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At least 631 records · Page 35

Oscillation-like diffusion of two-dimensional liquid dusty plasmas on one-dimensional periodic substrates with varied widths

The long-time diffusion of two-dimensional dusty plasmas on a one-dimensional periodic substrate with varying widths is investigated using Langevin dynamical simulations. When the substrate is narrow and the dust particles form a single row, the diffusion is the smallest in both directions. In this study, we find that as the substrate width gradually increases to twice its initial value, the long-time diffusion of the two-dimensional dusty plasmas first increases, then decreases, and finally increases again, giving an oscillation-like diffusion with varying substrate widths. When the width increases to a specific value, the dust particles within each potential well arrange themselves in a stable zigzag pattern, greatly reducing the diffusion and leading to the observed oscillation in the diffusion with the increase in the width. In addition, the long-time oscillation-like diffusion is consistent with the number of dust particles that are hopping across the potential wells of the substrate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Constraining early dark energy with gravitational waves before recombination

In this work, we show that the nonperturbative decay of ultralight scalars into Abelian gauge bosons, recently proposed as a possible solution to the Hubble tension, produces a stochastic background of gravitational waves which is constrained by the cosmic microwave background. We simulate the full nonlinear dynamics of resonant dark photon production and the associated gravitational-wave production, finding the signals to exceed constraints for the entire parameter space we consider. Our findings suggest that gravitational-wave production from the decay of early dark energy may provide a unique probe of these models.

79 ASTRONOMY AND ASTROPHYSICS↗

Utilizing cosmic-ray positron and electron observations to probe the averaged properties of Milky Way pulsars

Pulsars have long been studied in the electromagnetic spectrum. Their environments are rich in high-energy cosmic-ray electrons and positrons likely enriching the interstellar medium with such particles. In this work we use recent cosmic-ray observations from the AMS-02, CALET and DAMPE collaborations to study the averaged properties of the local Milky Way pulsar population. We perform simulations of the local Milky Way pulsar population, for interstellar medium assumptions in agreement with a range of cosmic-ray nuclei measurements. Each such simulation contains $\sim 10^{4}$ pulsars of unique age, location, initial spin-down power and cosmic-ray electron/positron spectra. We produce more than $7 \times 10^3$ such Milky Way pulsar simulations. Further, we account for and study i) the pulsars' birth rates and the stochastic nature of their birth, ii) their initial spin-down power distribution, iii) their time evolution in terms of their braking index and characteristic spin-down timescale, iv) the fraction of spin-down power going to cosmic-ray electrons and positrons and v) their propagation through the interstellar medium and the Heliosphere. We find that pulsars of ages $\sim 10^{5}-10^{7}$ yr, have a braking index that on average has to be 3 or larger. Given that electromagnetic spectrum observations of young pulsars find braking indices lower than 3, our work provides strong hints that pulsars' braking index increases on average as they age, allowing them to retain some of their rotational energy. Moreover, we find that pulsars have relatively uniform properties as sources of cosmic-ray electrons and positrons in terms of the spectra they produce and likely release $O(10\%)$ of their rotational energy to cosmic rays in the ISM. Finally, we find at $\simeq$12 GeV positrons a spectral feature that suggests a new subpopulation of positron sources contributing at these energies.

79 ASTRONOMY AND ASTROPHYSICS↗

Potential Impacts of Assimilating All-sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection

Geostationary infrared satellite observations are spatially dense [< 1/(20 km)2] and temporally frequent (< 1/hour). These suggest the possibility of using these observations to constrain sub-synoptic features over data sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than EnSRF algorithm.

Chan, Man-Yau↗

Environmental and Economic Assessments and Uncertainties of Multiple Lignocellulosic Biomass Utilization for Bioenergy Products: Case Studies

Life-cycle assessment (LCA) and techno-economic analysis (TEA) were applied to assess the economic feasibility and environmental benefits of utilizing multiple biomass feedstocks for bioenergy products under three different technological pathways with consideration of uncertainties. Three cases were studied for the production of pellets, biomass-based electricity, and pyrolysis bio-oil. A Monte Carlo simulation was used to examine the uncertainties of fossil energy consumption, bioenergy conversion efficiency, stochastic production rate, etc. The cradle-to-gate LCA results showed that pellet production had the lowest greenhouse gas (GHG) emissions, water and fossil fuels consumption (8.29 kg CO2 eq (equivalent), 0.46 kg, and 105.42 MJ, respectively). The conversion process presented a greater environmental impact for all three bioenergy products. When producing 46,929 Mg of pellets, 223,380 MWh of electricity, and 78,000 barrels of pyrolysis oil, the net present values (NPV) indicated that only pellet and electricity production were profitable with NPVs of $1.20 million for pellets, and $5.59 million for biopower. Uncertainty analysis indicated that pellet production showed the highest uncertainty in GHG emission, and bio-oil production had the least uncertainty in GHG emission but had risks producing greater-than-normal amounts of GHG. Biopower production had the highest probability to be a profitable investment with 85.23%.

09 BIOMASS FUELS↗

Two stochastic models useful in petroleum exploration

A model of the petroleum exploration process that tests empirically the hypothesis that at an early stage in the exploration of a basin, the process behaves like sampling without replacement is proposed along with a model of the spatial distribution of petroleum reserviors that conforms to observed facts. In developing the model of discovery, the following topics are discussed: probabilitistic proportionality, likelihood function, and maximum likelihood estimation. In addition, the spatial model is described, which is defined as a stochastic process generating values of a sequence or random variables in a way that simulates the frequency distribution of areal extent, the geographic location, and shape of oil deposits

Kaufman, G. M.↗

A simulation of remote sensor systems and data processing algorithms for spectral feature classification

A computational model of the deterministic and stochastic processes involved in multispectral remote sensing was designed to evaluate the performance of sensor systems and data processing algorithms for spectral feature classification. Accuracy in distinguishing between categories of surfaces or between specific types is developed as a means to compare sensor systems and data processing algorithms. The model allows studies to be made of the effects of variability of the atmosphere and of surface reflectance, as well as the effects of channel selection and sensor noise. Examples of these effects are shown.

Arduini, R. F.↗

Structural considerations for a software life cycle dynamic simulation model

This paper presents the results of a preliminary study into the prospects for simulating the software implementation and maintenance life cycle process, with the aim of producing a computerized tool for use by management and software engineering personnel in project planning, tradeoff studies involving product, environmental, situational, and technological factors, and training. The approach taken is the modular application of a 'flow of resource' concept to the systems dynamics simulation modeling technique. The software life cycle process is represented as a number of stochastic, time-varying, interacting work tasks that each achieves one of the project milestones. Each task is characterized by the item produced, the personnel applied, and the budgetary profile.

Tausworthe, R. C.↗

Estimation of optical flow in airborne electro-optical sensors by stochastic approximation

The essence of motion or range estimation by passive electrooptical means is the ability to determine the correspondence of picture elements in pairs of image frames and to estimate their coordinates and their disparity (relative shifts) in the image plane of an electrooptical imaging sensor. The disparity can be in successive frames due to self-motion or in simultaneous frames of a stereo pair. A key issue is to provide these estimates on-line. This paper describes the theoretical background of such an interframe shift estimator. It is based on a stochastic gradient algorithm, specifically implementing a form of stochastic approximation, which can achieve rapid convergence of the shift estimate. Analytical and numerical simulation examples for random texture and isolated features validate the feasibility and the effectiveness of the estimator.

Merhav, S. J.↗

Stochastic acceleration in the transrelativistic region and pion production in solar flares

The stochastic Fermi acceleration spectrum in the transrelativistic region obtained from a Monte-Carlo simulation for an energy-independent alpha(T) is much harder than the extension of the nonrelativistic analytic spectrum to this energy range for the same alpha(T). The latter, with alpha(T) = 0.043, was used to model the pion and nuclear line emissions for the impulsive phase of the 3 Jun. 1982 flare, as well as the 2.223 MeV emission from this flare. We find that the ratios of these three emissions for the Monte-Carlo spectrum with alpha(T) = 0.028 are essentially the same as those for the analytical spectrum with alpha(T) = 0.043. We also find that the acceleration time from approximately 30 MeV to approximately 1 GeV is less than or approximately = 10 s, consistent with the observations of the 3 Jun. 1982 flare.

Miller, James A.↗

Particle acceleration in cosmic plasmas; Proceedings of the Workshop, Bartol Research Inst., Newark, DE, Dec. 4-6, 1991

The present volume on particle acceleration in cosmic plasmas discusses transport theory, particle acceleration in the sun, heliosphere, and galaxy, particle acceleration at nonrelativistic shocks, and stochastic particle acceleration. Attention is given to particle acceleration in relativistic flows and shocks, simulations, composition and sources of high-energy cosmic rays, electron acceleration, and observation priorities and perspectives. Topics addressed include particle acceleration in the magnetosphere, particle transport from a turbulence perspective, numerical simulations of time-dependent cosmic ray mediated shocks, and solar flare plasma. Also discussed are relativistic shock waves and the excitation of plerions, ion acceleration at collisionless shock interactions, ultrahigh-energy cosmic rays from Fanaroff Riley, and constraints on electron acceleration in the Crab Nebula.

Zank, G. P.↗

Direct Numerical Simulation of Turbulent Condensation in Clouds

In this brief, we investigate the turbulent condensation of a population of droplets by means of a direct numerical simulation. To that end, a coupled Navier-Stokes/Lagrangian solver is used where each particle is tracked and its growth by water vapor condensation is monitored exactly. The main goals of the study are to find out whether turbulence broadens the droplet size distribution, as observed in in situ measurements. The second issue is to understand if and for how long a correlation between the droplet radius and the local supersaturation exists for the purpose of modeling sub-grid scale microphysics in cloud-resolving codes. This brief is organized as follows. In Section 2 the governing equations are presented, including the droplet condensation model. The implementation of the forcing procedure is described in Section 3. The simulation results are presented in Section 4 together with a sketch of a simple stochastic model for turbulent condensation. Conclusions and the main outcomes of the study are given in Section 5.

Shariff, K.↗

Tectonic-Climate Interactions in Action Orogenic Belts: Quantification of Dynamic Topography with SRTM data

This project was undertaken to examine the approach to steady state in collisional mountain belts. Although the primary thrust of this grant was to look at larger collisional mountain belts, such as the Himalaya, the Tien Shan, and Southern Alps, we began by looking at smaller structures represented by growing and propagating folds. Like ranges that are evolving toward a topographic steady state, these folds undergo a series of morphologic changes as they are progressively uplifted and eroded. We wanted to document the nature of these changes and to try to discern some of the underlying controls on them. We initially focused on the Wheeler Ridge anticline in southern California. Subsequently, we progressed to looking at the topographic development and the effects of differential uplift and glaciation on the Kyrgyz Range in the northern Tien Shan. This range is unusual inasmuch as it is transformed along its length from a simple uplift with a largely preserved Mesozoic erosion surface arching across it to a highly dissected and heavily glaciated uplift in the region where uplift has been sustained at higher rates over longer intervals. In efforts to understand the distribution of erosion rates at 10(exp 3) - 10(exp 5) year time scales, cosmogenic radionuclide (CRN) concentrations have been gaining increasingly widespread usage (Brown et al., 1995; Riebe et al., 2004; Riebe et al., 2001; Vance et al., 2003). Most studies to date, however, have been conducted in slowly eroding ranges. In rapidly eroding mountains where landslides deliver most of the sediments to the rivers, we hypothesized that CRN concentrations could be highly perturbed by the stochastic processes of landsliding. Therefore, we undertook the development of a numerical model that simulated the effects of both landsliding and grain-by-grain attrition within fluvial catchments. This modeling effort has shown the effects of catchment size and erosion rate on CRN concentrations and allows a prediction of where to sample to obtain the optimal erosion rate estimates using CRN techniques. Finally, we developed computational techniques to operate on DEMs to extract useful information that would enable quantification of climate-erosion interactions. In particular, we worked on rapid techniques to define catchments of any given range of sizes, to extract channel gradients, to combine precipitation information to calculate discharge, and to utilize various stream-power models to determine the erosional energy within any given catchment within a transect. We briefly describe results from Wheeler Ridge, the Kyrgyz Range, the Nepal Himalaya, and our numerical modeling.

Burbank, Douglas W.↗

Quantum Decision-Maker

A quantum device simulating human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and a classical neural nets describing evolution of probabilities of these processes which represent the mental dynamics.

quantum↗

Process-oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction

Weather forecasting nowadays often requires some estimation of uncertainties associated with the output of meteorological models, in order to better inform decision making, especially in the context of intense weather events. Ensemble prediction systems provide such information through sets of possible scenarios which are designed to represent various uncertainty sources, including model uncertainties. A wide variety of methods have been proposed to estimate model uncertainties, among which perturbation methods targeting uncertain processes are a promising research field. Here, we focus on the representation of small-scale variability by process-oriented perturbation schemes applied to two key physical processes, namely turbulence and shallow convection. The perturbations are applied to a single-column version of the convection-permitting AROME model, in three idealized boundary-layer cases. Large-eddy simulations (LESs) of the same cases serve as a reference for the subgrid variability that has to be represented, and the results are also compared to those given by the Stochastically Perturbed Parametrization Tendencies (SPPT) method, which is a method commonly used by weather forecast centres to represent model uncertainty. The spread produced by our process-oriented perturbations of turbulence and shallow convection does not represent all the small-scale variability implied by the LESs for temperature and humidity. However, it is of a similar order of magnitude for the wind, thanks to perturbations generated by the stochastic turbulence scheme. The dispersion is structurally different from what is obtained with SPPT. It is non-negligible in the lower levels, where SPPT perturbations are usually suppressed because of numerical instabilities, indicating a possible complementarity between the schemes.

54 ENVIRONMENTAL SCIENCES↗

A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure

In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stochastic dynamical system where first-principles electromechanical dynamics are excited by small Gaussian disturbances in demand and generation around a specified operating point. In this context, a single line failure is interpreted in a large deviation context as a first escape event across a surface in phase space defined by line security constraints. The resulting system of stochastic differential equations admits a transverse decomposition of the drift, which leads to considerable simplification in evaluating the quasipotential (rate function) and, consequently, computation of exit rates. Tractable expressions for the rate of transmission line failure in a restricted network are derived from large deviation theory arguments and validated against numerical simulations. Extensions to realistic settings are considered, and individual line failure models are aggregated into a Markov model of cascading failure inspired by chemical kinetics. Cascades are generated by traversing a graph composed of weighted edges representing transitions to degraded network topologies. Numerical results indicate that the Markov model can produce cascades with qualitative power-law properties similar to those observed in empirical cascades.

Kinetic Monte Carlo, rare events, Cascading Failur↗

Social network structure and the spread of complex contagions from a population genetics perspective

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a non-linear function of the fraction of the individuals’ affected neighbors, such a spreading process becomes a “complex contagion”. This non-linearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g. for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g. for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary non-linear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of ”selection” (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.

59 BASIC BIOLOGICAL SCIENCES↗