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The presented paper describes a shape optimization workflow using Bayesian strategies. It is applied to a novel automotive axle system consisting of leaf springs made from glass fiber reinforced plastics (GFRP). Besides the primary objectives of cost and mass reduction, the assembly has to meet multiple technical constraints with respect to various loading conditions. The related large-scale finite element model is fully parameterized by splines, hence the general shape of the guide curve as well as the spring’s height, width and material properties can be altered by the corresponding workflow. For this purpose, a novel method is developed to automatically generate high-quality meshes depending on the geometry of the respective springs. The size and complexity of the model demands the implementation of efficient optimization techniques with a preferably small number of required response function evaluations. Therefore, an existing optimization framework is extended by state-of-the-art Bayesian methods, including different kernel combinations and multiple acquisition function approaches, which are then tested, evaluated and compared. To properly address the use of GFRP as spring material in the objective function, an appropriate cost model is derived. Emerging challenges, such as conflicting targets regarding direct material costs and potential lightweight measures, are considered and investigated. The intermediate steps of the developed optimization procedure are tested on various sample functions and simplified models. The entire workflow is finally applied to the complete model and evaluated. Concluding, ideas and possibilities in improving the optimization process, such as the use of models with varying complexity, are discussed.

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

Zusammenfassung Zur Erreichung der EU-Klima- und Energieziele bis zum Jahre 2030 hat die Europäische Kommission im Jahre 2018 einen Aktionsplan zur Finanzierung nachhaltigen Wachstums in der EU vorgelegt. Die EU-Kommission geht davon aus, dass die dafür jährlich notwendigen Mittel i. H. v. 180 Mrd. € pro Jahr nicht nur von öffentlichen, sondern auch von privaten Investoren aufzubringen sind. Der Aktionsplan sieht deshalb vor, dass künftig ESG-Präferenzen in der Finanzberatung bei Privatanlegern mit Hilfe eines Klassifizierungssystem für nachhaltige Tätigkeiten – die sog. EU-Taxonomie – zu berücksichtigen sind. Der Beitrag ermittelt den aktuellen Diskussionsstand durch Einbeziehung von Experten. Aufbauend darauf werden Anforderungen und Handlungsempfehlungen für ein regulatorisches Umsetzungskonzept entwickelt.

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

Die Automobilindustrie befindet sich derzeit in einem tiefgreifenden Veranderungsprozess. Strenge Emissionsgrenzwerte, vermehrte Fahrverbotszonen fur Innenstadte und hohere staatliche Subventionen fur emissionsreduzierte Fahrzeuge beschleunigen den Wandel hin zur Elektromobilitat. Uber die Veranderungen der Produktwelt hinaus steigt auch der gesellschaftliche Druck auf die Automobilhersteller und -zulieferer, in der Dekarbonisierung der eigenen Herstellungsprozesse erkennbare Fortschritte zu erzielen. Denn die Automobilindustrie zahlt in vielen Landern zu den groseren Treibhausgasemittenten und Rohstoffverbrauchern. Auch die ElringKlinger AG richtet ihr unternehmerisches Handeln umweltorientiert aus. Dabei steht die Erreichung der Klimaneutralitat langfristig im Fokus. Sie wird sich auf alle internen Prozesse auswirken. Die Herausforderung fur ElringKlinger besteht darin, einerseits in neuen Wettbewerbsstrukturen mit neuen Produkten erfolgreich zu sein und andererseits fur alle Werke Strukturen, Prozesse und Technologien zu schaffen, die eine klimaneutrale Produktion ermoglichen. Vor allem die Mitarbeiter spielen im stetig voranschreitenden Transformationsprozess eine entscheidende Rolle. Es gilt, sie fur die neuen Aufgabenbereiche zu qualifizieren und weiterzubilden. Mit dem Streben nach Klimaneutralitat in der Produktion will ElringKlinger langfristig nicht nur weitere Wettbewerbsvorteile schaffen, sondern auch einen okologischen Mehrwert erzielen.

The electric field response of the lead-free solid solution (1&minus, x)Bi0.53Na0.47TiO3&ndash, xBaTiO3 (BNT&ndash, BT) in the higher BT composition range with x = 0.12 was investigated using in situ synchrotron X-ray powder diffraction. An introduced Bi-excess non-stoichiometry caused an extended morphotropic phase boundary, leading to an unexpected fully reversible relaxor to ferroelectric (R&ndash, FE) phase transformation behavior. By varying the field frequency in a broad range from 10&minus, 4 up to 102 Hz, BNT&ndash, 12BT showed a frequency-dependent gradual suppression of the field induced ferroelectric phase transformation in favor of the relaxor state. A frequency triggered self-heating within the sample was found and the temperature increase exponentially correlated with the field frequency. The effects of a lowered phase transformation temperature TR&ndash, FE, caused by the non-stoichiometric composition, were observed in the experimental setup of the freestanding sample. This frequency-dependent investigation of an R&ndash, FE phase transformation is unlike previous macroscopic studies, in which heat dissipating metal contacts are used.

Applied physics letters 116(18), 182902 - (2020). doi:10.1063/5.0005401, In this work, the lead-free composition (1-$x$)Bi$_{0.5}$Na$_{0.5}$TiO$_3$–$x$BaTiO$_3$ (BNT–BT) with x = 0.12 was investigated using in situ Synchrotron x-ray powder diffraction. With the applied electric field, the pseudo-cubic relaxor phase reversibly transforms to a ferroelectric state. The reversibility is still preserved after 104 bipolar electric field cycles. A Rietveld refinement with a structure, strain, and texture analysis using a model based on the atomic scale was applied for four frequencies from 10−4 to 101 Hz. The analysis allowed us to separately determine the two coexisting phases, their electric field dependent evolution, and the underlying strain mechanisms. For all the applied frequencies, we showed that domain switching is the only strain mechanism appearing in the tetragonal phase and the lattice strain is the only mechanism in the rhombohedral phase. The coercive field of the tetragonal phase (4 kV/mm) is found to be higher than that of the rhombohedral phase (3 kV/mm). This divergence has not been observed in previously investigated lead-containing materials and cannot be detected solely using macroscopic strain and polarization experiments. Moreover, the domain strain abruptly starts to occur only after a threshold field value and exhibits high hysteresis. The lattice strain, on the other hand, starts nearly from the beginning and increases more linearly during the bipolar field cycle. It could, therefore, be demonstrated that complex structural mechanisms underlie the apparent clear and continuous macroscopic strain curve. These findings are crucial for all actuator materials undergoing a relaxor to ferroelectric phase transformation and provide approaches and strategies to optimize lead-free materials for tailored applications., Published by American Inst. of Physics, Melville, NY

The aim of this contribution is to describe our recent development of a novel compact field-programmable gate-array (FPGA)-based data acquisition (DAQ) system for use with multichannel X-ray detectors at synchrotron radiation facilities. The system is designed for time resolved counting of single photons arriving from several—currently 12—independent detector channels simultaneously. Detector signals of at least 2.8 ns duration are latched by asynchronous logic and then synchronized with the system clock of 100 MHz. The incoming signals are subsequently sorted out into 10 000 time-bins where they are counted. This occurs according to the arrival time of photons with respect to the trigger signal. Repeatable mode of triggered operation is used to achieve high statistic of accumulated counts. The time-bin width is adjustable from 10 ns to 1 ms. In addition, a special mode of operation with 2 ns time resolution is provided for two detector channels. The system is implemented in a pocket-size FPGA-based hardware of 10 cm $\times 10$ cm $\times 3$ cm and thus can easily be transported between synchrotron radiation facilities. For setup of operation and data read-out, the hardware is connected via USB interface to a portable control computer. DAQ applications are provided in both LabVIEW and MATLAB environments.

In this paper we address certain issues in the valuation of long-term (insurance) guarantees from a practical point of view. These issues arise, because markets are incomplete or inactive. We provide a general, arbitrage-free valuation approach in this context and discuss the following topics in more detail: extrapolation of interest rates, treatment of interest rate spreads and counter-cyclical measures in distorted markets. By deriving solutions from a practitioner’s point of view this paper aims to contribute to an effective regulation under Solvency II. Moreover, we hope to provide a starting point for an in-depth academic analysis of these problems.

We consider a stochastic control problem over an infinite horizon where the state process is influenced by an unobservable environment process. In particular, the Hidden-Markov-model and the Bayesian model are included. This model under partial information is transformed into an equivalent one with complete information by using the well-known filter technique. In particular, the optimal controls and the value functions of the original and the transformed problem are the same. An explicit representation of the filter process which is a piecewise-deterministic process, is also given. Then we propose two solution techniques for the transformed model. First, a generalized verification technique (with a generalized Hamilton–Jacobi–Bellman equation) is formulated where the strict differentiability of the value function is weaken to local Lipschitz continuity. Second, we present a discrete-time Markovian decision model by which we are able to compute an optimal control of our given problem. In this context we are also able to state a general existence result for optimal controls. The power of both solution techniques is finally demonstrated for a parallel queueing model with unknown service rates. In particular, the filter process is discussed in detail, the value function is explicitly computed and the optimal control is completely characterized in the symmetric case.

All hitherto identified aromatic compounds accumulating in leaves of Arabidopsis thaliana (L.) Heynh. upon infection with virulent or avirulent strains of Pseudomonas syringae pathovar tomato ( Pst) were indolic metabolites. We now report the strong accumulation of a novel type of natural product, 3'-O-beta-D-ribofuranosyl adenosine (3'RA), exclusively during compatible interactions. In contrast to the various indolic metabolites, 3'RA was undetectable in incompatible interactions of A. thaliana leaves with an avirulent Pst strain, as well as in uninfected control leaves. A similar, strong induction of 3'RA was observed in compatible but, again, not in incompatible interactions of Pst with its natural host, Lycopersicon esculentum. The strength of the effect and its confinement to compatible interactions suggests that it may be applicable as a diagnostic tool.

The project of periodizing capitalism will remain an inconsequential academic exercise unless it engages in closer reflection on its underlying theoretical foundations. On the basis of such reflection, it should be possible to differentiate concepts of the various stages, phases and crises of capitalist development (concepts which are often conflated) and, above all, to assess the potential of an action-oriented approach with the aim of overcoming entrenched power, exploitation and repression. We will attempt in the following analysis to use our own underlying theoretical concepts of periodization as a means to understand the present social context and the current developmental tendencies of capitalism. To study the breaks and continuities of capitalist development, we take up the regulation approach, an approach we believe can mediate between the Marxist theory of the capitalist mode of production and the concrete analysis of social formations, and thus produce a theory of the periodization of capitalist development.