Refine
H-BRS Bibliography
- yes (50)
Departments, institutes and facilities
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (50) (remove)
Document Type
- Article (25)
- Conference Object (19)
- Part of a Book (2)
- Doctoral Thesis (2)
- Contribution to a Periodical (1)
- Part of Periodical (1)
Year of publication
- 2019 (50) (remove)
Keywords
- lignin (4)
- Extrusion blow molding (2)
- UAV (2)
- aerodynamics (2)
- antimicrobial activity (2)
- bone tissue engineering (2)
- chemometrics (2)
- dynamic vector fields (2)
- evaluation (2)
- flight zone (2)
- geofence (2)
- injection moulding (2)
- learning outcomes (2)
- modeling of complex systems (2)
- ACPYPE (1)
- ANN controller (1)
- Adams-Moulton (1)
- Aircraft (1)
- Antioxidant activity (1)
- B-splines (1)
- BDF (1)
- Basiswerkstoff (1)
- Benetzbarkeit (1)
- Biomass (1)
- CPACS (1)
- Carbohydrate (1)
- Crystallinity (1)
- Cybersecurity (1)
- Draw ratio (1)
- European horse chestnut (1)
- Exergame (1)
- Flow direction (1)
- Folin–Ciocalteu assay (1)
- Force field (1)
- Führungspositionen (1)
- Geometry (1)
- Gitter-Boltzmann-Methode (1)
- Glycam06 (1)
- Gordon surface (1)
- Grid Control (1)
- Grid Stability (1)
- Gromacs (1)
- HSQC NMR (1)
- Heart Rate Prediction (1)
- Horizontale und vertikale Segmentation (1)
- Indirect Encodings (1)
- Information and Communication Technologies (1)
- Integrative simulation (1)
- Internet of Things (1)
- Inverter (1)
- Journalistinnen (1)
- Kontaktwinkel (1)
- Kraft lignin (1)
- Lattice Boltzmann method (1)
- Lignocellulose feedstock (1)
- MAP-Elites (1)
- MOOC (1)
- Medienproduktion (1)
- Miscanthus (1)
- Miscanthus x giganteus (1)
- Mold temperature (1)
- Naturkautschuk (1)
- Neuroevolution (1)
- Nonbonded scaling factor (1)
- Organosolv lignin (1)
- Orthotropic material behavior (1)
- Paulownia (1)
- Phasenübergang (1)
- Prediction of physiological responses to strain (1)
- Process dependent material parameters (1)
- Quality Diversity (1)
- Quotierung (1)
- Rheometer (1)
- Shan-Chen (1)
- Silphium (1)
- Storage modulus (1)
- Taylor-Green (1)
- Total phenol content (1)
- UV spectrum (1)
- Vulkanisation (1)
- V˙CO2 prediction (1)
- V˙O2 prediction (1)
- West Africa (1)
- Western Africa (1)
- Wireless sensor networks (1)
- active packaging (1)
- additive (1)
- advanced applications (1)
- agarose (1)
- angiogenesis (1)
- antimicrobial (1)
- antioxidant (1)
- antiradical activity (1)
- applications (1)
- artificial neural networks (1)
- atmospheric aerosol (1)
- biobased (1)
- biocomposite (1)
- biomass (1)
- brilliant green (1)
- brushless motors (1)
- bulk and local viscoelastic properties (1)
- cell harvesting (1)
- cell migration (1)
- chitosan (1)
- coefficient of thermal expansion (1)
- collaborative learning (1)
- composites (1)
- control (1)
- cross-disciplinary (1)
- cross-evaluation (1)
- crystal violet (1)
- demethylation (1)
- diversity (1)
- drone video quality (1)
- drug release (1)
- endothelial cells (1)
- energy efficiency (1)
- energy meteorology (1)
- extraction (1)
- extrusion blow molding (1)
- first-semester students (1)
- fitness-fatigue model (1)
- food loss (1)
- gamification (1)
- gas transport networks (1)
- genotype (1)
- globally convergent solvers (1)
- hands-on experiences (1)
- holistic learning (1)
- hydrogel (1)
- hydroxyapatite (1)
- hydroxypropylmethylcellulose (1)
- intercultural learning (1)
- international (1)
- international teams (1)
- lignocellulosic feedstock (1)
- low-input crops (1)
- mathematical chemistry (1)
- mathematical modeling (1)
- microindentation (1)
- monolignol ratio (1)
- motor drive (1)
- multidisciplinary (1)
- multistep (1)
- multivariate data analysis (1)
- observational data and simulations (1)
- organosolv (1)
- osteogenesis (1)
- pathogenic microorganisms (1)
- performance modeling (1)
- performance prediction (1)
- permanent magnet motors (1)
- plastic manufacturing (1)
- polyphenols (1)
- polysaccharide (1)
- polyurethane coatings (1)
- practical learning (1)
- principal component analysis (1)
- proanthocyanidins (1)
- processing-structure-property relationship (1)
- project-based learning (1)
- prototype apparatus (1)
- radio transceivers (1)
- scratch assay (1)
- seed coat (1)
- self-assessment (1)
- sensor systems (1)
- serious games (1)
- size exclusion chromatography (1)
- smart agriculture (1)
- solar power (1)
- stability (1)
- stem cells (1)
- sustainability (1)
- temperature control (1)
- temporal discretization (1)
- time integration (1)
- topological reduction (1)
- training performance relationship (1)
- trapezoidal rule (1)
- triphenylmethane dyes (1)
- welfare technology (1)
- wound healing assay (1)
TREE Jahresbericht 2018
(2019)
Herein we report an update to ACPYPE, a Python3 tool that now properly converts AMBER to GROMACS topologies for force fields that utilize nondefault and nonuniform 1–4 electrostatic and nonbonded scaling factors or negative dihedral force constants. Prior to this work, ACPYPE only converted AMBER topologies that used uniform, default 1–4 scaling factors and positive dihedral force constants. We demonstrate that the updated ACPYPE accurately transfers the GLYCAM06 force field from AMBER to GROMACS topology files, which employs non-uniform 1–4 scaling factors as well as negative dihedral force constants. Validation was performed using β-d-GlcNAc through gas-phase analysis of dihedral energy curves and probability density functions. The updated ACPYPE retains all of its original functionality, but now allows the simulation of complex glycomolecular systems in GROMACS using AMBER-originated force fields. ACPYPE is available for download at https://github.com/alanwilter/acpype.
This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board.
The paper presents the topological reduction method applied to gas transport networks, using contraction of series, parallel and tree-like subgraphs. The contraction operations are implemented for pipe elements, described by quadratic friction law. This allows significant reduction of the graphs and acceleration of solution procedure for stationary network problems. The algorithm has been tested on several realistic network examples. The possible extensions of the method to different friction laws and other elements are discussed.
In mathematical modeling by means of performance models, the Fitness-Fatigue Model (FF-Model) is a common approach in sport and exercise science to study the training performance relationship. The FF-Model uses an initial basic level of performance and two antagonistic terms (for fitness and fatigue). By model calibration, parameters are adapted to the subject’s individual physical response to training load. Although the simulation of the recorded training data in most cases shows useful results when the model is calibrated and all parameters are adjusted, this method has two major difficulties. First, a fitted value as basic performance will usually be too high. Second, without modification, the model cannot be simply used for prediction. By rewriting the FF-Model such that effects of former training history can be analyzed separately – we call those terms preload – it is possible to close the gap between a more realistic initial performance level and an athlete's actual performance level without distorting other model parameters and increase model accuracy substantially. Fitting error of the preload-extended FF-Model is less than 32% compared to the error of the FF-Model without preloads. Prediction error of the preload-extended FF-Model is around 54% of the error of the FF-Model without preloads.
Atmospheric aerosols affect the power production of solar energy systems. Their impact depends on both the atmospheric conditions and the solar technology employed. By being a region with a lack in power production and prone to high solar insolation, West Africa shows high potential for the application of solar power systems. However, dust outbreaks, containing high aerosol loads, occur especially in the Sahel, located between the Saharan desert in the north and the Sudanian Savanna in the south. They might affect the whole region for several days with significant effects on power generation. This study investigates the impact of atmospheric aerosols on solar energy production for the example year 2006 making use of six well instrumented sites in West Africa. Two different solar power technologies, a photovoltaic (PV) and a parabolic through (PT) power plant, are considered. The daily reduction of solar power due to aerosols is determined over mostly clear-sky days in 2006 with a model chain combining radiative transfer and technology specific power generation. For mostly clear days the local daily reduction of PV power (at alternating current) (PVAC) and PT power (PTP) due to the presence of aerosols lies between 13 % and 22 % and between 22 % and 37 %, respectively. In March 2006 a major dust outbreak occurred, which serves as an example to investigate the impact of an aerosol extreme event on solar power. During the dust outbreak, daily reduction of PVAC and PTP of up to 79 % and 100 % occur with a mean reduction of 20 % to 40 % for PVAC and of 32 % to 71 % for PTP during the 12 days of the event.
This study presents a microindentation system which allows spatially resolved local as well as bulk viscoelastic material information to be obtained within one instrument. The microindentation method was merged with dynamic mechanical analysis (DMA) for a tungsten cone indenter. Three tungsten cone indenters were investigated: tungsten electrode, tungsten electrode + 2% lanthanum, and tungsten electrode + rare earth elements. Only the tungsten electrode + 2% lanthanum indenter showed the sinusoidal response, and its geometry remained unaffected by the repeated indentations. Complex moduli obtained from dynamic microindentation for high-density polyethylene, polybutylene terephthalate, polycarbonate, and thermoplastic polyurethane are in agreement with the literature. Additionally, by implementing a specially developed x-y-stage, this study showed that dynamic microindentation with a tungsten cone indenter was an adequate method to determine spatially resolved local viscoelastic surface properties.
Process-dependent thermo-mechanical viscoelastic properties and the corresponding morphology of HDPE extrusion blow molded (EBM) parts were investigated. Evaluation of bulk data showed that flow direction, draw ratio, and mold temperature influence the viscoelastic behavior significantly in certain temperature ranges. Flow induced orientations due to higher draw ratio and higher mold temperature lead to higher crystallinities. To determine the local viscoelastic properties, a new microindentation system was developed by merging indentation with dynamic mechanical analysis. The local process-structure-property relationship of EBM parts showed that the cross-sectional temperature distribution is clearly reflected by local crystallinities and local complex moduli. Additionally, a model to calculate three-dimensional anisotropic coefficients of thermal expansion as a function of the process dependent crystallinity was developed based on an elementary volume unit cell with stacked layers of amorphous phase and crystalline lamellae. Good agreement of the predicted thermal expansion coefficients with measured ones was found up to a temperature of 70 °C.
It is shown that the electrochemical kinetics of alkaline methanol oxidation can be reduced by setting certain fast reactions contained in it to a steady state. As a result, the underlying system of Ordinary Differential Equations (ODE) is transformed to a system of Differential-Algebraic Equations (DAE). We measure the precision characteristics of such transformation and discuss the consequences of the obtained model reduction.
Pseudopotential (PP)-basierte Lattice-Boltzmann-Methoden werden zunehmend für die Simulation von Mehrphasenströmungen eingesetzt. Da sie auf einem phänomenologischen Ansatz basieren, ist ihr Einsatz mit einem hohen Modellierungsaufwand verbunden. Zudem entstehen an den Phasengrenzen sogenannte Scheingeschwindigkeiten, welche Genauigkeit und numerische Stabilität beeinträchtigen. Daher werden PP-Modelle in dieser Arbeit um drei neue Aspekte erweitert. Erstens wird gezeigt, dass bei der Modellierung unterschiedlicher Kontaktwinkel mit gängigen Methoden in Kombination mit verbesserten Kräfteschemata Scheintröpfchen entstehen. Diese werden durch einen neuartigen Ansatz eliminiert, der auf zusätzlichen Randbedingungen für alle Wechselwirkungskräfte basiert. Diese Technik verhindert nicht nur das Auftreten der Scheintröpfchen, sondern erhöht auch die Stabilität in wandgebundenen Strömungen. Zweitens wird ein neuartiges Verfahren zur Reduktion von Scheingeschwindigkeiten eingeführt. Dabei wird die Diskretisierung der Interaktionskräfte erweitert und die zusätzlichen, freien Koeffizienten in Simulationen statischer Tropfen numerisch optimiert. Die resultierende Diskretisierung wurde in Simulationen stationärer und dynamischer Testfälle validiert, wobei Scheingeschwindigkeiten deutlich reduziert werden konnten. Drittens und letztens wurden die Diffusionseigenschaften in Mehrstoffsystemen detailliert untersucht, wobei eine kritische Abhängigkeit zwischen den makroskopischen Diffusionskoeffizienten und dem Kräfteschema aufgezeigt wird. Diese Analyse bildet die Grundlage für den Vergleich und die zukünftige Entwicklung neuer Potentialfunktionen (für Mehrstoffsysteme) und reduziert den Modellierungsaufwand.
Energy Profiles of the Ring Puckering of Cyclopentane, Methylcyclopentane and Ethylcyclopentane
(2019)
Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models can estimate the performance of unknown inputs, and so be used in place of expensive objective functions. Evolutionary techniques such as genetic programming or neuroevolution commonly alter the structure of the genome itself. A lack of consistency in the genotype is a fatal blow to data-driven modeling techniques: interpolation between points is impossible without a common input space. However, while the dimensionality of genotypes may differ across individuals, in many domains, such as controllers or classifiers, the dimensionality of the input and output remains constant. In this work we leverage this insight to embed differing neural networks into the same input space. To judge the difference between the behavior of two neural networks, we give them both the same input sequence, and examine the difference in output. This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology. In a robotic navigation task, we show that models trained using this phenotypic embedding perform as well or better as those trained on the weight values of a fixed topology neural network. We establish such phenotypic surrogate models as a promising and flexible approach which enables surrogate modeling even for representations that undergo structural changes.
Are quality diversity algorithms better at generating stepping stones than objective-based search?
(2019)
The route to the solution of complex design problems often lies through intermediate "stepping stones" which bear little resemblance to the final solution. By greedily following the path of greatest fitness improvement, objective-based search overlooks and discards stepping stones which might be critical to solving the problem. Here, we hypothesize that Quality Diversity (QD) algorithms are a better way to generate stepping stones than objective-based search: by maintaining a large set of solutions which are of high-quality, but phenotypically different, these algorithms collect promising stepping stones while protecting them in their own "ecological niche". To demonstrate the capabilities of QD we revisit the challenge of recreating images produced by user-driven evolution, a classic challenge which spurred work in novelty search and illustrated the limits of objective-based search. We show that QD far outperforms objective-based search in matching user-evolved images. Further, our results suggest some intriguing possibilities for leveraging the diversity of solutions created by QD.