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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Physics Flash December 2016

This is the December 2016 issue of Physics Flash, the newsletter of the Physics Division of Los Alamos National Laboratory (LANL). In this issue, the following topics are covered: Novel liquid helium technique to aid highly sensitive search for a neutron electrical dipole moment; Silverleaf: Prototype Red Sage experiments performed at Q-site; John L. Kline named 2016 APS Fellow; Physics students in the news; First Entropy Engine quantum random number generator hits the market; and celebrating service.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

High Energy Physics Research: Experimental & Theoretical (Final Report)

The research at the energy frontier was to explore high energy and luminosity collisions at the LHC using the ATLAS and CMS detectors to lead physics analyses in characterization of the Higgs Boson, to search for its potential partners, to lead searches for Dark Matter and to make extensive studies of Electroweak phenomena. The objectives are also to operate and upgrade the ATLAS and CMS detectors and their computing to optimize their physics performance in the present data taking and to enable the full exploitation of planned High Luminosity LHC. The research for the theory frontier was to analyze and interpret the new data from the experimental frontiers: energy, cosmic and intensity (Phenomenology), to investigate new theories of high energy physics extending the standard model by including gravity within consistent frameworks such as string theory, and examine their phenomenological and cosmological implications (String Theory and Theoretical Cosmology.)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Constraining Physical Models at Gigabar Pressures

High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative information. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA Laser System using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a 1-D hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. The goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Neutrino Cross Sections II: Neutrino Boogaloo. UF Task P3, Accelerator-Based Experimental Neutrino Physics. Final Report

We report on progress in a series of different directions within high energy physics research. 1. Neutrino research in hardware and software on the Minerva and MiniBooNE experiments 2. Experimental particle physics at the hadron colliders, with emphasis on research and development and data analysis on the CMS experiment operating at the CERN LHC. This includes research on the discovery and properties on the Higgs Boson. 3. Educational outreach through the Quarknet program, taking physics research into High School classrooms. 4. Theoretical and Phenomenological High Energy research, covering a broad range of activities ranging from fundamental theoretical issues to areas of immediate phenomenological importance. 5. Experiment searches for the Axion, as part of the ADMX experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Improved Few-group Homogenized Cross-section Sensitivity Analysis for Lattice Physics Calculation

In the conventional reactor physics 'two-step' calculation scheme, lattice-physics calculation is followed by core calculation using neutron diffusion theory. The few-group spatially homogenized cross-sections are the outputs of the lattice-physics calculation as well as the inputs of the core calculation. The uncertainties of fundamental nuclear data will be propagated to the core calculation results through few-group spatially homogenized cross-sections. Consequently, assessing the sensitivity and uncertainty of few-group homogenized cross-sections is necessary for the core-calculation sensitivity and uncertainty analysis. Conventional method for sensitivity analysis is usually performed with respect to effective self-shielding cross sections but not continuous-energy cross sections by neglecting resonance self-shielding effects. The purpose of this work is to improve the sensitivity analysis of few-group cross sections and assess the effects introduced by resonance calculation with the subgroup method being used for resonance calculation. This paper is organized as follows: Firstly, theoretical models of this work are described. Secondly, validation and analysis are performed based on a boiling-water reactor (BWR) fuel-pin cell under hot zero power (HZP) condition. Finally, summaries and conclusions are given. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Overview of the MAST Upgrade physics programme: testing novel concepts at low aspect ratio to inform future devices

The research programme performed on the Mega Amp Spherical Tokamak (MAST) Upgrade experiment has made significant advances in developing the physics understanding of low aspect ratio tokamaks in support of the operation of ITER and design of fusion powerplants. High performance plasma scenarios have been developed to facilitate a broad programme of experiments, in which confinement is constrained by the presence of m/n = 2/1 modes that cause substantial losses of fast ions. The onset of these modes coincides with the q = 2 surface residing in a local minimum in the toroidal current density profile. The maximum electron temperature at the pedestal top, T e,ped is limited with gas fuelling to ∼350 eV to maintain regular ELMs; higher T e,ped results in a transition to a non-stationary ELM-free regime. The operational space of spherical tokamaks has been expanded into small and ELM-free regimes. Strong shaping of the last closed flux surface can induce a transition from large to small ELMs, and ELM suppression with resonant magnetic perturbations has been observed for the first time in a low aspect ratio tokamak. Negative triangularity shaping has induced a transition from ELMy H-mode to a high-performance L-mode regime for the first time in a low aspect ratio tokamak. In studies of fast ion confinement, losses of fast particles due to Global Alfvén Eigenmodes have been identified. Interactions between fast ions generated by off-axis neutral beam injection and thermal neutrals can result in significant losses of fast ions. Experiments with on- and off-axis neutral beam injection exhibit a flux pumping mechanism, where the central safety factor is held to ∼1 in the absence of sawteeth. In studies of pedestal physics, it has been found that elevated main chamber neutral pressures result in an increase in the electron density and reduction in the temperature at the pedestal top. Advances in understanding plasma exhaust include the integration of a high-performance plasma core with detached outer divertors in the X-point target configuration. A newly commissioned lower divertor cryopump reduces the lower divertor neutral pressure by up to 50%, with minimal effect on the main chamber or upper divertor. New measurements and SOLPS-ITER simulations emphasise the importance of plasma–neutral interactions on divertor detachment in the conditions accessible in experiments. Real-time control of the ionisation front location in both divertor chambers independently has been demonstrated in double null experiments, enabled by the tightly baffled divertor chambers.

MAST Upgrade↗
DOE OSTI2022

New Insights into Physical Aging‐Induced Structure Evolution in Carbon Molecular Sieve Membranes

Abstract Carbon molecular sieve (CMS) membranes offer the best available combination of scalable economical processability with excellent separation performance. Physical aging of CMS membranes causes pore structure changes over time that affect CMS membrane performance. To provide fundamental insights into the structure evolution in CMS membranes during physical aging, a combined dual‐mode sorption and transport model is used in this study to characterize the diffusion coefficients of gas molecules in fresh and 7‐day vacuum aged CMS membranes. The results show physical aging of CMS membrane is primarily “diffusion related” and such aging behavior simultaneously causes ultramicropore changes in the continuous phase and Langmuir phase of CMS membrane. The new insights offered in this study suggest strategies to control the physical aging of CMS membranes and even use it as a valuable tool to tune the separation performance of CMS membranes for demanding gas separations.

Liu, Zhongyun↗
DOE OSTI2023

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

Abstract The accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.

Engineering↗
DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗
DOE OSTI2024

Griffin: A MOOSE-based reactor physics application for multiphysics simulation of advanced nuclear reactors

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor physics application for multiphysics simulations of advanced reactor designs jointly developed by Idaho National Laboratory and Argonne National Laboratory. This paper summarizes the motivation, significance, architecture, design, and features of Griffin. Griffin offers flexible and extensible features to address the challenges associated with advanced reactor designs. These features range from fundamental particle transport to specific reactor physics tasks. The features cover a wide range including on-the-fly and traditional two-step cross-section generation methods, steady-state and transient transport solvers suitable for both heterogeneous and homogeneous models, high-fidelity depletion where thousands of isotopes can be tracked and low-fidelity depletion characterized by burnup, etc. The most fundamental aspect that sets Griffin apart from other reactor analysis codes is that it is developed based on the MOOSE framework. A modular development approach is strongly enforced, with multiphysics being an essential element considered since the beginning of Griffin’s development. Griffin links various MOOSE physics modules and couples to other MOOSE-based applications and non-MOOSE-based applications for multiphyiscs simulations. Griffin includes three modules: ISOXML for preparing and managing multigroup cross sections, radiation transport for solving the neutron transport equation, and reactor analysis for user-oriented reactor physics analysis functionalities. Griffin uses various finite element methods for spatial discretization, multigroup approximation for energy discretization and discrete ordinates method, spherical harmonics expansion method, and diffusion approximation for streaming direction discretization to solve the neutron transport equation. Griffin’s flexibility is evidenced through Griffin’s various applications to fast reactor, high-temperature reactor, pebble bed reactor, molten salt reactor, and microreactor designs. Griffin development follows the software quality assurance procedure for MOOSE-based applications and with software requirements consistent with the ASME NQA-1 standard. Griffin has been adopted into the reactor analysis system for the U.S. NRC and is in use at U.S. companies, universities and national laboratories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

42 ENGINEERING↗
DOE OSTI2021

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

We report a recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.

42 ENGINEERING↗
DOE OSTI2018

Neurocognitive and physical functioning in the Seveso Women's Health Study

Highlights: • Little research exists on the neurotoxic effects of dioxins in older women. • TCDD was associated with grip strength but not other measures of physical function. • TCDD exposure was not adversely associated with working memory. • This relationship was not modified by menopause nor timing of TCDD exposure around menarche. • TCDD susceptibility may increase as the women age, warranting further research. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is neurotoxic in animals but few studies have investigated its effects on the human brain. Related dioxin-like compounds have been linked to poorer cognitive and motor function in older adults, with effects more pronounced in women, perhaps due to the loss of neuro-protective estrogen in menopause. On 10 July 1976, a chemical explosion in Seveso, Italy, resulted in one of the highest known residential exposures to TCDD. In 1996, we initiated the Seveso Women's Health Study, a retrospective cohort study of the health of the women who were newborn to 40 years old in 1976. Here, we investigate whether TCDD exposure is associated with physical functioning and working memory more than 20 years later. Individual TCDD concentration (ppt) was measured in archived serum collected soon after the explosion. In 1996 and 2008, we measured physical functioning (n=154) and working memory (n=459), respectively. We examined associations between serum TCDD and motor and cognitive outcomes with multivariate linear regression and semi-parametric estimators. A 10-fold increase in serum TCDD was not associated with walking speed (adjusted β=0.0006 ft/s, 95% Confidence Interval (CI): −0.13, 0.13), upper body mobility (adjusted β=−0.06, 95% CI: −0.36, 0.23), or manual dexterity (adjusted β=0.34, 95% CI: −0.65, 1.33). We observed an inverted U-shaped association in grip strength, with poorer strength in the lowest and highest TCDD exposure levels. There was no association between TCDD and the Wechsler digit and spatial span tests. Neither menopause status at assessment nor developmental timing of exposure modified associations between TCDD and working memory. Our findings, in one of the only studies of TCDD's effects on neuropsychological and physical functioning in women, do not indicate an adverse effect on these domains, with the exception of a U-shaped relationship with grip strength. Given the limited assessment and relative youth of the women at this follow-up, future work examining additional neuropsychological outcomes is warranted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding fundamental effects of biofuel structure on ignition and physical fuel properties

The development of biochemicals and biofuels with advantaged properties of higher combustion efficiency at lower emission levels can be aided by a priori prediction of critical global characteristics based on molecular structure. Existing predictive techniques are incomplete for comprehensive ignition and physical fuel property prediction. Here this investigation focuses on filling this knowledge gap by developing a priori prediction techniques for predicting important ignition as well as physical fuel properties of promising advantaged biofuels. We demonstrate the utility of this a priori prediction technique on saturated (≤C 5 ) alcohols, both linear and branched in nature by correlating their molecular structure with both important ignition and physical properties of the fuels. This work utilizes a semi-automated method of predicting ignition characteristics via accurate fundamental-based calculations of the fuel radical cascades that govern low-temperature combustion. This numerical methodology is supported by the relevant fuel ignition delay time (IDT) and research octane number (RON) data experimentally measured by the Advanced Fuel Ignition Delay Analyzer (AFIDA). Phyiscal fuel property trends are also analyzed by correlating the molecular structure of the fuel to its physical characteristics. Based on their molecular structure, recommendations are made on an a priori method for selecting biofuels that have advantaged properties for the selected fuel application. Lastly, the utility of this investigation is extended beyond the subset of alcohols considered by analyzing the IDT trends of i-Pentanol and n-Hexanol, revealing the fidelity of the novel kinetic modelling approach and the accuracy of the predicted trends in this study.

09 BIOMASS FUELS↗
DOE OSTI2020

Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches

Permeability prediction has been an important problem since the time of Darcy. Most approaches to solve this problem have used either idealized physical models or empirical relations. In recent years, machine learning (ML) has led to more accurate and robust, but less interpretable empirical models. Using 211 core samples collected from 12 wells in the Garn Sandstone from the North Sea, this study compared idealized physical models based on the Carman-Kozeny equation to interpretable ML models. We found that ML models trained on estimates of physical properties are more accurate than physical models. The results show evidence of a threshold of about 10% volume fraction, above which pore-filling cement strongly affects permeability.

03 NATURAL GAS↗
DOE OSTI2016

Physical and hydraulic properties of modern sinter deposits: El Tatio, Atacama

We report sinters are siliceous, sedimentary deposits that form in geothermal areas. Formation occurs in two steps. Hot water circulates in the subsurface and dissolves silica from the host rock, usually rhyolites. Silica then precipitates after hot water is discharged and cools. Extensive sinter formations are linked to up-flow areas of fluids originating from high temperature (> 175 °C) deep reservoirs. Fluid geochemistry, microbial communities, and environmental conditions of deposition determine the texture of sinter and pore framework. Porosity strongly influences physical and hydraulic properties of rocks. To better understand the properties controlling the transport of fluids, and interpret geophysical observations in geothermal systems, here we studied 17 samples of modern geyserite sinter deposits (< 10 ka) from the active El Tatio geothermal field in northern Chile. We measured the physical properties (hydraulic, seismic, and electrical), and internal microstructure (using μX-Ray computed tomography). We find that the pore structure, and thus hydraulic and physical properties, is controlled by the distribution of microbial matter. Based on velocity-porosity relationships, permeability-porosity scaling, and image analysis of the 3D pore structure; we find that the physical and hydraulic properties of sinter more closely resemble those of vesicular volcanic rocks and other material formed by precipitation in geothermal settings (i.e., travertine) than clastic sedimentary rocks.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Physical and absorption properties of titanium nanoparticles incorporated into zinc magnesium phosphate glass

We report the influences of Titania (TiO{sub 2}) nanoparticles (NPs) on the physical and optical properties of melt quench synthesized zinc magnesium phosphate glasses. Five glass samples with composition (42 − x)P{sub 2}O{sub 5}–50ZnO–8MgO–xTiO{sub 2}, where x = 0, 1, 2, 3, 4 mol% are prepared and characterized. XRD pattern verified the amorphous nature of all samples. TEM images manifested the growth of Ti NPs of average size ≈ 5.78 nm. TiO{sub 2} NP concentration dependent variation in the physical properties including glass density, molar volume, molar refractivity, electronic polarizability and ionic packing density are determined. The values of glass refractive indices, density and ionic packing density are increased with the increase of TiO{sub 2} NP contents. Conversely, the Urbach energy, direct and indirect optical band gap are found to decrease with the increase of TiO{sub 2} NP concentration. These glass compositions may be potential for various solid state devices including laser. - Highlights: • TiO{sub 2} NP embedded self-cleaning phosphate glass are synthesized for the first time. • Well dispersed and uniform sized TiO{sub 2} NPs are grown. • Absorption spectra revealed prominent peak in the UV region. • TiO{sub 2} NPs strongly influenced the physical and absorption features of synthesized glasses. • The effects of TiO{sub 2} NPs on the physical and optical properties of these glasses are determined.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗