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AbstractThis paper presents an experimental assessment of a blended fatigue‐extreme controller for load control employing trailing edge flaps on a lab‐scale wind turbine. The controller blends between a repetitive model predictive controller that targets fatigue loads and a dedicated extreme load controller, which consists of a simple on‐off load control strategy. The Fatigue controller uses the flapwise blade root bending moments of the three blades as input sensors. The Extreme controller additionally uses on‐blade angle of attack and velocity measurements as well as acceleration measurements to detect extreme events and to allow for a fast reaction. The experiments are conducted on the Berlin Research Turbine within the large wind tunnel of the TU Berlin. In order to reproduce test cases with deterministic extreme wind conditions that follow industry standards, the wind tunnel was redesigned. The analyzed test cases are extreme direction change, extreme coherent gust, extreme operating gust and extreme coherent gust with direction change. The test cases are analyzed by on‐blade angle of attack and velocity measurements. The load control performance of the Blended controller is compared to the pure fatigue oriented and the pure extreme load controller. The Blended controller achieves a maximum flapwise blade root bending moment reduction of 23%, which is comparable to the reduction achieved by the Extreme controller.

In the search for a nontoxic replacement of the commonly employed CdS buffer layer for Cu(In,Ga)(S,Se) $_\mathrm{2}$ based solar cells, chemically deposited Zn(O,S) thin films are a most promising choice. In this paper, we address the usually slow deposition speed of Zn(O,S) in a newly developed ammonia-free chemical bath process, resulting in a deposition of 30 nm in 3 min with good homogeneity on 30 cm × 30 cm sized substrates. Solar cells with buffer layers prepared from this process match the efficiency of CdS reference cells. In a second step, we address the light-soaking post-treatment, still needed for maximum efficiencies. By addition of aluminum to the deposition process, the initial efficiencies can be increased slightly. With the addition of boron, the light-soaking post-treatment is rendered unnecessary, while maintaining high efficiencies above 15%, surpassing reference cells with CdS buffer.

AbstractThe co‐occurrence of chronic pain and alcohol use disorders (AUDs) involves complex interactions between genetic and neurophysiological aspects, and the research has reported mixed findings when they both co‐occur. There is also an indication of a gender‐dependent effect; males are more likely to use alcohol to cope with chronic pain problems than females. Recently, a new conceptualization has emerged, proposing that the negative affective component of pain drives and maintains alcohol‐related behaviors. We studied in a longitudinal fashion alterations in alcohol drinking patterns and pain thresholds in a mouse model of chronic neuropathic pain in a sex‐dependent manner. Following partial denervation (spared nerve injury [SNI]), stimulus‐evoked pain responses were measured before chronic alcohol consumption, during drinking, during a deprivation phase, and following an episode of excessive drinking. During the course of alcohol drinking, we observed pronounced sex differences in pain thresholds. Male mice showed a strong increase in pain thresholds, suggesting an analgesic effect induced by alcohol over time, an effect that was not observed in female mice. SNI mice did not differ from sham‐operated controls in baseline alcohol consumption. However, following a deprivation phase and the reintroduction of ethanol, male SNI mice but not female mice showed more pronounced excessive drinking than controls. Finally, we observed decreased central ethanol sensitivity in male SNI mice but not in females. Together with our finding, that ethanol is able to decrease a pain‐induced negative affective memory we come to following conclusion. We propose that a lower sensitivity to the intoxicating effects of alcohol together with the ability of alcohol to reduce the negative affective component of pain may explain the higher co‐occurrence of AUD in male chronic pain patients.

This article presents numerical prediction of a thermoacoustic limit cycle in an industrial gas turbine combustor. The case corresponds to an experimental high pressure test rig equipped with the full-scale Siemens SGT-100 combustor operated at two mean pressure levels of 3 bar and 6 bar. The Flame Transfer Function (FTF) characterising the global unsteady response of the flame to velocity perturbations is obtained for both operating pressures by means of incompressible Large Eddy Simulations (LES). A linear stability analysis is then performed by coupling the FTFs with a wave-based low order thermoacoustic network solver. All the thermoacoustic modes predicted at 3 bar pressure are stable; whereas one of the modes at 6 bar is found to be unstable at a frequency of 231 Hz, which agrees with the experiments. A weakly nonlinear stability analysis is carried out by combining the Flame Describing Function (FDF) predicted by LES with the low order thermoacoustic network solver. The frequency, mode shape and velocity amplitude corresponding to the predicted limit cycle at 209 Hz are used to compute the absolute pressure fluctuation amplitude in the combustor. The numerically reconstructed amplitude is found to be reasonably close to the measured dynamics.

The presented Solid Fuel Entrained Flow Gasification Database contains complete datasets on individual solid fuels considering the most important physical properties and conversion behavior analysis necessary for the simulation of pressurized high temperature entrained flow gasification. This includes their composition (proximate- and ultimate analysis, XRF of ash at 200, 450, 815 and 1500 °C) as well as physical properties such as original mineral phase composition (XRD), density (true and tap) and particle size distribution. The database also contains data on fuel behavior such as heating values, swelling factors, fragmentation index, slag viscosity, ash melting behavior and ash mineral phase evolution during heat-up and cool-down. Moreover, the database provides model parameters describing their pyrolysis behavior, gasification kinetics including product gas inhibition, thermal deactivation and surface area development. Chars produced and gasified in pressurized high temperature entrained flow reactors like the PiTER (located at the Chair of Energy Systems of the Technical University of Munich) and the KIVAN (operated by the Institute of Energy Process Engineering and Chemical Engineering of the TU Bergakademie Freiberg) were analyzed in thermogravimetric and structural analyzer. Since these reactors are designed for temperatures up to 1800 °C (PiTER) and pressures up to 100 bar (KIVAN), the resulting model parameters are relevant for the simulation of industrial scale applications. In order to validate the applied models for entrained flow gasification kinetics, the database refers to several publications describing the models and experimental setups as well as providing additional experimental data points.

This contribution provides the simulated data and surrogate models for the DTU 10 MW reference wind turbine in an onshore configuration simulated with FAST v8.16.00. The dimensions include mean wind speed, turbulence intensity, and power level, as well as the application of an individual blade control (IBC) loop. Down-regulation up to 50% is considered using two controller trajectories. The constTSR trajectory considers only pitching for down-regulation, maintaining a constant tip speed ratio, and the lin70 trajectory considers both pitch and rotational speed reduction to achieve down-regulation. Power boosting is performed up to 130% power level by following the optimal Cp trajectory until the requested power level is reached. The regression is done with two methods: a spline-based interpolation and a Gaussian Process Regression (GPR). The raw data, smoothened data, and the trained GPR models are provided along with scripts for generating the surrogate model's predictions with both methods. A short description of the simulation parameters and variables considered is given in the supplementary pdf file. The dataset is part of the doctoral thesis 'Wind Turbine Operational Optimization Considering Revenue and Fatigue Objectives' by Vasilis Pettas at the University of Stuttgart (http://dx.doi.org/10.18419/opus-13959) and the journal publication 'Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities' (https://doi.org/10.3390/en17061284). Detailed analysis of the controller design and validation of the surrogate models can be found in these publications. 

Optimisation results for the lowest lifetime cost system consisting of solar photovoltaic (PV), hybrid photovoltaic-thermal (PV-T) and solar-thermal collectors alongside battery and hot-water storage systems for meeting the electrical and thermal (hot-water) needs of three multi-effect distillation (MED) plants. The updated results are from optimisations runs carried out in response to peer-review comments.

Raw Data, evaluated files and a list of experiments is provided.