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- 2019 (453) (entfernen)
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- 7 Bauwerkssicherheit (204)
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- 7.5 Technische Eigenschaften von Polymerwerkstoffen (44)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (35)
- 3.2 Gefahrguttanks und Unfallmechanik (34)
- 7.4 Baustofftechnologie (32)
Eingeladener Vortrag
- nein (212)
Die Verwendung pyrotechnischer Gegenstände in Menschenmengen (u. a. in Fußballstadien) hat in den letzten Jahren stark zugenommen. Zur Verwendung gelangen vorrangig sonstige pyrotechnische Gegenstände der Kategorie P1. Diese Gegenstände sind frei verkäuflich für Personen ab einem Alter von 18 Jahren. Am häufigsten werden pyrotechnische Gegenstände verwendet, die Rauch und Licht in verschiedenen Farben erzeugen.
We present, to our knowledge for the first time, a 100-km Brillouin Optical Frequency-Domain Analysis (BOFDA) employing a 200-km fiber loop. Compared to our previous publication, enhanced sensor length, sensor accuracy and spatial resolution are presented. The performance improvements are achieved by applying distributed Raman amplification (DRA) and a digital high-pass filter. We report on temperature measurements over sensing distances of 75 km and 100 km both with a 12.5-m spatial resolution. Temperature changes of 5 °C have been measured along 75 km sensing fiber. A temperature change of 30 °C has been detected at 99.5 km.
An analysis has been presented pertaining to the use of a convex
bottom tank shape instead of an elliptical, for a road tanker.
Two analyses have been made, concerning the static and the dynamic
relative performance expectations of such tank shape when compared
with the elliptical shape performance.
The outputs of both analyses suggest that the level of average benefit
due to using the proposed shape, is around 10%, with a maximum
benefit observed in the dynamic performance in the case of the
minimum fill level.
Correlation between static and dynamic outputs. Manufacturing.
The potential benefits to the roll stability of a road tanker, derived from using a convex-bottom tank, instead of a concave-bottom one, are assessed. The convex-bottom tank consists of a modified elliptical figure to which the concave bottom is substituted by a convex one. The concave-bottom consists of an elliptical shape. Static and dynamic analyses are performed, for which the proposed shape revels enhancements on the order of 10%. The static rollover stability factor, and the lateral load transfer ratio, are used as performance measures for the static and the dynamic analysis, respectively. The dynamic analysis derives from a simplified model, for which the sloshing mass is substituted by a calibrated simple pendulum. Other potential benefits of the proposed tank shape are discussed, including a reduction in the aerodynamic drag, and a longer life for the vehicle components.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
Driven steel piles are commonly used as deep foundations for a wide range of engineering structures, particularly in the offshore branch. They are also an interesting example among the broad spectrum of geotechnical applications where the fluid-solid interaction at the pore-scale can play a major role for the macromechanical behaviour of the whole system.
In the context of the geotechnical practice for offshore wind-farm structures, both the industrial design and the actual dimensions of the large piles used as foundations in the seabed are often driven by factors such as the soil resistance to driving (SRD), which are still not well understood and often estimated based on mere empirical correlations or overly simplified one-dimensional models. In particular, the role of the micromechanical effects during the installation process (e.g. local dilatancy or contractancy) and their consequences on the pore pressure levels at the pile-tip and on the effective resistance to driving, are generally either disregarded or at most assumed to be covered by the simplified engineering “black-box” solutions.
Here, we propose a general framework to address such local aspects of a geotechnical application involving fluid-saturated soils while retaining the focus on the micro-scale phenomena. We advocate for an approach that combines the relative simplicity of the Discrete Element Method (DEM) for the solid mechanics with the capabilities of the Lattice Boltzmann Method (LBM) for the fluid dynamics. In this sense, we aim to compile some useful techniques and practical recommendations for an efficient GPU-based implementation of a micromechanical LBM-DEM simulation tool.
A hyper reduced domain decomposition approach for modeling nonlinear heterogeneous structures
(2019)
Many of today's problems in engineering demand reliable and accurate prediction of failure mechanisms of mechanical structures. Herein it is necessary to take into account the often heterogeneous structure on the fine scale, to capture the underlying physical phenomena. However, an increase of accuracy by dissolving the fine scale inevitably leads to an increase in computational cost. In the context of multiscale simulations, the FE2 method is widely used. In a two-level computation, the fine scale is depicted by a boundary value problem for a representative volume element (RVE), which is then solved in each integration point of the macro scale to determine the macroscopic response. However, the FE2 approach in general is computationally expensive and problematic in the special case of concrete structures. Here rather large RVEs are necessary to sufficiently represent the meso-structure, such that separation of scales cannot be assumed.
Therefore, the aim is to develop an efficient approach to modeling nonlinear heterogeneous structures using domain decomposition and reduced order modeling.
This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.