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Horizontal-branch (HB) sequences have been computed for different rates of convective overshooting at the edge of the convective core. We find that the convective core undergoes a series of 'breathing pulses' during most of the HB phase. The average behavior of these sequences closely mimics the behavior of sequences computed with canonical semiconvection.
The Roadmap for the Structure and Evolution of the Universe (SEU) Theme embraces three fundamental, scientific quests: (1) To explain structure in the Universe and forecast our cosmic destiny, (2) To explore the cycles of matter and energy in the evolving Universe, (3) To examine the ultimate limits of gravity and energy in the Universe. We develop these quests into six focused research campaigns: (1) Identify dark matter and learn how it shapes galaxies and systems of galaxies, (2) Explore where and when the chemical elements were made (3) Understand the cycles in which matter, energy, and magnetic field are exchanged between stars and the gas between stars, (4) Discover how gas flows in disks and how cosmic jets are formed, (5) Identify the sources of gamma-ray bursts and high-energy cosmic rays, (6) Measure how strong gravity operates near black holes and how it affects the early Universe. These campaigns lead to a portfolio of future major missions of great scientific interest and popular appeal, strongly endorsed by the scientific community. Many have undergone significant initial study. Some are in a state of readiness that make them ideal candidates for the present Office of Space Science Strategic Plan; others may well feature in the next Plan. Each provides a golden scientific opportunity to advance our understanding of the Universe. We have identified three top-priority near-term science objectives together with missions to accomplish these goals. The three problems span a diverse range of subdisciplines, of observational technique, of timescales, and of cost, and are thus complementary, forming a coherent core program for the SEU theme in the 2003-2007 timeframe.These are : (1) Obtain precise measures of the chemical composition and physical conditions in objects ranging from the closest stars to the most distant quasars, (2) Utilize gravitational radiation as a probe of supermassive blackholes throughout the universe, (3) determine the nature of the highest energy cosmic rays. We also describe a small number of exciting missions which are strong candidates for new start status in the midterm, 2008-2013, pending technology development. These missions tackle fundamental problems through the entire electromagnetic spectrum, from the radio through gamma rays, and in many cases develop fascinating technologies with applicability not only elsewhere in NASA but outside of space science as well. Finally, we describe a set of "vision missions," which stretch our scientific imagination and set technology challenges for our field.
Abstract not provided.
Accurate modeling and prediction of damage induced by dynamic loading in materials have long proved to be a difficult task. Examination of postmortem recovered samples cannot capture the time-dependent evolution of void nucleation and growth, and attempts at analytical models are hindered by the necessity to make simplifying assumptions, because of the lack of high-resolution, in situ, time-resolved experimental data. We use absorption contrast imaging to directly image the time evolution of spall damage in metals at ~1.6-μm spatial resolution. We observe a dependence of void distribution and size on time and microstructure. The insights gained from these data can be used to validate and improve dynamic damage prediction models, which have the potential to lead to the design of superior damage-resistant materials.
Theoretical descriptors differentiate the catalytic activity of materials for the oxygen evolution reaction by the strength of oxygen binding in the reactive intermediate created upon electron transfer. Recently, time-resolved spectroscopy of a photo-electrochemically driven oxygen evolution reaction followed the vibrational and optical spectra of this intermediate, denoted M-OH*. However, these inherently kinetic experiments have not been connected to the relevant thermodynamic quantities. Here we discover that picosecond optical spectra of the Ti-OH* population on lightly doped SrTiO 3 are ordered by the surface hydroxylation. A Langmuir isotherm as a function of pH extracts an effective equilibrium constant relatable to the free energy difference of the first oxygen evolution reaction step. Thus, time-resolved spectroscopy of the catalytic surface reveals both kinetic and energetic information of elementary reaction steps, which provides a critical new connection between theory and experiment by which to tailor the pathway of water oxidation and other surface reactions.
The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.
Preliminary experimental data on the effectiveness of conventional radars in measuring the movement and evolution of meteorological echoes when the radar is connected to a programmable real-time processor are examined. In the processor programming is accomplished by conceiving abstract machines which constitute the actual programs used in the methods employed. An analysis of these methods, such as the center of gravity method, the contour-displacement method, the method of slope, the cross-section method, the contour crosscorrelation method, the method of echo evolution at each point, and three-dimensional measurements, shows that the motions deduced from them may differ notably (since each method determines different quantities) but the plurality of measurement may give additional information on the characteristics of the precipitation.
Purpose: This essay is a retelling of general relativity in a language in which space-time geometry is expressed as a fluid. This trivial and useful reformulation gives 1) a non-perturbative covariant description of cosmological inhomogeneities and 2) a simple formula describing how cosmic inhomogeneities are generated on super-horizon scales. Methods: Equating the Ricci curvature with the associated matter stress-energy gives a description of space-time geometry in terms of fluid properties. These locally measurable (covariant) non-perturbative quantities are in some ways superior to commonly used "gauge invariant" quantities. The dynamics of a quantity (kurvature) which describes cosmological inhomogeneities is described in detail. A detailed comparison is made of space-time fluid dynamics with that of a classical (Newtonian physics) fluid. Results: The fluid lexicon permits an unambiguous definition of the velocity of space-time. The evolution of the space-time fluid is in many ways identical with that of the classical fluid when expressed in Lagrangian coordinates. Kurvature is a measure of the specific binding energy of the fluid and is a most useful covariant measure of cosmological inhomogeneities. For plausible matter models kurvature will increase, even on super-horizon scales, due to non-linear hydrodynamic effects rather than gravity. This phenomena is also exhibited by classical fluids. Conclusion: The space-time fluid representation of geometrodynamics gives a simple and useful description of the evolution of cosmological inhomogeneities.
Light-driven water splitting by plants, algae and cyanobacteria is pivotal for global bioenergetics and biomass formation. A manganese cluster bound to the photosystem II proteins catalyzes the complex reaction at high rate, but the rate-determining factors are insufficiently understood. Here we trace the oxygen-evolution transition by time-resolved polarography and infrared spectroscopy for cyanobacterial photosystems genetically modified at two strategic sites, complemented by computational chemistry. Our results highlight three rate-determining roles of the protein environment of the metal cluster: acceleration of proton-coupled electron transfer, acceleration of substrate-water insertion after O 2 -formation, and balancing of rate-determining enthalpic and entropic contributions. Whereas in general the substrate-water insertion step may be unresolvable in time-resolved experiments, here it likely becomes traceable because of deceleration by genetic modification. Our results may stimulate new time-resolved experiments on substrate-water insertion in photosynthesis, clarification of enthalpy-entropy compensation in enzyme catalysis, and knowledge-guided development of inorganic catalyst materials.
The advent of short-pulse electron and x-ray sources has enabled pump-probe approaches for elucidating ultrafast materials dynamics. From such studies, a comprehensive picture of the time-dependent evolution of the initial steps of energy deposition, propagation, relaxation, and conversion in a wide range of materials can be generated. In this article, we provide an overview of the capabilities of femtosecond electron and x-ray scattering for resolving structural dynamics of materials. With such approaches, time resolutions are ultimately limited by the durations of the electron and x-ray pulses, and dynamics can be studied at length scales spanning atomic to mesoscale dimensions. The articles in this issue represent a cross section of the vigorous activity occurring in the study of light-induced ultrafast materials dynamics as it relates to charge carriers, surfaces and interfaces, lattice-coupling mechanisms, coherent structural motions, and next-generation instrument development. Here, the approaches highlighted here are leading to new physical insights, new possibilities for engineering the properties of matter, and ultimately, a new understanding of materials functionality on ultrasmall and ultrashort spatiotemporal scales.
Consideration is given to the estimate of Maher and Stevenson (1988) of the time at which life could have developed on earth through chemical evolution within a time interval between impact events, assuming chemical or prebiotic evolution times of 100,000 to 10,000,000 yrs. An error in the equations used to determine the time periods between impact events in estimating this time is noted. A revised equation is presented and used to calculate the point in time at which impact events became infrequent enough for life to form. By using this equation, the finding of Maher and Stevenson that life could have first originated between 4,100 and 4,300 million years ago is changed to 3,700 to 4,000 million years ago.
Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.
Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.
The inclusion of van der Waals attractions in the interaction between cloud droplets has been recently shown to significantly increase the collision efficiencies of the smaller droplets. In the current work, these larger values for the collision efficiencies are used in a population dynamics model of the droplet size distribution evolution with time, in hopes of at least partially resolving the long-standing paradox in cloud microphysics that predicted rates of the onset of precipitation are generally much lower than those which are observed. Evolutions of several initial cloud droplet spectra have been tracked in time. Size evolutions are compared as predicted from the use of collision efficiencies computed using two different models to allow for droplet-droplet contact: one which considers slip flow effects only, and one which considers the combined effects of van der Waals forces and slip flow. The rate at which the droplet mass density function shifts to larger droplet sizes is increased by typically 20-25 percent, when collision efficiencies which include van der Waals forces are used.
In this work, we conducted a suite of experiments to evaluate the long-term permeability evolution of shale rocks under constant effective stress, before, during, and after interaction with supercritical carbon dioxide (scCO 2 ). To do so, we measured the time-dependent evolution of argon permeabilities for the pre- and post-reacted samples (i.e., before and after long-term interaction with scCO 2 ). In addition, we obtained permeability evolution during long-term interaction with scCO 2 . The samples showed either relatively constant permeabilities or a moderate decrease during pre reaction long-term argon tests. The permeability evolution during long-term CO 2 tests showed continuous increase, continuous decrease, or cycles of increase/decrease in permeability. The long term response of the samples to CO 2 included phenomena such as 20 (i) salt precipitation, (ii) swelling-induced cracks, and (iii) carbonate dissolution. While it is obvious that salt precipitation and swelling-induced cracks decrease and increase the permeability, respectively, the sample response to carbonate dissolution proved to be more complex and may increase or decrease the permeability. The permeability evolution during post-reaction long-term argon injection is also affected by the contribution of each of these three phenomena, during long-term interaction with CO 2 . We observe increase, decrease, and constant permeability evolution during post-reaction argon tests. Our experiments reveal that the initial permeability of the samples plays a significant role on the long-term permeability response of shales in the presence argon and scCO 2 fluids. This study shows, when shales are hydraulically fractured with CO 2 , their initial permeability has a more significant role than their permeability evolution over time.
The magneto-Rayleigh-Taylor (MRT) instability has been explored in great detail in previous work using magnetohydrodynamic and kinetic models for low-beta plasmas. The work presented here extends previous studies of this instability to regimes where finite-Larmor-Radius (FLR) effects may be important. Comparisons of the MRT instability are made using a 5-moment and a 10-moment two-fluid model, the two fluids being ions and electrons. The 5-moment model includes Hall stabilization, whereas the 10-moment model includes Hall and FLR stabilization. Results are presented for these two models using different electron mass to understand the role of electron iner- tia in the late-time nonlinear evolution of the MRT instability. For the 5-moment model, the late- time nonlinear MRT evolution does not significantly depend on the electron inertia. Yet, when FLR stabilization is important, the 10-moment results show that a lower ion-to-electron mass ratio (i.e., larger electron inertia) under-predicts the energy in high-wavenumber modes due to larger FLR stabilization.
Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.
We have selected square subareas (110 km on a side) from coastal zone color scanner (CZCS) and advanced very high resolution radiometer (AVHRR) images for 1981 in the California Current region off northern California for which we could identify sequences of cloud-free data over periods of days to weeks. We applied a two-dimensional fast Fourier transformation to images after median filtering, (x, y) plane removal, and cosine tapering. We formed autospectra and coherence spectra as functions of a scalar wavenumber. Coherence estimates between pairs of images were plotted against time separation between images for several wide wavenumber bands to provide a temporal lagged coherence function. The temporal rate of loss of correlation (decorrelation time scale) in surface patterns provides a measure of the rate of pattern change or evolution as a function of spatial dimension. We found that patterns evolved (or lost correlation) approximately twice as rapidly in upwelling jets as in the 'quieter' regions between jets. The rapid evolution of pigment patterns (lifetime of about 1 week or less for scales of 50-100 km) ought to hinder biomass transfer to zooplankton predators compared with phytoplankton patches that persist for longer times. We found no significant differences between the statistics of CZCS and AVHRR images (spectral shape or rate of decorrelation). In addition, in two of the three areas studied, the peak correlation between AVHRR and CZCS images from the same area occurred at zero lag, indicating that the patterns evolved simutaneously. In the third area, maximum coherence between thermal and pigment patterns occurred when pigment images lagged thermal images by 1-2 days, mirroring the expected lag of high pigment behind low temperatures (and high nutrients) in recently upwelled water. We conclude that in dynamic areas such as coastal upwelling systems, the phytoplankton cells (identified by pigment color patterns) behave largely as passive scalars at the mesoscale and that growth, death, and sinking of phytoplankton collectively play at most a mariginal role in determining the spectral statistics of the pigment patterns.