Exploiting the Layout Engine to Assess Diagram Completions

A practicable approach to diagram completion is to first compute model completions on the abstract syntax level. These can be translated to corresponding diagram changes by the layout engine afterwards. Normally, several different model completions are possible though. One way to deal with this issue is to let the user choose among them explicitly, which is already helpful. However, such a choice step is a quite time-consuming interruption of the editing process. We argue that users often are mainly interested in completions that preserve their original diagram as far as possible. This criterion cannot be checked on the abstract syntax level though. In fact, minimal model changes might still result in enormous changes of the original diagram. Therefore, we suggest to use the layout engine in advance for assessing all possible model completions with respect to the diagram changes they eventually cause

Layout Specification on the Concrete and Abstract Syntax Level of a Diagram Language

A visual language consists of several visual component types, e.g. states or transitions in DFAs. Nowadays, the language itself is usually specified via a meta model. To make a diagram look nice, a layouter is required. This layouter may either operate on the concrete syntax level, i.e., on the visual components, or on the abstract syntax level, i.e., on the model instance. In this paper we present an approach that is capable of specifying a flexible layout on both, the concrete as well as the abstract syntax level of a diagram. The approach uses pattern-based transformations. Besides structured editing, it also supports free-hand editing, a challenging task for the layouter. We introduce how such a specification can be created and examine the advantages and shortcomings of each of either operating on the concrete syntax level or on the abstract syntax level

On using Machine Learning Algorithms for Motorcycle Collision Detection

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds passenger car accidents, efforts have been directed toward increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria

Multiscale microkinetic modelling of carbon monoxide and methane oxidation over Pt/γ-Al2O3 catalyst

Although compared to conventional diesel and gasoline engines gas engines running on methane-based fuels emit less pollutants, slip of unburnt methane is a hurdle to be overcome. In this regard, particularly noble metal-based catalysts allow for an efficient methane conversion even at low temperatures. Since these catalysts can undergo modifications under the highly dynamic operation [1] affecting activity and stability, the present work aims at creating a multiscale microkinetic model that has a strong link to the structure of the active sites, which change according to the chemical environment they are exposed. A detailed surface reaction mechanism for platinum-catalysed abatement of exhaust gases by Koop et al. [2] was used as a basis for the further development. The model is validated using light-off experiments with a monolithic Pt/Al2O3 catalyst in stoichiometric model gas mixtures. Simulations were carried out using the DETCHEMCHANNEL software [3] and show a remarkable difference, especially regarding the predicted ignition temperature. This different behaviour could be associated to the activation energies of the key reactive steps that need further investigation, i.e. dissociative adsorption of CH4. Along with theoretical considerations, spatially resolved information from experiments are used to improve the model. [1] P. Lott, O. Deutschmann, “Lean-Burn Natural Gas Engines: Challenges and Concepts for an Efficient Exhaust Gas Aftertreatment System” Emiss. Control Sci. Technol. 7, 1-6 (2021). [2] J. Koop, O. Deutschmann, “Detailed surface reaction mechanism for Pt-catalyzed abatement of automotive exhaust gases”, Appl. Cat. B 91, 1 (2009) [3] O. Deutschmann, S. Tischer, C. Correa, D. Chatterjee, S. Kleditzsch, V.M. Janardhanan, N. Mladenov, H. D. Minh, H. Karadeniz, M. Hettel, V. Menon, A. Banerjee, H. Goßler, E. Daymo, DETCHEM Software package, 2.8 ed., www.detchem.com, Karlsruhe 2020

Dynamic Human Body Models in Vehicle Safety: An Overview

Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation and mobility classes lead to a larger number of occupant positions, interiors and load directions. As safety systems interact with and protect occupants, it is essential to place the human, with its variability and vulnerability, at the center of the design and operation of these systems. Digital human body models (HBMs) can help meet these requirements and are therefore increasingly being integrated into the development of new vehicle models. This contribution provides an overview of current HBMs and their applications in vehicle safety in different driving modes. The authors briefly introduce the underlying mathematical methods and present a selection of HBMs to the reader. An overview table with guideline values for simulation times, common applications and available variants of the models is provided. To provide insight into the broad application of HBMs, the authors present three case studies in the field of vehicle safety: (i) in-crash finite element simulations and injuries of riders on a motorcycle; (ii) scenario-based assessment of the active pre-crash behavior of occupants with the Madymo multibody HBM; (iii) prediction of human behavior in a take-over scenario using the EMMA model

Influence of Pre-strain on Very-Low-Cycle Stress–Strain Response and Springback Behavior

The influence of pre-strain on the very-low-cycle loading behavior as occurring, for example during roller leveling of sheet metals, is not yet fully understood. A key factor in this context is the stiffness of the material and its changes upon processing. To study the general mechanical property changes during low-cycle loading with small amplitudes for a wide variety of metals, sheet samples of mild steel DC01, pure copper CU-DHP and α-titanium are subjected to low-cycle tension–compression tests. The general influences of pre-strain and the applied strain amplitude are investigated regarding material hardening and changes in the elastic properties. It is shown that all tested materials feature changes in the Bauschinger behavior during cycling. The apparent elastic modulus of the materials decreases with increasing accumulated plastic strain, and the evolution depends on the strain amplitude and the pre-strain. For all three materials, changes in technical springback are present and depend on the loading history. © 2020, The Author(s)