University of Maribor

About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.

Observation of the ψ(4415)→DD̄2*(2460) decay using initial-state radiation

Abstract: Reference EPFL-ARTICLE-154403doi:10.1103/PhysRevLett.100.062001View record in Web of Science Record created on 2010-11-05, modified on 2017-12-10

The second Sandia Fracture Challenge: predictions of ductile failure under quasi-static and moderate-rate dynamic loading

Abstract: Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in $$\sim $$ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.

Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy

Abstract: This paper presents the parameter identification of an equivalent circuit-based proton-exchange membrane fuel cell model. This model is represented by two electrical circuits, of which one reproduces the fuel cell’s output voltage characteristic and the other its thermal characteristic. The output voltage model includes activation, concentration, and ohmic losses, which describe the static properties, while the double-layer charging effect, which delays in fuel and oxygen supplies, and other effects provide the model’s dynamic properties. In addition, a novel thermal model of the studied Ballard’s 1.2-kW Nexa fuel cell is proposed. The latter includes the thermal effects of the stack’s fan, which significantly improve the model’s accuracy. The parameters of both, the electrical and the thermal, equivalent circuits were estimated on the basis of experimental data using an evolution strategy. The resulting parameters were validated by the measurement data obtained from the Nexa module. The comparison indicates a good agreement between the simulation and the experiment. In addition to simulations, the identified model is also suitable for usage in real-time fuel cell emulators. The emulator presented in this paper additionally proves the accuracy of the obtained model and the effectiveness of using an evolution strategy for identification of the fuel cell’s parameters.