, cement is placed in the annulus between well casing and formations. Wellbore cement can fracture due to chemical and physical stress. A polymer-cement (composite) with self-healing properties, stable at high temperatures, was recently developed by our group. Here we present data on the elemental distribution, mineral composition, internal microstructure, and tensile elasticity of this composite and how it compares to cement without polymer (control cement). Entrapped air porosity was similar between control cement (0.44% by volume) and composite (0.22% by volume). Total voids increased with polymer content in the cement (10% and 25% composite), and polymer was found to fill most of these voids. Polymer appears to act as a (cement hydration) retarder increasing the amount of unhydrated silicate minerals in the matrix. Tensile elasticity was improved for composites as compared to control cement, suggesting that the composite materials perform better under mechanical stress. Density-functional calculations reproduce the drop in Young’s modulus by about 60% observed experimentally when polymer is added to the cement. In addition, calculations indicate that the bonding interactions between the cement and polymer remain stable over the range of strain studied.

Rod, Kenton A. (ORCID:0000000318189094)↗
DOE OSTI2025

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be nonconservative and nonreciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here, we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, nonidentical particles, and learns the effective nonreciprocal forces between particles with exquisite accuracy (R 2 > 0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model’s accuracy enables precise measurements of particle charge and screening length, identifying large deviations from common theoretical assumptions. Our ability to identify unknown physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.

Science & Technology - Other Topics↗
DOE OSTI2022

Physics Informed Neural Networks as Computational Physics Emulators

This report is a brief overview and evaluation of Physics Informed Neural Networks (PINNs). Karniadakis and co-workers, e.g., Karniadakis et al. (2021) assert that the PINNs approach integrates seamlessly both data and mathematical physics models, even in partially understood, uncertain and high-dimensional contexts. They further claim that PINNs are effective and efficient for ill-posed and inverse problems, and when combined with domain decomposition, are scalable to large problems and a tool to discover hidden physics. While they demonstrate the capabilities in specific academic instances, their overarching claims about PINNs seem to be an overstatement, at least at the current time. We have briefly considered a few of the limitations of PINNs in this investigation. It is not clear to us if the PINNs approach can ever be competitive with approaches that use specialized algorithms to achieve high-accuracy solutions of governing equations and other techniques that can combine observational data with such solutions. As an example of the latter, consistent with the principles of Bayesian inference, data assimilation, or more generally data-model fusion, is a process that fuses observational data typically with a computational model that respects certain constraints such as conservation laws. For example, improvements in observational network combined with data assimilation have been key in improving weather predictions over the past four decades Kalnay (2003).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

MRT 7365 Power flow physics and key physics phenomena: EMPIRE verification suite

This milestone work baselines electromagnetic particle-in-cell capability of the EMPIRE plasma simulation code to model key processes germane to the physics of electrode plasmas arising in magnetically-insulated transmission lines operating at or near 20 MA. This evaluation is done so through the provision of benchmark verification problems designed to exercise the individual and combined physics models on a small-scale surrogate geometry for the final-feed-to-load region of the Z accelerator under representative operating conditions. In this report, we overview our test designs, and present a portfolio of simulation results along with performance assessments which altogether establish state-of-the-art. In particular, two main verification categories are covered this report: (1) Z-relevant desorption physics (Temkin isotherm), and (2) two approaches to simulate electrode plasma creation and dynamics (automatic creation versus self-consistent creation through direct simulation Monte Carlo collisions).

43 PARTICLE ACCELERATORS↗
NASA NTRS1986

Physics through the 1990s: Elementary-particle physics

The volume begins with a non-mathematical discussion of the motivation behind, and basic ideas of, elementary-particle physics theory and experiment. The progress over the past two decades with the quark model and unification of the electromagnetic and weak interactions is reviewed. Existing theoretical problems in the field, such as the origin of mass and the unification of the fundamental forces, are detailed, along with experimental programs to test the new theories. Accelerators, instrumentation, and detectors are described for both current and future facilities. Interactions with other areas of both theoretical and applied physics are presented. The sociology of the field is examined regarding the education of graduate students, the organization necessary in large-scale experiments, and the decision-making process involved in high-cost experiments. Finally, conclusions and recommendations for maintaining US excellence in theory and experiment are given. Appendices list both current and planned accelerators, and present statistical data on the US elementary-particle physics program. A glossary is included.

Source record↗
DOE OSTI2022

How to Read the Snowmass White Papers on Power Dynamics in Physics, Informal Socialization in Physics Training, and Policing and Gatekeeping in STEM

The Community Engagement Frontier presents this set of three white papers, as part of Snowmass 2021. These papers address critical issues -- Power Dynamics in Physics, Informal Socialization in Physics Training, and Policing and Gatekeeping in STEM -- that make significant impacts on the experiences of the people who work in and learn particle physics. In this introductory document, we present crosscutting concepts that appear in each paper, and some advice on how to manage readers' responses to the contents. We expect that you will learn something new here. We hope that whatever you encounter, you will be energized to increase justice in this discipline we all love.

Hodari, Apriel K.↗
DOE OSTI2017

Hierarchical calibration and validation for modeling bench-scale solvent-based carbon capture. Part 1: Non-reactive physical mass transfer across the wetted wall column

A hierarchical model calibration and validation are proposed here for quantifying the confidence level of mass transfer prediction using a computational fluid dynamics (CFD) model, where the solvent-based carbon dioxide (CO 2 ) capture is simulated and the predicted results are compared to the corresponding bench-scale experimental data. Two unit problems with an increasing level of complexity are proposed to break down the complex physical/chemical processes of solvent-based CO 2 capture into relatively simpler problems to separate the effects of physical transport and chemical reaction. This paper focuses on the calibration and validation of the first unit problem, i.e., the CO 2 mass transfer across a falling ethanolamine (MEA) film in the absence of chemical reaction. This problem is investigated both experimentally and numerically using non-reactive nitrous oxide (N 2 O) as a surrogate for CO 2 . To capture motion of the gas-liquid interface, a volume of fluid method is employed with a one-fluid formulation to compute the mass transfer between the two phases. Parallel bench-scale experiments are designed and conducted to validate and calibrate the CFD models using a general Bayesian calibration approach. Two important transport parameters, Henry's constant and gas diffusivity, are calibrated to produce the posterior distributions, which will be used as input for the second unit problem to address the chemical absorption of CO 2 across the MEA falling film, where both mass transfer and chemical reaction are involved. Finally, mass transfer coefficients predicted by CFD are also compared with those predicted by the traditional/empirical correlations under both steady and wavy falling film conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The physics potential of a reactor neutrino experiment with Skipper-CCDs: searching for new physics with light mediators

In this work, we explore the sensitivity to new physics of the recently proposed vIOLETA experiment: a 10 kg Skipper Charged Coupled Device detector deployed 12 meters away from a commercial nuclear reactor core. We investigate two broad classes of models which benefit from the very low energy recoil threshold of these detectors, namely neutrino magnetic moments and light mediators coupled to neutrinos and quarks or electrons. We find that this experimental setup is very sensitive to light, weakly coupled new physics, and in particular that it could probe potential explanations of the event excess observed in XENON1T. We also provide a detailed study on the dependence of the sensitivity on the experimental setup assumptions and on the neutrino flux systematic uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Physics for neuromorphic computing

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. Here, we discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.

device physics↗
DOE OSTI2014

Algebraic vs physical N = 6 3-algebras

In our previous paper, we classified linearly compact algebraic simple N = 6 3-algebras. In the present paper, we classify their “physical” counterparts, which actually appear in the N = 6 supersymmetric 3-dimensional Chern-Simons theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Kinetic electron and ion instability of the lunar wake simulated at physical mass ratio

The solar wind wake behind the moon is studied with 1D electrostatic particle-in-cell (PIC) simulations using a physical ion to electron mass ratio (unlike prior investigations); the simulations also apply more generally to supersonic flow of dense magnetized plasma past non-magnetic objects. In this work, a hybrid electrostatic Boltzmann electron treatment is first used to investigate the ion stability in the absence of kinetic electron effects, showing that the ions are two-stream unstable for downstream wake distances (in lunar radii) greater than about three times the solar wind Mach number. Simulations with PIC electrons are then used to show that kinetic electron effects can lead to disruption of the ion beams at least three times closer to the moon than in the hybrid simulations. This disruption occurs as the result of a novel wake phenomenon: the non-linear growth of electron holes spawned from a narrow dimple in the electron velocity distribution. Most of the holes arising from the dimple are small and quickly leave the wake, approximately following the unperturbed electron phase-space trajectories, but some holes originating near the center of the wake remain and grow large enough to trigger disruption of the ion beams. Lastly, non-linear kinetic-electron effects are therefore essential to a comprehensive understanding of the 1D electrostatic stability of such wakes, and possible observational signatures in ARTEMIS data from the lunar wake are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Doping effect on the physical properties of Ca 10 Pt 3 As 8 (Fe 2 As 2 ) 5 single crystals

Ca 10 Pt 3 As 8 (Fe 2 As 2 ) 5 is a unique parent compound for superconductivity, which consists of both semiconducting Pt 3 As 8 and metallic FeAs layers. We report the observation of superconductivity induced via chemical doping in either Ca site using rare-earth (RE) elements (RE = La, Gd) or Fe site using Pt. The interlayer distance and the normal-state physical properties of the doped system change correspondingly. The coupled changes include (1) superconducting transition temperature T c increases with increasing both doping concentration and interlayer distance, (2) our T c value is higher than previously reported maximum value for Pt doping in the Fe site, (3) both the normal-state in-plane resistivity and out-of-plane resistivity change from non-metallic to metallic behavior with increasing doping concentration and T c , and (4) the transverse in-plane magnetoresistance (MR ab ) changes from linear-field dependence to quadratic behavior upon increasing T c . For La-doped compound with the highest T c (~35 K), upper critical fields ($H_{c2}^{ab} , H_{c2}^c$), coherence lengths (ξ ab , ξ c ), and in-plane penetration depth (λ ab ) are estimated. Finally, we discuss the relationship between chemical doping, interlayer distance, and physical properties in this system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗