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TREE Jahresbericht 2017
(2018)
Knapp fünf Jahre nach Gründung als Fachbereichsinstitut und zwei Jahre nach Verankerung als zentrale wissenschaftliche Einrichtung der Hochschule präsentieren wir - nicht ganz ohne Stolz - den ersten Jahresbericht des Instituts TREE. Er soll in seiner Breite als auch in seiner Tiefe die Stärken unserer gemeinschaftlichen Anstrengungen im Forschungsfeld der nachhaltigen Technologien aufzeigen: interdisziplinär, forschungsstark, nachwuchsfördernd und gesellschaftszugewandt. TREE ist weiterhin ein im Aufbruch begriffenes Institut, aber gerade das Jahr 2017 zeigt auch, dass wir uns schon in der Wissenschaftslandkarte einen Namen machen konnten: nach NaWETec konnte mit dem Themenkomplex "Effiziente Transportalternativen" ein zweiter Forschungsschwerpunkt drittmittelgefördert etabliert werden. Erste Promotionen im Rahmen des TREE konnten erfolgreich abgeschlossen und interessante Nachwuchswissenschaftler für "FHKarrierewege" gewonnen werden.
Design and Analysis of an OFDM-Based Orthogonal Chaotic Vector Shift Keying Communication System
(2018)
We propose a new non-coherent multicarrier spread-spectrum system that combines orthogonal chaotic vector shift keying (OCVSK) and orthogonal frequency-division multiplexing (OFDM). The system enhances OCVSK by sending multiple groups of information sequences with the same orthogonal chaotic vector reference sequences over the selected subcarriers. Each group carries M information bits and is separated from other groups by orthogonal chaotic reference signals. We derive the information rate enhancement (IRE) and the energy saving enhancement (ESE) factors as well as the bit error rate theory of OFDM-OCVSK under additive white Gaussian noise and multipath Rayleigh fading channels and compare the results with conventional OCVSK systems. For large group numbers, the results show that the IRE and ESE factors approachM×100% andM/(M+1)×100%, respectively, and thus outperform OCVSK systems. The complexity analysis of the proposed scheme as compared with OFDM-DCSK shows a significant reduction in the number of complex multiplications required.
Bei Thymian (Thymus vulgaris) handelt es sich um eine sehr varietätenreiche Art, die aufgrund ihres Gehaltes an therapeutisch wirksamen Inhaltsstoffen als Arzneipflanze monographiert ist. Insbesondere das ätherische Öl mit dem Hauptbestandteil Thymol (ca. 50%) hat eine hohe antioxidative Wirkung. Ziel ist es, dieses Potential als nachhaltig produzierte Additive zu nutzen. Hierfür eignen sich antioxidativ bzw. antimikrobiell wirksame sowie UV-absorbierende Substanzen, die das Produkt bei Zusatz vor oxidativem Stress, mikrobiellem Abbau und Qualitätsverlust schützen.
Hierzu werden zunächst sechs Varianten auf verschiedene Parameter analysiert, um die potenteste Variante auszuwählen. Auf diese Variante wird sich die weitere Forschung konzentrieren.
Daher wird das ätherische Öl durch azeotrope Destillation extrahiert und mittels GCMS analysiert. In Extrakten werden zudem das AP und Absorptionsverhalten bestimmt. Auch die chemische Zusammensetzung des Extrakts sowie die flüchtigen Stoffe des Thymians werden untersucht. Generell gibt es wenig qualitative, teilweise jedoch quantitative Unterschiede: Eine Variante weist u.a. einen deutlich höheren Thymolgehalt im Öl (ca. 65 %) und ein hohes hydrophiles AP auf. Somit ist eine vielversprechende Variante für die weitere Entwicklung und Optimierung bioaktiver Additive gefunden.
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
(2018)
The present article presents a summarizing view at differential-algebraic equations (DAEs) and analyzes how new application fields and corresponding mathematical models lead to innovations both in theory and in numerical analysis for this problem class. Recent numerical methods for nonsmooth dynamical systems subject to unilateral contact and friction illustrate the topicality of this development.
The elucidation of conformations and relative potential energies (rPEs) of small molecules has a long history across a diverse range of fields. Periodically, it is helpful to revisit what conformations have been investigated and to provide a consistent theoretical framework for which clear comparisons can be made. In this paper, we compute the minima, first- and second-order saddle points, and torsion-coupled surfaces for methanol, ethanol, propan-2-ol, and propanol using consistent high-level MP2 and CCSD(T) methods. While for certain molecules more rigorous methods were employed, the CCSD(T)/aug-cc-pVTZ//MP2/aug-cc-pV5Z theory level was used throughout to provide relative energies of all minima and first-order saddle points. The rPE surfaces were uniformly computed at the CCSD(T)/aug-cc-pVTZ//MP2/aug-cc-pVTZ level. To the best of our knowledge, this represents the most extensive study for alcohols of this kind, revealing some new aspects. Especially for propanol, we report several new conformations that were previously not investigated. Moreover, two metrics are included in our analysis that quantify how the selected surfaces are similar to one another and hence improve our understanding of the relationship between these alcohols.
The use of wearable devices or “wearables” in the physical activity domain has been increasing in the last years. These devices are used as training tools providing the user with detailed information about individual physiological responses and feedback to the physical training process. Advantages in sensor technology, miniaturization, energy consumption and processing power increased the usability of these wearables. Furthermore, available sensor technologies must be reliable, valid, and usable. Considering the variety of the existing sensors not all of them are suitable to be integrated in wearables. The application and development of wearables has to consider the characteristics of the physical training process to improve the effectiveness and efficiency as training tools. During physical training, it is essential to elicit individual optimal strain to evoke the desired adjustments to training. One important goal is to neither overstrain nor under challenge the user. Many wearables use heart rate as indicator for this individual strain. However, due to a variety of internal and external influencing factors, heart rate kinetics are highly variable making it difficult to control the stress eliciting individually optimal strain. For optimal training control it is essential to model and predict individual responses and adapt the external stress if necessary. Basis for this modeling is the valid and reliable recording of these individual responses. Depending on the heart rate kinetics and the obtained physiological data, different models and techniques are available that can be used for strain or training control. Aim of this review is to give an overview of measurement, prediction, and control of individual heart rate responses. Therefore, available sensor technologies measuring the individual heart rate responses are analyzed and approaches to model and predict these individual responses discussed. Additionally, the feasibility for wearables is analyzed.
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a two-dimensional airfoil optimization problem SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic three-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.
Influence of design of extrusion blow molding (EBM) in terms of extrusion direction set-up and draw ratio as well as process conditions (mold temperature) on storage modulus of high density polyethylene EBM containers was analyzed with dynamic mechanical analysis. All three parameters - mold temperature, flow direction and draw ratio - are statistically significant and lead to relative and absolute evaluation of storage modulus. Furthermore, flow induced changes in crystallinity was analyzed by differential scanning calorimetry. Obtained data on deformation properties can be employed for more sophisticated finite element simulations with the aim to reach more sustainable extrusion blow molding production.
Transition point prediction in a multicomponent lattice Boltzmann model: Forcing scheme dependencies
(2018)
Die im Folgenden dargestellten wichtigsten Ergebnisse des Teilprojektes 5 "Mathematische Beschreibung der relevanten physikalischen Prozesse und numerische Simulation von Wasseraufbereitung und -verteilung" beziehen sich auf die Arbeitspakete 2 "Daten und Methoden zum Modellaufbau, zur Zustandsschätzung, Prognose und Bewertung" und 3 "Physikalische Modelle und Numerische Verfahren".
Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.
Solar energy plants are one of the key options to serve the rising global energy need with low environmental impact. Aerosols reduce global solar radiation due to absorption and scattering and therewith solar energy yields. Depending on the aerosol composition and size distribution they reduce the direct component of the solar radiation and modify the direction of the diffuse component compared to standard atmospheric conditions without aerosols.