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Das Projekt befasst sich mit einem neuen Ansatz, den pH-Wert im Beton zu bestimmen.
Der pH-Wert ist vor allem für Stahlbeton-Bauwerke von Bedeutung, da dieser maßgeblich die Korrosion des Stahls beeinflusst. Im frischen Beton liegt der pH-Wert im basischen Bereich.
Der Stahl ist in diesem Bereich passiviert, also vor schädlicher Korrosion geschützt. Durch die sogenannte Karbonatisierung sinkt der pH-Wert und die Korrosionswahrscheinlichkeit steigt deutlich an. Die Stabilität von Bauwerken in denen Stahlbeton verbaut ist, wird durch diese Korrosion langfristig beeinträchtigt. Allein in deutscher Infrastruktur rechnet man mit circa 5 Milliarden Euro Schaden jährlich.
Die entwickelte Methode verwendet Sonden, welche die Korrosion Monitoren sollen. Im Sondeninneren befindet sich eine Schicht pH-Indikator (Thymolblau) und eine Schicht mit Quantenpunkten. Die Quantenpunkte fluoreszieren, nach Anregung durch zum Beispiel einen Laser, bei circa 440 nm (blau) beziehungsweise 610 nm (gelb-orange). Das Thymolblau ist im basischen Milieu (pH > 9,6) blau, im neutralen Milieu (um pH 7) gelb-orange. Der Indikator wirkt wie ein Farbfilter und lässt, je nach pH-Wert, unterschiedliche Wellenlängen zur Glasfaser durch. Aus der gemessenen Intensität bei 440 nm und 610 nm kann ein Verhältnis ermittelt werden. Dieses Verhältnis lässt erkennen, welchen pH-Wert das Milieu besitzt, in dem sich die Sonde befindet.
Die entwickelte Methodik ist zerstörungsfrei, das heißt kein Material muss aus den Bauwerken entnommen werden. Vielmehr sollen die Sonden beim Betonieren in das Bauwerk eingebettet werden. Über Glasfasern können die Sonden jederzeit angesprochen werden. Dies ermöglicht permanentes pH-Monitoring, was die Früherkennung von Korrosionsgefahr verbessert und die Sanierungskosten verringert.
The finite volume method (FVM), like the finite element method (FEM), is a numerical method for determining an approximate solution for partial differential equations. The derivation of the two methods is based on very different considerations, as they have historically evolved from two distinct engineering disciplines, namely solid mechanics and fluid mechanics. This makes FVM difficult to learn for someone familiar with FEM. In this paper we want to show that a slight modification of the FEM procedure leads to an alternative derivation of the FVM. Both numerical methods are starting from the same strong formulation of the problem represented by differential equations, which are only satisfied by their exact solution. For an approximation of the exact solution, the strong formulation must be converted to a so-called weak form. From here on, the two numerical methods differ. By appropriate choice of the trial function and the test function, we can obtain different numerical methods for solving the weak formulation of the problem. While typically in FEM the basis functions of the trial function and test function are identical, in FVM they are chosen differently. In this paper, we show which trial and test function must be chosen to derive the FVM alternatively: The trial function of the FVM is a “shifted” trial function of the FEM, where the nodal points are now located in the middle of an integration interval rather than at the ends. Moreover, the basis functions of the test function are no longer the same as those of the trial function as in the FEM, but are shown to be a constant equal to 1. This is demonstrated by the example of a 1D Poisson equation.
The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach.
The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach.
Die PKI zum digitalen Akkreditierungssymbol der DAkkS sowie dessen Funktionsweise und daraus resultierende Mehrwerte für Kalibrierlaboratorien und Endanwender werden vorgestellt. Im Anschluss wird der aktuelle Stand bei der Einführung des digitalen Kalibrierscheins (DCC) im akkreditierten Kalibrierlabor der BAM wiedergegeben. Eine Roadmap für die Digitalisierung des Kalibrierlabors sowie die Vorstellung des Quality-X Konzeptes geben einen Ausblick in die nähere Zukunft.
Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.
The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring
(2021)
A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.
This presentation gives an introduction to the gas-sensitive aerial robots developed at BAM, including various application examples in the field of mobile robot olfaction: gas source localization and gas distribution mapping.