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We present a new approach to the problem of optimal control of solar sails for low-thrust trajectory optimization. The objective was to find the required control torque magnitudes in order to steer a solar sail in interplanetary space. A new steering strategy, controlling the solar sail with generic torques applied about the spacecraft body axes, is integrated into the existing low-thrust trajectory optimization software InTrance. This software combines artificial neural networks and evolutionary algorithms to find steering strategies close to the global optimum without an initial guess. Furthermore, we implement a three rotational degree-of-freedom rigid-body attitude dynamics model to represent the solar sail in space. Two interplanetary transfers to Mars and Neptune are chosen to represent typical future solar sail mission scenarios. The results found with the new steering strategy are compared to the existing reference trajectories without attitude dynamics. The resulting control torques required to accomplish the missions are investigated, as they pose the primary requirements to a real on-board attitude control system.
Analysis of error and time behavior of the IEEE 802.15.4 PHY-layer in an industrial environment
(2006)
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.
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.
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.
Analysis of Big Data Streams to obtain Braking Reliability Information for Train Protection systems
(2017)
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.
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.
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.
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.
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.