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In diesem Beitrag wird gezeigt wie mit Thermogravimetrischen Analysen (TGA) Aktivkohleoberflächen charakterisiert werden können. Außerdem wie TGA mit Zersetzungsgasanalyse zur Untersuchung von Adsorbat-Systemen (Carbamazepin an drei verschiedenen Aktivkohlen) zur Aufklärung von Adsorptionszuständen genutzt werden kann.
The development of hydrogen technologies is a key strategy to reduce greenhouse gas emission worldwide. Power-to-Gas is a challenging solution, in which hydrogen and methane can be used in mobility, industry, heat supply and electricity generation applications. This presentation deals with the tribological behaviour of polymer materials in hydrogen and methane, both in gas and in liquefied form.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure.
Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches – building a reference state from an averaging method or a piecewise method – a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches.
Depending on the specific plastic’s ageing sensitivities, the durations which plastic components remain in the environment can be very long. As, in the past, the advantages in ageing resistance and durability were highly evaluated, we now face the problem of quite stable plastic waste within the environment.
However, there is only little knowledge on the real timescales until macroscopic fragmentation for the different kinds of plastic under various environmental conditions.
Here, weathering methods are presented, which have been used for the failure prediction in specific outdoor conditions. Issues of uncertainty, reproducibility, or validation are discussed.
For the prediction of the plastic’s fragmentation, much longer time scales have to be considered. To do this within a limited project life span, various processes as well as their acceleration potential have to be evaluated separately, such as temperature increase or spectral shift.
The possibilities and limitations of such controlled acceleration will be discussed.
The investigation of the long-term performance of sealing systems employed in containers for radioactive waste and spent nuclear fuel is one research focus area for division 3.4 “Safety of Storage Containers” at the Bundesanstalt für Materialforschung und -prüfung (BAM). Our investigations comprise investigations on metallic and elastomeric seals and covers experimental investigations to get a database on the component/material behaviour, work on analytical descriptions and numerical analysis. Our aim is to understand the long-term behaviour of the sealing systems for evaluation of their performance during possible extended interim storage and subsequent transportation.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated
from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure.
In this paper, we introduce a nano aerial robot swarm for Indoor Air Quality (IAQ) monitoring applications such as occupational health and safety of (industrial) workplaces. The robotic swarm is composed of nano Unmanned Aerial Vehicles (UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight Metal Oxide (MOX) gas sensors for measuring the Total Volatile Organic Compound (TVOC), which is a measure for IAQ. An indoor localization and positioning system is used to estimate the absolute 3D position of the swarm similar to GPS. A test scenario was built up to validate and optimize the swarm for the intended applications. Besides calibration of the IAQ sensors, we performed experiments to investigate the influence of the rotor downwash on the gas measurements at different altitudes and compared them with stationary measurements. Moreover, we did a first evaluation of the gas distribution mapping performance. Based on this novel IAQ monitoring concept, new algorithms in the field of Mobile Robot Olfaction (MRO) are planned to be developed exploiting the abilities of an aerial robotic swarm.