530 Physik
Refine
Year of publication
Document Type
- Article (970) (remove)
Keywords
- diffusion (25)
- anomalous diffusion (23)
- gamma rays: general (20)
- ISM: supernova remnants (12)
- cosmic rays (12)
- stars: massive (11)
- organic solar cells (9)
- stars: atmospheres (9)
- astroparticle physics (8)
- perovskite solar cells (8)
Institute
- Institut für Physik und Astronomie (893)
- Institut für Chemie (37)
- Institut für Mathematik (15)
- Extern (8)
- Institut für Geowissenschaften (6)
- Institut für Umweltwissenschaften und Geographie (6)
- Mathematisch-Naturwissenschaftliche Fakultät (5)
- Institut für Biochemie und Biologie (4)
- Fachgruppe Politik- & Verwaltungswissenschaft (3)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (3)
- Department Linguistik (2)
- Institut für Informatik und Computational Science (2)
- Sozialwissenschaften (2)
- Hasso-Plattner-Institut für Digital Engineering GmbH (1)
- Institut für Ernährungswissenschaft (1)
- Potsdam Institute for Climate Impact Research (PIK) e. V. (1)
- UP Transfer (1)
According to established understanding, deep-water formation in the North Atlantic and Southern Ocean keeps the deep ocean cold, counter-acting the downward mixing of heat from the warmer surface waters in the bulk of the world ocean. Therefore, periods of strong Atlantic meridional overturning circulation (AMOC) are expected to coincide with cooling of the deep ocean and warming of the surface waters. It has recently been proposed that this relation may have reversed due to global warming, and that during the past decades a strong AMOC coincides with warming of the deep ocean and relative cooling of the surface, by transporting increasingly warmer waters downward. Here we present multiple lines of evidence, including a statistical evaluation of the observed global mean temperature, ocean heat content, and different AMOC proxies, that lead to the opposite conclusion: even during the current ongoing global temperature rise a strong AMOC warms the surface. The observed weakening of the AMOC has therefore delayed global surface warming rather than enhancing it.
Social Media Abstract:
The overturning circulation in the Atlantic Ocean has weakened in response to global warming, as predicted by climate models. Since it plays an important role in transporting heat, nutrients and carbon, a slowdown will affect global climate processes and the global mean temperature. Scientists have questioned whether this slowdown has worked to cool or warm global surface temperatures. This study analyses the overturning strength and global mean temperature evolution of the past decades and shows that a slowdown acts to reduce the global mean temperature. This is because a slower overturning means less water sinks into the deep ocean in the subpolar North Atlantic. As the surface waters are cold there, the sinking normally cools the deep ocean and thereby indirectly warms the surface, thus less sinking implies less surface warming and has a cooling effect. For the foreseeable future, this means that the slowing of the overturning will likely continue to slightly reduce the effect of the general warming due to increasing greenhouse gas concentrations.
Data-driven prediction and physics-agnostic machine-learning methods have attracted increased interest in recent years achieving forecast horizons going well beyond those to be expected for chaotic dynamical systems. In a separate strand of research data-assimilation has been successfully used to optimally combine forecast models and their inherent uncertainty with incoming noisy observations. The key idea in our work here is to achieve increased forecast capabilities by judiciously combining machine-learning algorithms and data assimilation. We combine the physics-agnostic data -driven approach of random feature maps as a forecast model within an ensemble Kalman filter data assimilation procedure. The machine-learning model is learned sequentially by incorporating incoming noisy observations. We show that the obtained forecast model has remarkably good forecast skill while being computationally cheap once trained. Going beyond the task of forecasting, we show that our method can be used to generate reliable ensembles for probabilistic forecasting as well as to learn effective model closure in multi-scale systems. (C) 2021 Elsevier B.V. All rights reserved.
To analyze stochastic processes, one often uses integral transform (Fourier and Laplace) methods. However, for the time-space coupled cases, e.g. the Levy walk, sometimes the integral transform method may fail. Here we provide a Hermite polynomial expansion approach, being complementary to the integral transform method, to the Levy walk. Two approaches are compared for some already known results. We also consider the generalized Levy walk with parameter dependent velocity. Namely, we consider the Levy walk with velocity which depends on the walking length or on the duration of each step. Some interesting features of the generalized Levy walk are observed, including the special shapes of the probability density function, the first passage time distributions, and various diffusive behaviors of the mean squared displacement.
Wire Arc Additive Manufacturing (WAAM) features high deposition rates and, thus, allows production of large components that are relevant for aerospace applications. However, a lot of aerospace parts are currently produced by forging or machining alone to ensure fast production and to obtain good mechanical properties; the use of these conventional process routes causes high tooling and material costs. A hybrid approach (a combination of forging and WAAM) allows making production more efficient. In this fashion, further structural or functional features can be built in any direction without using additional tools for every part. By using a combination of forging basic geometries with one tool set and adding the functional features by means of WAAM, the tool costs and material waste can be reduced compared to either completely forged or machined parts. One of the factors influencing the structural integrity of additively manufactured parts are (high) residual stresses, generated during the build process. In this study, the triaxial residual stress profiles in a hybrid WAAM part are reported, as determined by neutron diffraction. The analysis is complemented by microstructural investigations, showing a gradient of microstructure (shape and size of grains) along the part height. The highest residual stresses were found in the transition zone (between WAAM and forged part). The total stress range showed to be lower than expected for WAAM components. This could be explained by the thermal history of the component.
Bifurcations in rotating spherical shell convection under the influence of differential rotation
(2021)
The bifurcations of thermal convection in a rotating spherical shell heated from the inner sphere and driven by the buoyancy of a central gravity field are studied numerically. This model of spherical Rayleigh-Benard convection describes large-scale convection in planets and in the outer zones of celestial bodies. In this work, the influence of an additionally imposed differential rotation of the inner sphere with respect to the outer one on the heat transfer and, more generally, on the whole bifurcation structure is investigated. In addition to numerical simulations, path-following techniques are applied in order to compute both stable and unstable solution branches. The dynamics and the heat transfer are essentially determined by a global bifurcation, which we have identified as a homoclinic bifurcation that consists of a collision of a stable modulated rotating with an unstable rotating wave.
With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.
This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise partly from the assumption that the models have a block-diagonal structure, with each block corresponding to an ARMA process, that allows the reconstruction of independent source components from linear mixtures, and partly from the need to keep models identifiable. The first stage of parameter fitting is performed by the expectation maximisation (EM) algorithm. Due to the identifiability constraint, a subset of the diagonal elements of the dynamical noise covariance matrix needs to be constrained to fixed values (usually unity). For this kind of constraints, so far, no closed-form update rules were available. We present new update rules for this situation, both for updating the dynamical noise covariance matrix directly and for updating a matrix square-root of this matrix. The practical applicability of the proposed algorithm is demonstrated by a low-dimensional simulation example. The behaviour of the EM algorithm, as observed in this example, illustrates the well-known fact that in practical applications, the EM algorithm should be combined with a different algorithm for numerical optimisation, such as a quasi-Newton algorithm.
We performed numerical simulations with the Kuramoto model and experiments with oscillatory nickel electrodissolution to explore the dynamical features of the transients from random initial conditions to a fully synchronized (one-cluster) state. The numerical simulations revealed that certain networks (e.g., globally coupled or dense Erdos-Renyi random networks) showed relatively simple behavior with monotonic increase of the Kuramoto order parameter from the random initial condition to the fully synchronized state and that the transient times exhibited a unimodal distribution. However, some modular networks with bridge elements were identified which exhibited non-monotonic variation of the order parameter with local maximum and/or minimum. In these networks, the histogram of the transients times became bimodal and the mean transient time scaled well with inverse of the magnitude of the second largest eigenvalue of the network Laplacian matrix. The non-monotonic transients increase the relative standard deviations from about 0.3 to 0.5, i.e., the transient times became more diverse. The non-monotonic transients are related to generation of phase patterns where the modules are synchronized but approximately anti-phase to each other. The predictions of the numerical simulations were demonstrated in a population of coupled oscillatory electrochemical reactions in global, modular, and irregular tree networks. The findings clarify the role of network structure in generation of complex transients that can, for example, play a role in intermittent desynchronization of the circadian clock due to external cues or in deep brain stimulations where long transients are required after a desynchronization stimulus.
For hybrid organic-inorganic as well as all-inorganic lead halide perovskites a Rashba effect has been invoked to explain the high efficiency in energy conversion by prohibiting direct recombination. Both a bulk and surface Rashba effect have been predicted. In the valence band of methylammonium (MA) lead bromide a Rashba effect has been reported by angle-resolved photoemission and circular dichroism with giant values of 7-11 eV angstrom. We present band dispersion measurements of MAPbBr(3) and spin-resolved photoemission of CsPbBr3 to show that a large Rashba effect detectable by photoemission or circular dichroism does not exist and cannot be the origin of the high effciency.
Employing extensive Monte Carlo computer simulations, we investigate in detail the properties of multichain adsorption of charged flexible polyelectrolytes (PEs) onto oppositely charged spherical nanoparticles (SNPs). We quantify the conditions of critical adsorption-the phase-separation curve between the adsorbed and desorbed states of the PEs-as a function of the SNP surface-charge density and the concentration of added salt. We study the degree of fluctuations of the PE-SNP electrostatic binding energy, which we use to quantify the emergence of the phase subtransitions, including a series of partially adsorbed PE configurations. We demonstrate how the phase-separation adsorption-desorption boundary shifts and splits into multiple subtransitions at low-salt conditions, thereby generalizing and extending the results for critical adsorption of a single PE onto the SNP. The current findings are relevant for finite concentrations of PEs around the attracting SNP, such as the conditions for PE adsorption onto globular proteins carrying opposite electric charges.
Moving spiral wave chimeras
(2021)
We consider a two-dimensional array of heterogeneous nonlocally coupled phase oscillators on a flat torus and study the bound states of two counter-rotating spiral chimeras, shortly two-core spiral chimeras, observed in this system. In contrast to other known spiral chimeras with motionless incoherent cores, the two-core spiral chimeras typically show a drift motion. Due to this drift, their incoherent cores become spatially modulated and develop specific fingerprint patterns of varying synchrony levels. In the continuum limit of infinitely many oscillators, the two-core spiral chimeras can be studied using the Ott-Antonsen equation. Numerical analysis of this equation allows us to reveal the stability region of different spiral chimeras, which we group into three main classes-symmetric, asymmetric, and meandering spiral chimeras.
Classical option pricing schemes assume that the value of a financial asset follows a geometric Brownian motion (GBM). However, a growing body of studies suggest that a simple GBM trajectory is not an adequate representation for asset dynamics, due to irregularities found when comparing its properties with empirical distributions. As a solution, we investigate a generalisation of GBM where the introduction of a memory kernel critically determines the behaviour of the stochastic process. We find the general expressions for the moments, log-moments, and the expectation of the periodic log returns, and then obtain the corresponding probability density functions using the subordination approach. Particularly, we consider subdiffusive GBM (sGBM), tempered sGBM, a mix of GBM and sGBM, and a mix of sGBMs. We utilise the resulting generalised GBM (gGBM) in order to examine the empirical performance of a selected group of kernels in the pricing of European call options. Our results indicate that the performance of a kernel ultimately depends on the maturity of the option and its moneyness.
Human migration is often studied using gravity models. These models, however, have known limitations, including analytic inconsistencies and a dependence on empirical data to calibrate multiple parameters for the region of interest. Overcoming these limitations, the radiation model has been proposed as an alternative, universal approach to predicting different forms of human mobility, but has not been adopted for studying migration. Here we show, using data on within-country migration from the USA and Mexico, that the radiation model systematically underpredicts long-range moves, while the traditional gravity model performs well for large distances. The universal opportunity model, an extension of the radiation model, shows an improved fit of long-range moves compared to the original radiation model, but at the cost of introducing two additional parameters. We propose a more parsimonious extension of the radiation model that introduces a single parameter. We demonstrate that it fits the data over the full distance spectrum and also-unlike the universal opportunity model-preserves the analytical property of the original radiation model of being equivalent to a gravity model in the limit of a uniform population distribution.
We report on the adsorption kinetics of azoben-zene-containing surfactants on solid surfaces of different hydrophobicity. The understanding of this processes is of great importance for many interfacial phenomena that can be actuated and triggered by light, since the surfactant molecules contain a photoresponsive azobenzene group in their hydrophobic tail. Three surfactant types are studied, differing in the spacer connecting the headgroup and the azobenzene unit by between 6 and 10 CH2 groups. Under irradiation with light of a suitable wavelength, the azobenzene undergoes reversible photoisomerization between two states, a nonpolar trans-state and a highly polar cis-state. Consequently, the surfactant molecule changes its hydrophobicity and thus affinity to a surface depending on the photoisomerization state of the azobenzene. The adsorption behavior on hydrophilic (glass) and hydrophobic (TeflonAF) surfaces is analyzed using quartz crystal microbalance with dissipation (QCM-D) and zeta-potential measurements. At equilibrium, the adsorbed surfactant amount is almost twice as large on glass compared to TeflonAF for both isomers. The adsorption rate for the trans-isomers on both surfaces is similar, but the desorption rate of the trans-isomers is faster at the glass-water interface than at the Teflon-water interface. This result demonstrates that the trans-isomers have higher affinity for the glass surface, so the trans-to-cis ratios on glass and TeflonAF are 80/1 and 2/1, respectively, with similar trends for all three surfactant types.
As VLSI design moves into ultra-deep-submicron technologies, timing margins added to the clock period are mandatory, to ensure correct circuit behavior under worst-case conditions. Timing resilient architectures emerged as a promising solution to alleviate these worst-case timing margins. These architectures allow improving system performance and reducing energy consumption. Asynchronous systems, on the other hand, have the potential to improve energy efficiency and performance. Blade is an asynchronous timing resilient template that leverages the advantages of both asynchronous and timing resilient techniques. However, Blade still presents challenges regarding its testability, which hinders its commercial or large-scale application. This paper demonstrates that scan chains can be prohibitive for Blade due to their high silicon costs., which can reach more than 100%. Then, it proposes an alternative test approach that allows concurrent testing, stuck-at, and delay testing. The test approach is based on the reuse the Blade features to provide testability, with silicon area overheads between 4 and 7%.
We use quantum chemical cluster models together with constrained density STM Ph CI functional theory (DFT) and ab initio molecular dynamics (AIMD) for open system to simulate tip and rationalize nonlocal scanning tunneling microscope (STM) manipulation experiments for Philh ci chlorobenzene (PhCl) on a Si(111)-7 X 7 surface. We consider three different processes, namely, the electron-induced dissociation of the carbon-chlorine bond for physisorbed PhCl molecules at low temperatures and the electron- or hole-induced desorption of chemisorbed PhCl at 300 K. All processes can be induced nonlocally, i.e., up to several nanometers (nm) away from the injection site, in STM experiments. We rationalize and explain the experimental findings regarding the STM-induced dissociation using constrained DFT. The coupling of STM-induced ion resonances to nuclear degrees of freedom is simulated with AIMD using the Gadzuk averaging approach for open systems. From this data, we predict a 4 fs lifetime for the cationic resonance. For the anion model, desorption could not be observed. In addition, the same cluster models are used for transition-state theory calculations, which are compared to and validated against time-lapse STM experiments.
We present a supervised learning method to learn the propagator map of a dynamical system from partial and noisy observations. In our computationally cheap and easy-to-implement framework, a neural network consisting of random feature maps is trained sequentially by incoming observations within a data assimilation procedure. By employing Takens's embedding theorem, the network is trained on delay coordinates. We show that the combination of random feature maps and data assimilation, called RAFDA, outperforms standard random feature maps for which the dynamics is learned using batch data.
We investigate the transition from incoherence to global collective motion in a three-dimensional swarming model of agents with helical trajectories, subject to noise and global coupling. Without noise this model was recently proposed as a generalization of the Kuramoto model and it was found that alignment of the velocities occurs discontinuously for arbitrarily small attractive coupling. Adding noise to the system resolves this singular limit and leads to a continuous transition, either to a directed collective motion or to center-of-mass rotations.
The Rarita-Schwinger operator is the twisted Dirac operator restricted to 3/2-spinors. Rarita-Schwinger fields are solutions of this operator which are in addition divergence-free. This is an overdetermined problem and solutions are rare; it is even more unexpected for there to be large dimensional spaces of solutions. In this paper we prove the existence of a sequence of compact manifolds in any given dimension greater than or equal to 4 for which the dimension of the space of Rarita-Schwinger fields tends to infinity. These manifolds are either simply connected Kahler-Einstein spin with negative Einstein constant, or products of such spaces with flat tori. Moreover, we construct Calabi-Yau manifolds of even complex dimension with more linearly independent Rarita-Schwinger fields than flat tori of the same dimension.
A double-layer transcrystalline polypropylene (PP) film with a flat central interface layer between its two transcrystalline layers is obtained by recrystallization from the melt between two polytetrafluoroethylene (PTFE) surfaces on both sides of the PP film. Its electret properties are studied and compared with those of a single-layer transcrystalline PP film re-crystallized in contact with only one PTFE surface. Within experimental uncertainty, the two types of transcrystalline films exhibit the same thermal properties and crystallinities. After thermal poling, however, two hetero-charge layers of opposite polarity are found on the internal interfaces of the double-layer transcrystalline films and may together be considered as micrometer-sized dipoles. The unexpected phenomenon does not occur in single-layer transcrystalline samples without a central interface layer, suggesting that the interfaces between the transcrystalline layers and the micrometer-thick central interface layer may be the origin of deeper traps rather than the crystalline structures in the transcrystallites or the spherulites. The origin of the interfacial charges was also studied by means of an injection-blocking charging method, which revealed that intrinsic charge carriers introduced during recrystallization are most likely responsible for the interfacial charges. It is fascinating that a material as familiar as PP can exhibit such intriguing properties with a special bipolar space-charge polarization across the central interface layer after quasi-epitaxial surface moulding into a double-layer transcrystalline form. In addition to applications in electret (micro-)devices for electro-mechanical transduction, the highly ordered structures may also be employed as a new paradigm for studying charge storage and transport in polymer electrets and in dielectrics for DC electrical insulation.