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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Global Plasmaspheric Imaging: A New "Light" Focusing on Familiar Questions

Until recently plasmaspheric physics, for that matter, magnetospheric physics as a whole, has relied primarily on single point in-situ measurement, theory, modeling, and a considerable amount of extrapolation in order to envision the global structure of the plasmasphere. This condition changed with the launch of the IMAGE satellite in March 2000. Using the Extreme Ultraviolet (EUV) imager on WAGE, we can now view the global structure of the plasmasphere bathed in the glow of resonantly scattered 30.4 nm radiation allowing the space physics community to view the dynamics of this global structure as never before. This talk will: (1) define the plasmasphere from the perspective of plasmaspheric physics prior to March 2000; (2) present a review of EUV imaging optics and the IMAGE mission; and focus on efforts to understand an old and familiar feature of plasmaspheric physics, embedded plasmaspheric density troughs, in this new global light with the assistance of forward modeling.

Adrian, M. L.↗
NASA NTRS2003

Striding Towards Better Physical Therapy

This paper presents a discussion on a new rehabilitative device that promises to improve physical therapy for patients working to regain the ability to walk after facing traumatic injuries or a degenerative illness. Produced by Enduro Medical Technology, of East Hartford, Connecticut, the Secure Ambulation Module (S.A.M.) creates a stable and secure environment for patients as they stand during ambulation therapy.

Source record↗
DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗
NASA NTRS1994

Accurate modeling of F-region electron densities

In the past year, we have made considerable progress in a number of areas including algorithm development, completion of two major case studies, and the development of a new EUV flux model. As a result, there has been a major improvement in our ability to model global emissions in support of NASA's imaging plans. Activity highlights include the following: developed a new algorithm to allow physical models to reproduce observed NmF2; investigated the relationship between NmF2 and F10.7 at Millstone Hill during 1990; developed a new solar EUV flux model; statistical survey of anomalously high nighttime electron T(sub e) at Millstone Hill; conducted a case study of the March 1990 magnetic storm; and conducted a comparison between theory and data of magnetically quiet behavior of the winter ionosphere at Millstone Hill.

Source record↗
DOE OSTI2025

Searches for New Long-Lived Particles and Upgrade to the ATLAS Inner Detector (Final Technical Report)

The search for new fundamental particles is one of the defining goals of the Large Hadron Collider (LHC). The discovery of the Higgs Boson by the ATLAS and CMS collaborations provided the capstone of the Standard Model of particle physics, but outstanding questions remain. Why does the Higgs boson have a mass of 125 GeV when its natural mass would be many orders of magnitude larger? Is there a universal symmetry which unites all three forces described by the Standard Model? Can that symmetry be extended to include gravity? Is dark matter, evidenced by astronomical observations, made of a particle that interacts via Standard Model forces with the rest of matter? Together, these motivations provide compelling arguments that new physical processes await discovery. This project addressed some outstanding questions about the fundamental particles and their interactions with the ATLAS experiment at the Large Hadron Collider. In particular, the project improved the discovery potential for new, long- lived particles produced via electroweak processes in proton-proton collisions and set world-leading limits on their existence for certain values of their potential mass and lifetime. To achieve this, the project developed new data analysis methods, developed new triggers to select events with new long-lived particles during data-taking of the ATLAS experiment, and analyzed the largest proton–proton collision dataset ever produced. The project also supported significant development of the data acquisition software for the upgrade to the ATLAS inner detector, the Inner TracKer (ITk). The upgrade of the ATLAS inner detector is essential to the success of the entire Phase II physics program on ATLAS. Personnel supported by the project provided support for integration, assembly, and testing of the inner two layers of the ITk pixel system during its prototype and pre-production phase. Four PhD students and two post-doctoral scholars were supported by the grant and received invaluable scientific training as part of the research endeavor. The students and postdocs gained essential professional skills in the areas of advanced data analysis techniques, statistical analysis of data and simulation, programming in C++ and Python, hardware and instrumentation development, and presentation and collaboration skills. Additionally, approximately ten undergraduate students supported through other funding sources participated in research activities synergistic with the goals of this project, receiving essential mentorship from the personnel supported by this project.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The Forward Physics Facility at the High-Luminosity LHC

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Optimization of an Interaction Region Quadrupole Magnet for a Future Electron-Ion Collider at JLab

The Jefferson Lab Electron Ion Collider (JLEIC) is a proposed new machine for nuclear physics research. It uses the existing CEBAF accelerator as a full energy injector to deliver 3 to 11 GeV electrons into a new electron collider ring. An all new ion accelerator and collider complex will deliver up to 200 GeV protons. The machine has luminosity goals of 10 34 cm -2 sec -1 . The whole detector region including forward detection covers about 80 meters of the JLEIC complex. The lattice file for the machine has changed to accommodate the higher energy; this change requires all the magnets in the interaction region to change. This paper will describe the requirements for both the ion and electron beam superconducting magnets around the interaction point. All the Final Focusing Quadrupole (FFQ) magnets now have preliminary designs. This paper will describe the optimization process for one of these FFQs. The optimization is performed using the optimization module of SIMULIA Opera FEA and compared with ROXIE. An analytical design tool has also been developed to estimate the peak field in the coils. The quadrupole has been optimized for peak field in the coils, and magnetic length.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Multiple ferroelectric nematic phases of a highly polar liquid crystal compound

Ferroelectric nematic liquid crystals represent not only fascinating, fundamental science, but they also hold promise for new technologies including high-density power storage or sub-millisecond switching information displays. In this work, we describe the synthesis and measurements of the physical properties of a new compound, 4-nitrophenyl 4-[(2,4-dimethoxylbenzoyl)oxy]-2-fluorobenzoate (RT11001). This material exhibits multiple, highly polar, ferroelectric nematic phases that have not been previously reported. Here we employ a wide range of physical characterisation methods including differential scanning calorimetry (DSC), mass density measurement, optical birefringence, polarising optical microscopy (POM), dielectric spectroscopy, electric current analysis, electro-optical switching, small-angle and wide-angle x-ray scattering measurements to show that RT11001 has multiple, distinct ferroelectric phases. We argue that the highest temperature phase is a polar nematic fluid with non-polar smectic clusters. Directly below appears to be a transition to another polar nematic phase containing polar positionally ordered clusters. Lastly, there are indications of an additional, polar biaxial liquid crystal phase at lower temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of Viscosity of a Deep Eutectic Solvent on CO 2 Capture Performance in an Energy-Efficient Membrane Contactor-Based Process

Greenhouse gas contributions to climate change have driven intense interest in the separation of CO 2 from wet flue gas streams. Deep eutectic solvents (DESs) are an emerging class of highly selective CO 2 absorbents. A prototypical DES, reline, is a mixture of choline chloride and urea. Reline is a thermally stable, nontoxic, and biodegradable solvent with negligible volatility and is inexpensive. We demonstrate a scalable and energy-efficient hollow fiber membrane contactor (HFMC)-based process using a green solvent for CO 2 capture. This process uses reline in HFMC to provide close interfacial interactions and contact between DES and CO 2 . This approach overcomes the disadvantages associated with direct absorption in DES and could potentially be applied to a variety of solvent-based CO 2 capture methods. Commercial, low-cost polymer hollow fiber membranes were evaluated for the capture of CO 2 with reline. From a mixed gas containing N 2 and CO 2 , the DES-based HFMC separated CO 2 with a purity of 97 mol %. The effect of the viscosity of reline on the CO 2 capture performance was investigated by adding water to the reline. The addition of water to reline significantly reduced its viscosity, which led to a permeate flux of 170 mmol/(m 2 ·h) at 35 °C, 4 bar, and 60 wt % water in solvent, which was approximately 8 times higher than that of the pure reline in the membrane contactor system. In situ Fourier transform infrared spectroscopy and nuclear magnetic resonance (NMR) revealed that reline absorbs CO 2 by physical absorption without forming new chemical compounds and that CO 2 separation by reline occurs via the pressure swing mechanism. This research provides fundamental insights about green physical solvent-based separation processes and a pathway toward industrial deployment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The use of idealised experiments in testing a new convective parametrization: Performance of CoMorph-A

CoMorph is a new mass-flux convection parametrization under development at the Met Office designed for use within the Unified Model and its successor model, LFRic. Use of a three-dimensional idealised model enables controlled tests of the performance of the scheme across different regimes. This includes the interaction between the physical parametrizations and the resolved dynamics, allowing study of the emergent organisation of convection on the resolved scale. A selection of well-known cases is revisited here, with the purpose of documenting the extent to which CoMorph captures a range of important, but challenging, behaviour such as the diurnal cycle and sensitivity to tropospheric moisture. Simulations using CoMorph-A, a new physics package, that has been demonstrated to perform well at numerical weather prediction (NWP) and climate scales, are compared against the current global atmosphere configuration and high-resolution results. In addition to an entirely new convection scheme, the package of changes includes significant changes to the cloud, microphysics, and boundary-layer parametrizations. Recognising that CoMorph-A is the first version of a scheme that will continue to be substantially developed and to obtain good performance, compromises in tuning have had to be made. These idealised tests therefore show what works well in this configuration, and what areas will require further work. As such, it is quite a demanding testbed and could be viewed as some of the equipment required for a “convective playground”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantitative proton radiography and shadowgraphy for arbitrary intensities

Charged-particle radiography and shadowgraphy data can be directly inverted to obtain a line-integrated transverse Lorentz force or a line-integrated transverse refractive index gradient if intensity modulations due to scattering and absorption are negligible, and angular deflections are small. We develop a new direct-inversion algorithm based on plasma physics and compare it to a new Monge–Ampère code and an existing power diagram code. The measured or source intensity is represented by electrons subject to drag, and the other intensity by fixed ions. The decrease in kinetic plus electrostatic energy determines convergence. The displacement of the electrons from their initial to their equilibrium positions determines the line-integrated force or refractive index gradient. We have implemented two approaches: PIC (particle in cell) and Lagrangian fluid, in 1-D and 2-D. The PIC code works for arbitrary intensities, can work efficiently in parallel, and can make use of existing codes. The Lagrangian code requires less memory and is faster than the PIC code without massively parallel processing, but fails in 2-D for large intensity modulations. The Monge–Ampère code is by far the fastest in 2-D, without massively parallel processing, but fails for intensities with large voids, high contrast ratios and large deflections across the boundaries, and could not obtain the degree of convergence possible with the PIC code. As a result, the power diagram code was by far the slowest and most memory intensive, and failed for large peaks in the measured intensity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2007

Challenges in 21st Century Physics

We are truly fortunate to live in one of the great epochs of human discovery, a time when science is providing new visions and understanding about ourselves and the world in which we live. At last, we are beginning to explore the Universe itself. One particularly exciting area of advancement is high-energy physics where several existing concepts will be put to the test. A brief survey will be given of accomplishments in 20th Century physics. These include relativity and quantum physics which have produced breakthroughs in cosmology, astrophysics, and high-energy particle physics. The current situation is then assessed, combining the last 100 years of progress with new 21st Century challenges about unification and where to go next. Finally, the future is upon us. The next frontier in experimental high-energy physics, the Large Hadron Collider (LHC) at CERN in Geneva, is scheduled to begin coming online this year (2007). The potential for the LHC to address several of the significant problems in physics today will be discussed, as this great accelerator examines the predictions of the Standard Model of particle physics and even cosmology. New physics and new science will surely emerge and a better vision of the world will unfold.

Wilson, Thomas L.↗
DOE OSTI2020

Stress-Drop and Source Scaling of the 2019 Ridgecrest, California, Earthquake Sequence

Stress drop, while difficult to measure reliably and at scale, is a key source parameter for understanding the earthquake rupture process and its relationship to strong ground motion. Here, we use a P-wave spectral decomposition approach, designed for large and densely sampled datasets, to measure earthquake stress drop in the region surrounding the 2019 Ridgecrest, California, earthquake sequence. With more than 11,000 measurements of earthquake stress drop in the 20-yr time period from 2000 through 2019, this dataset provides an opportunity to understand how coseismic stress changes and how other geophysical factors relate to the distribution of stress drop and its evolution in space and time. We observe a mild but persistent deviation from self-similar scaling, with larger events having systematically higher stress drops, though this trend depends on the assumption of an omega-square source spectral model. Earthquake stress drop increases with hypocentral depth in this study region, and the Ridgecrest aftershocks tend to have higher stress drops than the pre-event seismicity. This is in part due to their deeper hypocenters. Coherent spatial patterns of stress drop in the aftershock sequence correlate with the slip distribution of the M 7.1 mainshock, whose northwest rupture tip terminated in a long-lived zone of enervated stress drop. Although physical interpretation of these results is complicated by the trade-offs between the timing, depth, and location of these earthquakes, the observations provide new insight into the physics of the earthquake source in an area of renewed seismic activity in southern California.

58 GEOSCIENCES↗
DOE OSTI2025

QuadTune version 1: a regional tuner for global atmospheric models

When a new, better-formulated physical parameterization is introduced into a global atmospheric model, aspects of the global model solutions are sometimes degraded. Then, in order to use the new global model to address science questions, there is an incentive to restore its accuracy. Oftentimes this restoration is achieved by tuning of model parameter values. Unfortunately, the retuning process is expensive because characterizing the parameter dependence requires numerous time-consuming global simulations. To reduce the cost of tuning, this manuscript introduces a “poor man's” model tuner, “QuadTune”. QuadTune carves the globe into regions and approximates the model parameter dependence through the use of an uncorrelated quadratic emulator (i.e., response surface). The simplicity of the emulator reduces the required number of global model simulations and aids explainability of tuner behavior. Tuning removes parametric error but leaves behind model structural error. Structural error manifests itself as regional residual biases, such as stubborn biases and tuning trade-offs. To visualize these residual biases, QuadTune's software includes a set of diagnostic plots. This paper illustrates the use of the plots for characterizing residual biases with an example tuning problem.

Environmental sciences↗
DOE OSTI2022

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Novel features and GPU performance analysis for EM particle transport in the Celeritas code

Celeritas is a new computational transport code designed for high-performance simulation of high-energy physics detectors. This work describes some of its current capabilities and the design choices that enable the rapid development of efficient on-device physics. The abstractions that underpin the code design facilitate low-level performance tweaks that require no changes to the higher-level physics code. We evaluate a set of independent changes that together yield an almost 40% speedup over the original GPU code for a net performance increase of 220× for a single GPU over a single CPU running 8.4M tracks on a small demonstration physics app.

Johnson, Seth R.↗
NASA NTRS2019

A Long-Term Vision for Space-Based Interferometry

The processes leading to the formation of planets; the extreme physics occurring near the event horizon of black holes; detailed studies of exoplanets through spectral-spatial mapping: new and unique insights into the physical processes involved across nearly the whole gamut of astrophysics await discovery at small angular scales. The fine spatial resolution needed to explore these processes, however, lies beyond the capabilities of current astronomical facilities and nearly all proposed future facilities. Interferometers can crack this angular resolution problem, and space-based interferometry missions promise to explore entirely new regions of scientific phase space, providing unique new insights into the physical processes lurking at small angular scales.

Rinehart, S. A.↗
DOE OSTI2020

Insights into cosmological structure formation with machine learning.

Our modern understanding of cosmological structure formation posits that small matter density fluctuations present in the early Universe, as traced by the cosmic microwave background, grow via gravitational instability to form extended haloes of dark matter. A theoretical understanding of the structure, evolution and formation of dark matter haloes is an essential step towards unravelling the intricate connection between halo and galaxy formation, needed to test our cosmological model against data from upcoming galaxy surveys. Physical understanding of the process of dark matter halo formation is made difficult by the highly non-linear nature of the haloes' evolution. I describe a new approach to gain physical insight into cosmological structure formation based on machine learning. This approach combines the ability of machine learning algorithms to learn non-linear relationships, with techniques that enable us to physically interpret the learnt mapping. I describe applicatio ns of th e method, with the aim of investigating which aspects of the early universe density field impact the later formation of dark matter haloes. First I present a case where the process of halo formation is turned into a binary classification problem; the algorithm predicts whether or not dark matter `particles' in the initial conditions of a simulation will collapse into haloes of a given mass range. Second, I present its generalization to regression, where the algorithm infers the final mass of the halo to which each particle will later belong. I show that the initial tidal shear does not play a significant role compared to the initial density field in establishing final halo masses. Finally, I demonstrate that extending the framework to deep learning algorithms such as convolutional neural networks allows us to explore connections between the early universe and late time haloes beyond those studied by existing analytic approximations of halo collapse.

79 ASTRONOMY AND ASTROPHYSICS↗