SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Visualizing data from neutron scattering experiments is the first step in understanding the physics. The program is intended to generate and plot cuts and slices, through the four dimensional single crystal inelastic datasets, measured on direct geometry neutron spectrometers at the Spallation Neutron Source (ARCS, CNCS, HYSPEC, SEQUOIA).
A description is provided of how fast magnetoacoustic waves are ducted along regions of low Alfven velocity (high density) in the corona, exhibiting a distinctive wave signature which may be used as a diagnostic probe of in situ coronal conditions (magnetic field strength, density inhomogeneity, etc.). Some observational knowledge of the start time of the impulsive wave source, possibly a flare, the start and end times of the generated wave event, and the frequency of the pulsations in that event permits a seismological deduction of the physical properties of the coronal medium in which the wave propagated. With good observations the theory offers a new means of probing the coronal atmosphere.
Black silicon (b-Si) photodiodes are an emerging technology that employs silicon nanostructures to enhance the efficiency of photon detection. Recently, we demonstrated nearly 100% quantum efficiency at <200 nm vacuum ultraviolet (VUV) wavelengths at ambient and in noble liquids, making such devices particularly useful for direct detection of scintillation light in noble liquid detectors. This is important in nuclear science experiments like DarkSide, LUX, XENON, and nEXO, where detection of weak VUV scintillation photons in noble liquids are used to identify rare physics radiation events. Here, we measured the response of b-Si photodiode to the scintillation light generated by alpha particles, all immersed in a LAr and LXe test cell. We established a guideline for future development of b-Si photodetectors with internal gain to reach single-photon sensitivity.
Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning techniques to identify important features that distinguish jets produced in heavy-ion collisions from jets produced in proton-proton collisions. We formulate the problem using binary classification and focus on leveraging machine learning in ways that inform theoretical calculations of jet modification: (i) we quantify the information content in terms of Infrared Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we identify optimally discriminating observables that are in principle calculable in perturbative QCD, and (iii) we assess the information loss due to the heavy-ion underlying event and background subtraction algorithms. We illustrate our methodology using Monte Carlo event generators, where we find that important information about jet quenching is contained not only in hard splittings but also in soft emissions and IRC-unsafe physics inside the jet. This information appears to be significantly reduced by the presence of the underlying event. We discuss the implications of this for the prospect of using jet quenching to extract properties of the QGP. Since the training labels are exactly known, this methodology can be used directly on experimental data without reliance on modeling. We outline a proposal for how such an experimental analysis can be carried out, and how it can guide future measurements.
Electrodermal activity has long been used for mental activity monitoring by measuring skin conductance at specific locations, such as fingertips, with high sweat gland density. However, electrodermal activity has not been considered useful for physical activity monitoring, where large sweat volumes are generated, resulting in the accumulation of sweat at the skin–electrode interface and, thus, preventing further dynamic response to sweating events. Here we show that electrodermal activity can be used as a proxy for sweat loss measurement under both low and high physical activity levels. We use wearable sweat sensors that consist of water-permeable electrodes and microfluidic-based sweat analysers, and show that skin conductance is proportional to the instantaneous sweat loss. We demonstrate that sweat loss during exercise can be estimated by integrating skin conductance over time, which can be applied to assess the body hydration status of exercisers. From multisite measurements of skin conductance, we show that the wrist, forearm and upper arm are reflective of physical activity levels, whereas the finger is indicative of mental activity. Simultaneous measurement of two different sites selectively decouples mental and physical activities.
Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.
Compelling evidence indicates that the solid Earth consists of two physicochemically distinct zones separated radially in the middle of the lower mantle at ~1800 km depth. The inner zone is governed by pressure-induced physics and chemistry dramatically different from the conventional behavior in the outer zone. These differences generate large physical and chemical potentials between the two zones that provide fundamental driving forces for triggering major events in Earth’s history. One of the main chemical carriers between the two zones is H 2 O in hydrous minerals that subducts into the inner zone, releases hydrogen, and leaves oxygen to create superoxides and form oxygen-rich piles at the core–mantle boundary, resulting in localized net oxygen gain in the inner zone. Accumulation of oxygen-rich piles at the base of the mantle could eventually reach a supercritical level that triggers eruptions, injecting materials that cause chemical mantle convection, superplumes, large igneous provinces, extreme climate changes, atmospheric oxygen fluctuations, and mass extinctions. Interdisciplinary research will be the key for advancing a unified theory of the four-dimensional Earth system.
Ever since Galileo's telescope made the rugged lunar surface more clearly visible (in 1610), men have strived to learn more about the origin and history of the Earth's big natural satellite, and never has so much progress been made as in the last few years. The fifth manned lunar landing was in a highlands area, quite different from the sites visited previously, and the discoveries there now seem certain to result in significant improvements in the hypotheses of lunar scientists. Much of the Moon's surface is similar to the Descartes Highlands that the Apollo 16 astronauts examined. From this highly productive mission, more photographs were obtained than on any previous Apollo flight, a greater amount of time was spent outside the lunar module, a greater weight of scientific equipment landed on the Moon, and a record weight of scientific samples was brought back to laboratories on Earth. The network of automatic scientific stations at work on the Moon was extended into a new area and has since detected a moonquake caused by the largest meteoroid impact that has yet been recorded. Additional experiments on the surface and in flight also were successfully performed on this mission for the enlightenment of students of natural phenomena. The Apollo 16 astronauts observed, and scientists studying material they collected have subsequently deduced, that this landing site differed surprisingly from earlier expectations. Future generations consequently may benefit from better concepts of the operation of the solar system and events throughout the physical universe than have hitherto been possible. This volume is but one of a series of NASA Special Publications being issued promptly to document potentially significant discoveries in the course of the Apollo Program, thereby possibly increasing their usefulness to scientists grappling with problems that have long perplexed mankind.
In the coming decade, the drive to increase the scientific returns on capital investment and to reduce costs will force automation to be implemented in many of the scientific tasks that have traditionally been manually overseen. Thus, spacecraft autonomy will become an even greater part of mission operations. While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. The new generation of space-based telescopes/observatories will see deeper, with greater clarity, and they will generate data at an unprecedented rate. Yet, while onboard data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant bottleneck and cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals of variability or minimum brightness, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.
The MicroBooNE detector is a liquid argon time projection chamber based at Fermilab that sits on-axis of the Fermilab’s Booster Neutrino Beam and off-axis of Fermilab’s NuMI beam.MicroBooNE has investigated the MiniBooNE anomaly using the BNB and published a wide array of inclusive and exclusive neutrino-argon cross section results using both neutrino beams. These proceedings highlight recent public results from MicroBooNE on cross sections involving charged baryons in the final state without mesons. These results have been compared to various event generator simulations with agreement observed between the data and predictions.
Explosive maritime cyclogenetic events are usually aligned with the warm currents off the east coasts of Japan and the U.S. One such event occurred during FGGE in an area which featured good instrumented ship and aircraft coverage. The UCLA 4th order GMC was employed to generate predictions of the event for comparisons with the real-world data. FGGE III-b data were used to initialize the model calculations. Attention was directed to the physical processes causing the onset of the cyclogenesis, particularly the heat budgets of the fluxes involved. The diabatic heating was observed to be equivalent to the horizontal temperature advection. The main source of energy around the cyclone center was identified as the conversion of energy from static energy to kinetic energy. Further analyses of similar events are required if the present results are to be validated.
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider.
While recent missions have made great strides in the ability to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in responding quickly to science driven events. Also, the new generation of space-based telescopes will generate data at an unprecedented rate. Yet, while on-board data processing and storage capability will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize early if an observation will not meet the science goals, and react accordingly, can have a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from the scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground systems - both human and machine - for confirmation and/or further high priority analyses.
Accurate and realistic geological modeling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.
Recent temperature extremes have shattered previously observed records, reaching intensities that were inconceivable before the events. Could the possibility of an event with such unprecedented intensity as the 2021 Pacific Northwest heatwave have been foreseen, based on climate model information available before the event? Could the scientific community have quantified its potential intensity based on the current generation of climate models? Here, we demonstrate how an ensemble boosting approach can be used to generate physically plausible storylines of a heatwave hotter than observed in the Pacific Northwest. We also show that heatwaves of much greater intensities than ever observed are possible in other locations like the Greater Chicago and Paris regions. In order to establish confidence in storylines of ‘black swan’-type events, different lines of evidence need to be combined along with process understanding to make this information robust and actionable for stakeholders.
Electromagnetic interference (EMI) signatures resulting from arc discharges are characterized, and the effects of electrostatic discharges (ESDs) on the design of spacecraft systems are investigated. EMI characterization experiments were performed on Mylar, Teflon, Kapton, fused silica, and fiberglass in a vacuum chamber with acrylic walls; the experimental design and procedures are described. Discharge current pulses and RF spectra generated by the sample materials are examined. The relation between the magnitude of EMI generated during an ESD event and the material, environment, and geometry is studied. The solar-array/plasma interaction is analyzed; particular attention is given to the rate of discharge as a function of plasma density. The physical mechanisms of ESD-generated EMI are discussed. The data reveal that ESD parameters are dependent on the test environment.