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Micro gas turbines (MGTs) are regarded as combined heat and power (CHP) units which offer high fuel utilization and low emissions. They are applied in decentralized energy neration.
To facilitate the planning process of energy systems, namely in the context of the increasing application of optimization techniques, there is a need for easy-to-parametrize component models with sufficient accuracy which allow a fast computation. In this paper, a model is proposed where the non-linear part load characteristics of the MGT are linearized by means of physical insight of the working principles of turbomachinery. Further, it is shown that the model can be parametrized by the data usually available in spec sheets. With this model a uniform description of MGTs from several manufacturers
covering an electrical power range from 30kW to 333kW can be obtained. The MGT model was
implemented by means of Modelica/Dymola. The resulting MGT system model, comprising further heat exchangers and hydraulic components, was validated using the experimental data of a 65kW MGT from a trigeneration energy system.
Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network.
A novel method for quasi-continuous tar monitoring in hot syngas from biomass gasification is reported. A very small syngas stream is extracted from the gasifier output, and the oxygen demand for tar combustion is determined by a well-defined dosage of synthetic air. Assuming the total oxidation of all of the combustible components at the Pt-electrode of a lambda-probe, the difference of the residual oxygen concentrations from successive operations with and without tar condensation represents the oxygen demand. From experiments in the laboratory with H2/N2/naphthalene model syngas, the linear sensitivity and a lower detection limit of about 70 ± 5 mg/m3 was estimated, and a very good long-term stability can be expected. This extremely sensitive and robust monitoring concept was evaluated further by the extraction of a small, constant flow of hot syngas as a sample (9 L/h) using a Laval nozzle combined with a metallic filter (a sintered metal plate (pore diameter 10 µm)) and a gas pump (in the cold zone). The first tests in the laboratory of this setup—which is appropriate for field applications—confirmed the excellent analysis results. However, the field tests concerning the monitoring of the tar in syngas from a woodchip-fueled gasifier demonstrated that the determination of the oxygen demand by the successive estimation of the oxygen concentration with/without tar trapping is not possible with enough accuracy due to continuous variation of the syngas composition. A method is proposed for how this constraint can be overcome.
Gas Analysis and Optimization of Debinding and Sintering Processes for Metallic Binder-Based AM*
(2022)
Binder-based additive manufacturing processes for metallic
AM components in a wide range of applications usually use
organic binders and process-related additives that must be
thermally removed before sintering. Debinding processes are
typically parameterized empirically and thus far from the optimum.
Since debinding based on thermal decomposition processes
of organic components and the subsequent thermochemical
reactions between process atmosphere and metal
powder materials make uncomplicated parameterization difficult,
in-situ instrumentation was introduced at Fraunhofer
IFAM. This measurement method relies on infrared spectroscopy
and mass spectrometry in various furnace concepts to
understand the gas processes of decomposition of organic
components and the subsequent thermochemical reactions
between the carrier gas atmosphere and the metal part, as well
as their kinetics. This method enables an efficient optimization
of the temperature-time profiles and the required atmosphere
composition to realize dense AM components with low contamination.
In the paper, the optimization strategy is presented,
and the achievable properties are illustrated using a fused
filament fabrication (FFF) component example made of 316L
stainless steel.
Gas adsorption studies of CO2 and N2 in spatially aligned double-walled carbon nanotube arrays
(2013)
Babu, Deepu J. ; Lange, Marcus ; Cherkashinin, Gennady ; Issanin, Alexander ; Staudt, Reiner ; Schneider, Jörg J.
Gas adsorption studies (CO2 and N2) over a wide pressure range on vertically, highly aligned dense double-walled carbon nanotube arrays of high purity and high specific surface area are reported. At high pressures, the adsorption capacity of these materials was found to be comparable to those of metal organic frameworks and mesoporous molecular sieves. These highly aligned CNT arrays were chemically modified by treating with oxygen plasma and structurally modified by decreasing the diameter of individual carbon nanotubes. Oxygen plasma treatment led to grafting of a large number of C–O functional groups onto the CNT surface, which further increased the gas adsorption capacity. It was found that gas adsorption is dependent on tube diameter and increases with decrease of the individual CNT diameter in the CNT bundles. As results of our studies we have found that at lower pressure regimes, plasma functionalized carbon nanotubes exhibit better adsorption characteristics whereas at higher pressures, lower diameter carbon nanotube structures exhibited better gas adsorption characteristics.
Gamification wird in vielen Bereichen, die auch den Bildungssektor einschließen, zur Motivations- und Leistungssteigerung eingesetzt. Dieser Beitrag beschreibt das Design, die Umsetzung und Evaluierung eines Gamification-Konzeptes für die Vorlesung „Software Engineering" an der Hochschule Offenburg. Gamification soll nach Intention der Lehrenden eine kontinuierliche und tiefergehende Auseinandersetzung mit den Themen der Vorlesung forcieren sowie einen positiven Einfluss auf die Motivation der Studierenden haben, um den Lernprozess zu unterstützen. Zentral für das Gamification-Design sind dabei eine freiwillige Teilnahme, die Wahrnehmung der Bedeutung der Lerninhalte und ein zielorientierter Einsatz von Gamification-Elementen. Das entwickelte Konzept wurde in der Lernplattform Moodle realisiert, über drei Semester eingesetzt und parallel evaluiert. Die Ergebnisse dieser Evaluierungen zeigen, dass die Studierenden den gamifizierten Kurs intensiv und oft über das gesamte Semester nutzten und aus eigenem Antrieb eine Vielzahl von Übungen absolvierten.
Proton Exchange Membrane Fuel Cell (PEMFC) is one of the most promising technologies for sustainable energy production due to the high power density, low operative temperature and more convenient use for several applications. Nevertheless, the high generated current that characterizes PEMFC requires a specific power conditioning. In addition, specific controller must be designed to fit with system operative points changing associated with the variation of this high current. To deal with this challenge, in this paper, an electrochemical system composed of a Proton Exchange Membrane Fuel Cell (PEMFC) feeding via two phases IBC has been proposed and investigated. For robustness, the used IBC for fuel cell voltage regulation is controlled by linear quadratic regulator (LQR). Then, genetic algorithms technique is applied to optimize the LQR controller parameters giving optimal control coefficients and can if necessary be adjusted according to each working situation change. The model of the entire system is studied using Matlab/Simulink environment. The simulation’s comparative standard and robustness results both demonstrate that the proposed GA-based LQR controller outperforms the conventional PI in terms of performance metrics (overshoot reduction: between 58.93% and 97.09%; response time reduction: between 56.40% and 77.00% and ripple reduction: between 84.00% and 94.86%).
This is a commentary note on the situation of functional neuroimaging in psychiatry. With this we would like to encourage psychiatrists and the journal editors of psychiatric and related journals to at least rethink the role of functional neuroimaging in this discipline and use these imaging techniques in their various aspects of clinical diagnosis and therapy regimens,respectively.
Fully Printed Inverters using Metal‐Oxide Semiconductor and Graphene Passives on Flexible Substrates
(2020)
Singaraju, Surya Abhishek ; Cadilha Marques, Gabriel ; Gruber, Patric ; Kruk, Robert ; Hahn, Horst ; Breitung, Ben ; Aghassi-Hagmann, Jasmin
Printed and flexible metal‐oxide transistor technology has recently demonstrated great promise due to its high performance and robust mechanical stability. Herein, fully printed inverter structures using electrolyte‐gated oxide transistors on a flexible polyimide (PI) substrate are discussed in detail. Conductive graphene ink is printed as the passive structures and interconnects. The additive printed transistors on PI substrates show an on/off ratio of 106 and show mobilities similar to the state‐of‐the‐art printed transistors on rigid substrates. Printed meander structures of graphene are used as pull‐up resistances in a transistor–resistor logic to create fully printed inverters. The printed and flexible inverters show a signal gain of 3.5 and a propagation delay of 30 ms. These printed inverters are able to withstand a tensile strain of 1.5% following more than 200 cycles of mechanical bending. The stability of the electrical direct current (DC) properties has been observed over a period of 5 weeks. These oxide transistor‐based fully printed inverters are relevant for digital printing methods which could be implemented into roll‐to‐roll processes.
Formal verification (FV) is considered by many to be complicated and to require considerable mathematical knowledge for successful application. We have developed a methodology in which we have added formal verification to the verification process without requiring any knowledge of formal verification languages. We use only finite-state machine notation, which is familiar and intuitive to designers. Another problem associated with formal verification is state-space explosion. If that occurs, no result is returned; our method switches to random simulation after one hour without results, and no effort is lost. We have compared FV against random simulation with respect to development time, and our results indicate that FV is at least as fast as random simulation. FV is superior in terms of verification quality, however, because it is exhaustive.