Universität Stuttgart
Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1
Browse
Search Results
Item Open Access The natural breakup length of a steady capillary jet : application to serial femtosecond crystallography(2021) Gañán-Calvo, Alfonso M.; Chapman, Henry N.; Heymann, Michael; Wiedorn, Max O.; Knoska, Juraj; Gañán-Riesco, Braulio; López-Herrera, José M.; Cruz-Mazo, Francisco; Herrada, Miguel A.; Montanero, José M.; Bajt, SašaOne of the most successful ways to introduce samples in Serial Femtosecond Crystallography has been the use of microscopic capillary liquid jets produced by gas flow focusing, whose length-to-diameter ratio and velocity are essential to fulfill the requirements of the high pulse rates of current XFELs. In this work, we demonstrate the validity of a classical scaling law with two universal constants to calculate that length as a function of the liquid properties and operating conditions. These constants are determined by fitting the scaling law to a large set of experimental and numerical measurements, including previously published data. Both the experimental and numerical jet lengths conform remarkably well to the proposed scaling law. We show that, while a capillary jet is a globally unstable system to linear perturbations above a critical length, its actual and shorter long-term average intact length is determined by the nonlinear perturbations coming from the jet breakup itself. Therefore, this length is determined solely by the properties of the liquid, the average velocity of the liquid and the flow rate expelled. This confirms the very early observations from Smith and Moss 1917, Proc R Soc Lond A Math Phys Eng, 93, 373, to McCarthy and Molloy 1974, Chem Eng J, 7, 1, among others, while it contrasts with the classical conception of temporal stability that attributes the natural breakup length to the jet birth conditions in the ejector or small interactions with the environment.Item Open Access Do not blame the media! : the role of politicians and parties in fragmenting online political debate(2021) Heiberger, Raphael; Majó-Vázquez, Silvia; Castro Herrero, Laia; Nielsen, Rasmus K.; Esser, FrankDemocratic politics builds on both clear differences and shared common ground. While the rise of digital media may have enabled more differences to be articulated, common ground is often seen as threatened by fragmentation of political debate, which some see as driven by news media. The relative importance of political actors (parties and politicians) in driving fragmentation has received less attention. In this paper, we compare how news media and political actors contribute to the fragmentation of online political debate on the basis of analysis of almost half a million election-related tweets collected during the 2017 French, German, and U.K. national elections. We employ a structural topic model to reduce online political debate to networks of topic overlap. Across the three countries with different political and media systems, we find news media are by far the most important actors in terms of creating and maintaining a common space of online political debate on Twitter. Our results also show that political actors, with some variation from country to country, contribute more to fragmentation as they focus on different topics while articulating clear differences. These findings underline the importance of complementing structural analysis of the rise of digital and social media with analysis of how important elite actors like news media and political parties/candidates use these media in different ways. Overall, we show how at least on Twitter, across three different countries with different media systems and political systems, news media create connection that contributes to commonality while political actors lay out clear differences that drive fragmentation.Item Open Access Potential analysis of E-scooters for commuting paths(2021) Edel, Fabian; Wassmer, Simon; Kern, MiraThe mobility needs of society are constantly increasing, resulting in congested urban areas. New mobility concepts such as e-scooters can help to reduce traffic. In particular, commuting paths, which generally remain within a specific distance, are short and manageable via an intermodal travel chain. In combination with public transport, commuting paths could be beneficial. To evaluate the potential of e-scooters used with commuting paths, a literature research focusing on mobility behavior and characteristics was conducted. In addition, an end-user survey was used to identify the ecological and economical potential for typical work routes. The research results indicate that both the mobility preferences of the users, e.g., acceptance of intermodal travel, and the technical specification of e-scooters, e.g., speed and range, meet the needs of commuting. The assessment of typical work routes shows that the use of e-scooters for the first and last mile, in combination with public transport, is highly beneficial. Furthermore, e-scooters have the potential to provide individual advantages in the areas of travel time and costs. From an ecological perspective, CO2-equivalent emissions may also be reduced for some users depending on the substituted modes.Item Open Access Linking qualitative scenarios with quantitative energy models: knowledge integration in different methodological designs(2021) Prehofer, Sigrid; Kosow, Hannah; Naegler, Tobias; Pregger, Thomas; Vögele, Stefan; Weimer-Jehle, WolfgangLinking qualitative scenarios with quantitative models is a common approach to integrate assumptions on possible future societal contexts into modeling. But reflection on how and to what degree knowledge is effectively integrated during this endeavor does not generally take place. In this paper, we reflect on the performance of a specific hybrid scenario approach (qualitative Cross-Impact Balance analysis, CIB, linked with quantitative energy models) concerning knowledge integration through eleven different process steps. In order to guide the scenario community in applying this approach, we reflect on general methodological features as well as different design options. We conceptualize different forms of interdisciplinary knowledge integration (compiling, combining and synthesizing) and analyze how and to what degree knowledge about society and uncertainty are integrated into scenario process and products. In addition, we discuss trade-offs regarding design choices and forms of knowledge integration. On the basis of three case studies we identify two general designs of linking which build on each other (basic and extended design) and which differ in essence regarding the balance of power between the CIB and the energy modeling. Ex-post assessment of the form of interdisciplinary knowledge integration in each step revealed that specific method properties of CIB as well as the interaction with additional quantitative as well as specific qualitative methods foster distinct forms of knowledge integration. The specific roles assigned to CIB in the hybrid scenario process can also influence the form of knowledge integration. In this study, we use a joint process scheme linking qualitative context scenarios with energy modeling. By applying our conceptualization of different forms of knowledge integration we analyze the designs´ respective potential for and respective effects on knowledge integration. Consequently, our findings can give guidance to those who are designing their own hybrid scenario processes. As this is an explorative study, it would be useful to further test our hypotheses in different hybrid scenario designs. Finally, we note that at some points in the process a more precise differentiation of three forms of knowledge integration would have been useful and propose to further differentiate and detail them in future research.Item Open Access Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations(2021) Miksch, April M.; Morawietz, Tobias; Kästner, Johannes; Urban, Alexander; Artrith, NongnuchRecent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (a) data collection, (b) model selection, (c) training and validation, and (d) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies.Item Open Access Increasing low-temperature toughness of 09Mn2Si steel through lamellar structuring by helical rolling(2021) Panin, Sergey; Vlasov, Ilya; Moiseenko, Dmitry; Maksimov, Pavel; Maruschak, Pavlo; Yakovlev, Alexander; Gomorova, Julia; Mishin, Ivan; Schmauder, SiegfriedThe aim of the paper was to investigate the helical rolling parameters (a number of passes) for the microstructural modification and the low-temperature impact toughness improvement of the 09Mn2Si High Strength Low-Alloyed (HSLA) steel. In order to achieve this purpose, work spent to crack initiation and propagation was analyzed and compared with patterns of fracture surfaces. The microstructure and impact toughness values were presented in the temperature range from +20 to -70°C. Also, the fracture mechanisms in individual regions on the fracture surfaces were discussed. In addition, a methodology for computer simulation of the process was developed and implemented within the framework of the excitable cellular automata method and its integration with the kinetic theory of fracture. Finally, a theoretical analysis of the effect of grain shapes and orientations on the strain response patterns of a certain meso-volume simulating the material after the helical rolling was carried out.Item Open Access Quantum Fourier transform for nanoscale quantum sensing(2021) Vorobyov, Vadim; Zaiser, Sebastian; Abt, Nikolas; Meinel, Jonas; Dasari, Durga; Neumann, Philipp; Wrachtrup, JörgThe quantum Fourier transformation (QFT) is a key building block for a whole wealth of quantum algorithms. Despite its proven efficiency, only a few proof-of-principle demonstrations have been reported. Here we utilize QFT to enhance the performance of a quantum sensor. We implement the QFT algorithm in a hybrid quantum register consisting of a nitrogen-vacancy (NV) center electron spin and three nuclear spins. The QFT runs on the nuclear spins and serves to process the sensor - i.e., the NV electron spin signal. Specifically, we show the application of QFT for correlation spectroscopy, where the long correlation time benefits the use of the QFT in gaining maximum precision and dynamic range at the same time. We further point out the ability for demultiplexing the nuclear magnetic resonance (NMR) signals using QFT and demonstrate precision scaling with the number of used qubits. Our results mark the application of a complex quantum algorithm in sensing which is of particular interest for high dynamic range quantum sensing and nanoscale NMR spectroscopy experiments.Item Open Access Quantification and mitigation of PIV bias errors caused by intermittent particle seeding and particle lag by means of large eddy simulations(2021) Martins, Fabio J. W. A.; Kirchmann, Jonas; Kronenburg, Andreas; Beyrau, FrankIn the present work, a standard large eddy simulation is combined with tracer particle seeding simulations to investigate the different PIV bias errors introduced by intermittent particle seeding and particle lag. The intermittency effect is caused by evaluating the velocity from tracer particles with inertia in a region where streams mix with different seeding densities. This effect, which is different from the vastly-discussed particle lag, is frequently observed in the literature but scarcely addressed. Here, bias errors in the velocity are analysed in the framework of a turbulent annular gaseous jet weakly confined by low-momentum co-flowing streams. The errors are computed between the gaseous flow velocity, obtained directly from the simulation, and the velocities estimated from synthetic PIV evaluations. Tracer particles with diameters of 0.037, 0.37 and 3.7 µm are introduced into the simulated flow through the jet only, intermediate co-flowing stream only and through both regions. Results quantify the influence of intermittency in the time-averaged velocities and Reynolds stresses when only one of the streams is seeded, even when tracers fulfil the Stokes-number criterion. Additionally, the present work proposes assessing unbiased velocity statistics from large eddy simulations, after validation of biased seeded simulations with biased PIV measurements. The approach can potentially be applied to a variety of flows and geometries, mitigating the bias errors.Item Open Access Correlations for computation and computation for correlations(2021) Demirel, Bülent; Weng, Weikai; Thalacker, Christopher; Hoban, Matty; Barz, StefanieQuantum correlations are central to the foundations of quantum physics and form the basis of quantum technologies. Here, our goal is to connect quantum correlations and computation: using quantum correlations as a resource for computation - and vice versa, using computation to test quantum correlations. We derive Bell-type inequalities that test the capacity of quantum states for computing Boolean functions within a specific model of computation and experimentally investigate them using 4-photon Greenberger-Horne-Zeilinger (GHZ) states. Furthermore, we show how the resource states can be used to specifically compute Boolean functions - which can be used to test and verify the non-classicality of the underlying quantum states. The connection between quantum correlation and computability shown here has applications in quantum technologies, and is important for networked computing being performed by measurements on distributed multipartite quantum states.Item Open Access Low-energy optical conductivity of TaP : comparison of theory and experiment(2021) Yaresko, Alexander; Pronin, Artem V.The ab-plane optical conductivity of the Weyl semimetal TaP is calculated from the band structure and compared to the experimental data. The overall agreement between theory and experiment is found to be best when the Fermi level is slightly (20 to 60 meV) shifted upwards in the calculations. This confirms a small unintentional doping of TaP, reported earlier, and allows a natural explanation of the strong low-energy (50 meV) peak seen in the experimental ab-plane optical conductivity: this peak originates from transitions between the almost parallel non-degenerate electronic bands split by spin-orbit coupling. The temperature evolution of the peak can be reasonably well reproduce by calculations using an analog of the Mott formula.