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An overview on dry low NOx micromix combustor development for hydrogen-rich gas turbine applications
(2019)
This study investigated the anaerobic digestion of an algal–bacterial biofilm grown in artificial wastewater in an Algal Turf Scrubber (ATS). The ATS system was located in a greenhouse (50°54′19ʺN, 6°24′55ʺE, Germany) and was exposed to seasonal conditions during the experiment period. The methane (CH4) potential of untreated algal–bacterial biofilm (UAB) and thermally pretreated biofilm (PAB) using different microbial inocula was determined by anaerobic batch fermentation. Methane productivity of UAB differed significantly between microbial inocula of digested wastepaper, a mixture of manure and maize silage, anaerobic sewage sludge, and percolated green waste. UAB using sewage sludge as inoculum showed the highest methane productivity. The share of methane in biogas was dependent on inoculum. Using PAB, a strong positive impact on methane productivity was identified for the digested wastepaper (116.4%) and a mixture of manure and maize silage (107.4%) inocula. By contrast, the methane yield was significantly reduced for the digested anaerobic sewage sludge (50.6%) and percolated green waste (43.5%) inocula. To further evaluate the potential of algal–bacterial biofilm for biogas production in wastewater treatment and biogas plants in a circular bioeconomy, scale-up calculations were conducted. It was found that a 0.116 km2 ATS would be required in an average municipal wastewater treatment plant which can be viewed as problematic in terms of space consumption. However, a substantial amount of energy surplus (4.7–12.5 MWh a−1) can be gained through the addition of algal–bacterial biomass to the anaerobic digester of a municipal wastewater treatment plant. Wastewater treatment and subsequent energy production through algae show dominancy over conventional technologies.
Direct air capture (DAC) combined with subsequent storage (DACCS) is discussed as one promising carbon dioxide removal option. The aim of this paper is to analyse and comparatively classify the resource consumption (land use, renewable energy and water) and costs of possible DAC implementation pathways for Germany. The paths are based on a selected, existing climate neutrality scenario that requires the removal of 20 Mt of carbon dioxide (CO2) per year by DACCS from 2045. The analysis focuses on the so-called “low-temperature” DAC process, which might be more advantageous for Germany than the “high-temperature” one. In four case studies, we examine potential sites in northern, central and southern Germany, thereby using the most suitable renewable energies for electricity and heat generation. We show that the deployment of DAC results in large-scale land use and high energy needs. The land use in the range of 167–353 km2 results mainly from the area required for renewable energy generation. The total electrical energy demand of 14.4 TWh per year, of which 46% is needed to operate heat pumps to supply the heat demand of the DAC process, corresponds to around 1.4% of Germany's envisaged electricity demand in 2045. 20 Mt of water are provided yearly, corresponding to 40% of the city of Cologne‘s water demand (1.1 million inhabitants). The capture of CO2 (DAC) incurs levelised costs of 125–138 EUR per tonne of CO2, whereby the provision of the required energy via photovoltaics in southern Germany represents the lowest value of the four case studies. This does not include the costs associated with balancing its volatility. Taking into account transporting the CO2 via pipeline to the port of Wilhelmshaven, followed by transporting and sequestering the CO2 in geological storage sites in the Norwegian North Sea (DACCS), the levelised costs increase to 161–176 EUR/tCO2. Due to the longer transport distances from southern and central Germany, a northern German site using wind turbines would be the most favourable.
Analysis and computation of the transmission eigenvalues with a conductive boundary condition
(2022)
We provide a new analytical and computational study of the transmission eigenvalues with a conductive boundary condition. These eigenvalues are derived from the scalar inverse scattering problem for an inhomogeneous material with a conductive boundary condition. The goal is to study how these eigenvalues depend on the material parameters in order to estimate the refractive index. The analytical questions we study are: deriving Faber–Krahn type lower bounds, the discreteness and limiting behavior of the transmission eigenvalues as the conductivity tends to infinity for a sign changing contrast. We also provide a numerical study of a new boundary integral equation for computing the eigenvalues. Lastly, using the limiting behavior we will numerically estimate the refractive index from the eigenvalues provided the conductivity is sufficiently large but unknown.
In the past, CSP and PV have been seen as competing technologies. Despite massive reductions in the electricity generation costs of CSP plants, PV power generation is - at least during sunshine hours - significantly cheaper. If electricity is required not only during the daytime, but around the clock, CSP with its inherent thermal energy storage gets an advantage in terms of LEC. There are a few examples of projects in which CSP plants and PV plants have been co-located, meaning that they feed into the same grid connection point and ideally optimize their operation strategy to yield an overall benefit. In the past eight years, TSK Flagsol has developed a plant concept, which merges both solar technologies into one highly Integrated CSP-PV-Hybrid (ICPH) power plant. Here, unlike in simply co-located concepts, as analyzed e.g. in [1] – [4], excess PV power that would have to be dumped is used in electric molten salt heaters to increase the storage temperature, improving storage and conversion efficiency. The authors demonstrate the electricity cost sensitivity to subsystem sizing for various market scenarios, and compare the resulting optimized ICPH plants with co-located hybrid plants. Independent of the three feed-in tariffs that have been assumed, the ICPH plant shows an electricity cost advantage of almost 20% while maintaining a high degree of flexibility in power dispatch as it is characteristic for CSP power plants. As all components of such an innovative concept are well proven, the system is ready for commercial market implementation. A first project is already contracted and in early engineering execution.
Deoxyribonucleic acid (DNA) and protein recognition are now standard tools in biology. In addition, the special optical properties of microsphere resonators expressed by the high quality factor (Q-factor) of whispering gallery modes (WGMs) or morphology dependent resonances (MDRs) have attracted the attention of the biophotonic community. Microsphere-based biosensors are considered as powerful candidates to achieve label-free recognition of single molecules due to the high sensitivity of their WGMs. When the microsphere surface is modified with biomolecules, the effective refractive index and the effective size of the microsphere change resulting in a resonant wavelength shift. The transverse electric (TE) and the transverse magnetic (TM) elastic light scattering intensity of electromagnetic waves at 600 and 1400 nm are numerically calculated for DNA and unspecific binding of proteins to the microsphere surface. The effect of changing the optical properties was studied for diamond (refractive index 2.34), glass (refractive index 1.50), and sapphire (refractive index 1.75) microspheres with a 50 µm radius. The mode spacing, the linewidth of WGMs, and the shift of resonant wavelength due to the change in radius and refractive index, were analyzed by numerical simulations. Preliminary results of unspecific binding of biomolecules are presented. The calculated shift in WGMs can be used for biomolecules detection.
This study analyses the expected utilization of an urban distribution grid under high penetration of photovoltaic and e-mobility with charging infrastructure on a residential level. The grid utilization and the corresponding power flow are evaluated, while varying the control strategies and photovoltaic installed capacity in different scenarios. Four scenarios are used to analyze the impact of e-mobility. The individual mobility demand is modelled based on the largest German studies on mobility “Mobilität in Deutschland”, which is carried out every 5 years. To estimate the ramp-up of photovoltaic generation, a potential analysis of the roof surfaces in the supply area is carried out via an evaluation of an open solar potential study. The photovoltaic feed-in time series is derived individually for each installed system in a resolution of 15 min. The residential consumption is estimated using historical smart meter data, which are collected in London between 2012 and 2014. For a realistic charging demand, each residential household decides daily on the state of charge if their vehicle requires to be charged. The resulting charging time series depends on the underlying behavior scenario. Market prices and mobility demand are therefore used as scenario input parameters for a utility function based on the current state of charge to model individual behavior. The aggregated electricity demand is the starting point of the power flow calculation. The evaluation is carried out for an urban region with approximately 3100 residents. The analysis shows that increased penetration of photovoltaics combined with a flexible and adaptive charging strategy can maximize PV usage and reduce the need for congestion-related intervention by the grid operator by reducing the amount of kWh charged from the grid by 30% which reduces the average price of a charged kWh by 35% to 14 ct/kWh from 21.8 ct/kWh without PV optimization. The resulting grid congestions are managed by implementing an intelligent price or control signal. The analysis took place using data from a real German grid with 10 subgrids. The entire software can be adapted for the analysis of different distribution grids and is publicly available as an open-source software library on GitHub.
The composition and physiochemical properties of aquatic-phase natural organic matter (NOM) are most important problems for both environmental studies and water industry. Laser desorption/ionization (LDI) mass spectrometry facilitated successful examinations of NOM, as humic and fulvic acids in NOM are readily ionized by the nitrogen laser. In this study, hydrophobic NOMs (HPO NOMs) from river, reservoir and waste water were characterized by this technique. The effect of analytical variables like concentration, solvent composition and laser energy was investigated. The exact masses of small molecular NOM moieties in the range of 200–1200 m/z were determined in reflectron mode. In addition, spectra of post-source-decay experiments in this range showed that some compounds from different natural NOMs had the same fragmental ions. In the large mass range of 1200–15 000 Da, macromolecules and their aggregates were found in HPO NOMs from natural waters. Highly humic HPO exhibited mass peaks larger than 8000 Da. On the other hand, the waste water and reservoir water mainly had relatively smaller molecules of about 2000 Da. The LDI-MS measurements indicated that highly humic river waters were able to form large aggregates and membrane foulants, while the HPO NOMs from waste water and reservoir water were unlikely to form large aggregates. Copyright © 2014 John Wiley & Sons, Ltd.
Analysis Of Base Isolated Liquid Storage Tanks With 3D Fsi-Analysis As Well As Simplified Approaches
(2017)
Tanks are preferably designed, for cost-saving reasons, as circular, cylindrical, thin-walled shells. In case of seismic excitation, these constructions are highly vulnerable to stability failures. An earthquake-resistant design of rigidly supported tanks for high seismic loading demands, however, uneconomic wall thicknesses. A cost-effective alternative can be provided by base isolation systems. In this paper, a simplified seismic design procedure for base isolated tanks is introduced, by appropriately modifying the standard mechanical model for flexible, rigidly supported tanks. The non-linear behavior of conventional base isolation systems becomes an integral part of a proposed simplified process, which enables
the assessment of the reduced hydrodynamic forces acting on the tank walls and the corresponding stress distribution. The impulsive and convective actions of the liquid are taken into account. The validity of this approach is evaluated by
employing a non-linear fluid-structure interaction algorithm of finite element method. Special focus is placed on the boundary conditions imposed from the base isolation and the resulting hydrodynamic pressures. Both horizontal and vertical
component of ground motion are considered in order to study the principal effects of the base isolation on the pressure distribution of the tank walls. The induced rocking effects associated with elastomeric bearings are discussed. The results
manifest that base isolated tanks can be designed for seismic loads by means of the proposed procedure with sufficient accuracy, allowing to dispense with numerically expensive techniques.
Analysis of Big Data Streams to obtain Braking Reliability Information for Train Protection systems
(2017)
With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Silos generally work as storage structures between supply and demand for various goods, and their structural safety has long been of interest to the civil engineering profession. This is especially true for dynamically loaded silos, e.g., in case of seismic excitation. Particularly thin-walled cylindrical silos are highly vulnerable to seismic induced pressures, which can cause critical buckling phenomena of the silo shell. The analysis of silos can be carried out in two different ways. In the first, the seismic loading is modeled through statically equivalent loads acting on the shell. Alternatively, a time history analysis might be carried out, in which nonlinear phenomena due to the filling as well as the interaction between the shell and the granular material are taken into account. The paper presents a comparison of these approaches. The model used for the nonlinear time history analysis considers the granular material by means of the intergranular strain approach for hypoplasticity theory. The interaction effects between the granular material and the shell is represented by contact elements. Additionally, soil–structure interaction effects are taken into account.
Autoradiography is a well-established method of nuclear imaging. When different radionuclides are present simultaneously, additional processing is needed to distinguish distributions of radionuclides. In this work, a method is presented where aluminium absorbers of different thickness are used to produce images with different cut-off energies. By subtracting images pixel-by-pixel one can generate images representing certain ranges of β-particle energies. The method is applied to the measurement of irradiated reactor graphite samples containing several radionuclides to determine the spatial distribution of these radionuclides within pre-defined energy windows. The process was repeated under fixed parameters after thermal treatment of the samples. The greyscale images of the distribution after treatment were subtracted from the corresponding pre-treatment images. Significant changes in the intensity and distribution of radionuclides could be observed in some samples. Due to the thermal treatment parameters the most significant differences were observed in the ³H and ¹⁴C inventory and distribution.