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Validation of a novel method for detecting and stabilizing malfunctioning areas in fuel cell stacks
(2014)
In this paper a setup for detecting malfunctioning areas of MEAs in fuel cell stacks is described. Malfunctioning areas generate electric cross currents inside bipolar plates. To exploit this we suggest bipolar plates consisting not of two but of three layers. The third one is a highly conducting layer and segmented such that the cross currents move along the segments to the surface of the stack where they can be measured by an inductive sensor. With this information a realistic model can be used to detect the malfunctioning area. Furthermore the third layer will prevent any current inhomogeneity of a malfunctioning cell to spread to neighbouring cells in the stack. In this work the results of measurements in a realistic cell setup will be compared with the results obtained in simulation studies with the same configuration. The basis for the comparison is the reliable characterisation of the electrical properties of the cell components and the implication of these results into the simulation model. The experimental studies will also show the limits in the maximum number of segments, which can be used for a reliable detection of cross currents.
It is well known that the already large dielectric constants of some electrolytes like BaTiO₃ can be enhanced further by adding metallic (e.g. Ni, Cu or Ag) nanoparticles. The enhancement can be quite large, a factor of more than 1000 is possible. The consequences for the properties will be discussed in the present paper applying a brick-layer model (BLM) for calculating dc-resistivities of thin layers and a modified one (PBLM) that includes percolation for calculating dielectric properties of these materials. The PBLM results in an at least qualitative description and understanding of the physical phenomena: This model gives an explanation for the steep increase of the dielectric constant below the percolation threshold and why this increase is connected to a dramatic decrease of the breakdown voltage as well as the ability of storing electrical energy. We conclude that metallic electrolyte composites like BaTiO₃ are not appropriate for energy storage.
Über die Äquivalenz zweier Transmissionsrandwertprobleme aus der elektromagnetischen Streutheorie
(1994)
On the Vanishing Displacement Current Limit for Eddy-Current Problems / Quell, P. ; Reißel, M.
(1996)
A novel scheme for precise diagnostics and effective stabilization of currents in a fuel cell stack
(2010)
Magnetotomography and Electric Currents in a Fuel Cell / Lustfeld, H. ; Reißel, M. ; Steffen, B.
(2009)
This paper describes the modeling of a high-temperature storage system for an existing solar tower power plant with open volumetric receiver technology, which uses air as heat transfer medium (HTF). The storage system model has been developed in the simulation environment Matlab/Simulink®. The storage type under investigation is a packed bed thermal energy storage system which has the characteristics of a regenerator. Thermal energy can be stored and discharged as required via the HTF air. The air mass flow distribution is controlled by valves, and the mass flow by two blowers. The thermal storage operation strategy has a direct and significant impact on the energetic and economic efficiency of the solar tower power plants.
Based on an identifying Volterra type integral equation for randomly right censored observations from a lifetime distribution function F, we solve the corresponding estimating equation by an explicit and implicit Euler scheme. While the first approach results in some known estimators, the second one produces new semi-parametric and pre-smoothed Kaplan–Meier estimators which are real distribution functions rather than sub-distribution functions as the former ones are. This property of the new estimators is particular useful if one wants to estimate the expected lifetime restricted to the support of the observation time.
Specifically, we focus on estimation under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. We show that some estimated linear functionals which are based on the new semi-parametric estimator are strong consistent, asymptotically normal, and efficient under SRCM. In a small simulation study, the performance of the new estimator is illustrated under moderate sample sizes. Finally, we apply the new estimator to a well-known real dataset.