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- Englisch (4) (entfernen)
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- Distributed acoustic sensing (2)
- Distributed fiber optic sensing (2)
- Artificial neural networks (1)
- DAS (1)
- Flammability (1)
- Halogen‐free (1)
- Multicomponent systems (1)
- Polypropylene (1)
- Train monitoring (1)
- Train tracking (1)
Organisationseinheit der BAM
Developing halogen‐free flame retardants with reasonably high efficiency, which thus function at limited loadings in polypropylene‐based wood/plastic composites (WPC), is still a challenge. Cost‐effective flame‐retarded WPC have been identified as a way to open the door to an interesting, broader spectrum of application in the building and transportation sectors. This work imparts a systematic comprehensive understanding and assessment of different basic routes to halogen‐free flame‐retarded WPC, taking into account economic and environmental considerations. Cheap, halogen‐free single‐component flame retardants and their multicomponent systems are investigated at reasonable filling grades of 20 wt%. The basic routes of promising synergistic multicomponent systems are discussed, and their potential and Limits assessed. Optimizing the consistency of fire residue; closing the surface of inorganic‐organic residual layers; the thermal stabilization and design of the residue, eg, synergistic combination of ammonium polyphosphate and expandable graphite; and the combination of different flame‐retardant mechanisms, eg, intumescence and flame inhibition, are proposed as promising routes to boost the flame‐retardant efficiency.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.