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Polyether and polyether/ester based TPU (thermoplastic polyurethanes) were investigated with wide-angle XRD (X-ray diffraction) and SAXS (small angle X-ray scattering). Furthermore, SAXS measurements were performed in the temperature range of 30 °C to 130 °C. Polyether based polymers exhibit only one broad diffraction signal in a region of 2 θ 15° to 25°. In case of polyurethanes with ether/ester modification, the broad diffraction signal arises with small sharp diffraction signals. SAXS measurements of polymers reveal the size and shape of the crystalline zones of the polymer. Between 30 °C and 130 °C the size of the crystalline zone changes significantly. The size decreases in most of investigated TPU. In the case of Desmopan 9365D an increase of the particle size was observed.
In this study, we investigate the thermo-mechanical relaxation and crystallization behavior of polyethylene using mesoscale molecular dynamics simulations. Our models specifically mimic constraints that occur in real-life polymer processing: After strong uniaxial stretching of the melt, we quench and release the polymer chains at different loading conditions. These conditions allow for free or hindered shrinkage, respectively. We present the shrinkage and swelling behavior as well as the crystallization kinetics over up to 600 ns simulation time. We are able to precisely evaluate how the interplay of chain length, temperature, local entanglements and orientation of chain segments influences crystallization and relaxation behavior. From our models, we determine the temperature dependent crystallization rate of polyethylene, including crystallization onset temperature.
Solar energy is one option to serve the rising global energy demand with low environmental impact.1 Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections. Clouds are moving on a short term timescale and have a high influence on the available solar radiation, as they absorb, reflect and scatter parts of the incoming light.2 However, the impact of cloudiness on photovoltaic power yields (PV) and cloud induced deviations from average yields might vary depending on the technology, location and time scale under consideration.
Introduction of Matrix-Filler Adhesion to Modelling of Elastic Moduli of Particulate Composites
(2022)
Cube in cube elementary volume (EV) concept serves to predict a filler-content dependent Young´s moduli of particle filled composites using moduli of a matrix EM and a filler EF. Paul and Ishai-Cohen derived formulas for composites moduli considering different load transfer boundaries in the EV assuming a complete filler-matrix adhesion. In this paper it is confirmed that their models represent the upper and lower bounds, respectively, with the respect to the experimental data. However, in vast majority of composites a filler-matrix adhesion is not complete. Therefore, an adhesion factor kadh gaining values between 0 and 1 was introduced into Paul´s model to consider the reduced adhesion as the reduction of the filler-matrix contact area for glass beads filled in polar and unpolar thermoplastic matrices as well as elastomer. The evaluation of these composite systems provides reasonable adhesion coefficients of PA66 > PBT > PP > PE-LD >> BR. It was also found that stiffening only occurs if kadh exceeds the minimum value adhesion of root square of E(M) divided by E(F). The determined kadh correspond to scanning electron microscopy observations of the composites fracture surfaces. Additionally, finite element analysis of the cubic and hexagonal arrangements of the EV show that the stress distributions are different, but they affect the calculated moduli only for the filler volume contents exceeding 20 %. The introduction of the filler-matrix adhesion provides more reliable predictions of Young´s moduli of particulate composites.
The cube in cube approach was used by Paul and Ishai-Cohen to model and derive formulas for filler content dependent Young's moduli of particle filled composites assuming perfect filler matrix adhesion. Their formulas were chosen because of their simplicity, and recalculated using an elementary volume approach which transforms spherical inclusions to cubic inclusions. The EV approach led to expression of the composites moduli that allows introducing an adhesion factor kadh ranging from 0 and 1 to take into account reduced filler matrix adhesion. This adhesion factor scales the edge length of the cubic inclusions, thus reducing the stress transfer area between matrix and filler. Fitting the experimental data with the modified Paul model provides reasonable kadh for PA66, PBT, PP, PE-LD and BR which are in line with their surface energies. Further analysis showed that stiffening only occurs if kadh exceeds [Formula: see text] and depends on the ratio of matrix modulus and filler modulus. The modified model allows for a quick calculation of any particle filled composites for known matrix modulus EM, filler modulus EF, filler volume content vF and adhesion factor kadh. Thus, finite element analysis (FEA) simulations of any particle filled polymer parts as well as materials selection are significantly eased. FEA of cubic and hexagonal EV arrangements show that stress distributions within the EV exhibit more shear stresses if one deviates from the cubic arrangement. At high filler contents the assumption that the property of the EV is representative for the whole composite, holds only for filler volume contents up to 15 or 20% (corresponding to 30 to 40 weight %). Thus, for vast majority of commercially available particulate composites, the modified model can be applied. Furthermore, this indicates that the cube in cube approach reaches two limits: (i) the occurrence of increasing shear stresses at filler contents above 20% due to deviations of EV arrangements or spatial filler distribution from cubic arrangements (singular), and (ii) increasing interaction between particles with the formation of particle network within the matrix violating the EV assumption of their homogeneous dispersion.
The cube in cube approach was used by Paul and Ishai-Cohen to model and derive formulas for filler content dependent Young´s moduli of particle filled composites assuming perfect filler matrix adhesion. Their formulas were chosen because of their simplicity, recalculated using an elementary volume approach which transforms spherical inclusions to cubic inclusions. The EV approach led to expression for the composites moduli that allow for introducing an adhesion factor kadh ranging from 0 and 1 to take into account none perfect reduced filler matrix adhesion. This adhesion factor scales the edge length of the cubic inclusions, thus, reducing the stress transfer area between matrix and filler. Fitting the experimental data with the modified Paul model provides reasonable kadh for PA66, PBT, PP, PE-LD and BR which are in line with their surface energies. Further analysis showed that stiffening only occurs if kadh exceeds <span class="math-tex">\( { \ \sqrt{E^M/E^F} \ }\) and depends on the ratio of matrix modulus and filler modulus. The modified model allows for a quick calculation of any particle filled composites for known matrix modulus EM, filler modulus EF, filler volume content vF and adhesion factor kadh. Thus, finite element analysis (FEA) simulations of any particle filled polymer parts as well as materials selection are significantly eased. FEA of cubic and hexagonal EV arrangements show that stress distributions within the EV exhibit more shear stresses if one deviates from the cubic arrangement. At high filler contents the assumption that the property of the EV is representative for the whole composite, holds only for filler volume contents up to 15 or 20 % (corresponding to 30 to 40 weight %). Thus, for vast majority of commercially available particulate composites, the modified model can be applied. Furthermore, this indicates that the cube in cube approach reaches two limits: i) the occurrence of increasing shear stresses at filler contents above 20 % due to deviations of EV arrangements or spatial filler distribution from cubic arrangements (singular), and ii) increasing interaction between particles with the formation of particle network within the matrix violating the EV assumption of their homogeneous dispersion.
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.
This study investigates the initial stage of the thermo-mechanical crystallization behavior for uni- and biaxially stretched polyethylene. The models are based on a mesoscale molecular dynamics approach. We take constraints that occur in real-life polymer processing into account, especially with respect to the blowing stage of the extrusion blow-molding process. For this purpose, we deform our systems using a wide range of stretching levels before they are quenched. We discuss the effects of the stretching procedures on the micro-mechanical state of the systems, characterized by entanglement behavior and nematic ordering of chain segments. For the cooling stage, we use two different approaches which allow for free or hindered shrinkage, respectively. During cooling, crystallization kinetics are monitored: We precisely evaluate how the interplay of chain length, temperature, local entanglements and orientation of chain segments influence crystallization behavior. Our models reveal that the main stretching direction dominates microscopic states of the different systems. We are able to show that crystallization mainly depends on the (dis-)entanglement behavior. Nematic ordering plays a secondary role.
Bonding wires made of aluminum are the most used materials for the transmission of electrical signals in power electronic devices. During operation, different cyclic mechanical and thermal stresses can lead to fatigue loads and a failure of the bonding wires. A prediction or prevention of the wire failure is not yet possible by design for all cases. The following work presents meaningful fatigue tests in small wire dimensions and investigates the influence of the R-ratio on the lifetime of two different aluminum wires with a diameter of 300 μm each. The experiments show very reproducible fatigue results with ductile failure behavior. The endurable stress amplitude decreases linearly with an increasing stress ratio, which can be displayed by a Smith diagram, even though the applied maximum stresses exceed the initial yield stresses determined by tensile tests. A scaling of the fatigue results by the tensile strength indicates that the fatigue level is significantly influenced by the strength of the material. Due to the very consistent findings, the development of a generalized fatigue model for predicting the lifetime of bonding wires with an arbitrary loading situation seems to be possible and will be further investigated.
West Africa has great potential for the use of solar energy systems, as it has both a high solar radiation rate and a lack of energy production. West Africa is a very aerosol-rich region, whose effects on photovoltaic (PV) use are due to both atmospheric conditions and existing solar technology. This study reports the variability of aerosol optical properties in the city of Koforidua, Ghana over the period 2016 to 2020, and their impact on the radiation intensity and efficiency of a PV cell. The study used AERONET ground (Giles et al., 2019) and satellite data produced by CAMS (Gschwind, et al., 2019), which both provide aerosol optical depth (AOD) and metrological parameters used for radiative transfer calculations with libRadtran (Emde, et al., 2016). A spectrally resolved PV model (Herman-Czezuch et al., 2022) is then used to calculate the PV yield of two PV technologies: polycrystalline and amorphous silicon. It is observed that for both data sets, the aerosol is mainly composed of dust and organic matter, with a very increased AOD load during the harmattan period (December-February), also due to the fires observed during this period.
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.
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pre-training methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multi-task pre-training in this context.
In contrast to the German power supply, the energy supply in many West African countries is very unstable. Frequent power outages are not uncommon. Especially for critical infrastructures, such as hospitals, a stable power supply is vital. To compensate for the power outages, diesel generators are often used. In the future, these systems will increasingly be supplemented by PV systems and storage, so that the generator will have to be used less or not at all when needed. For the design and operation of such systems, it is necessary to better understand the atmospheric variability of PV power generation. For example, there are large variations between rainy and dry seasons, between days with high and low dust levels - caused by sandstorms (harmattan) or urban air pollution.
Background & Objective: Due to the policy goals for sustainable energy production, renewable energy plants such as photovoltaics are increasingly in use. The energy production from solar radiation depends strongly on atmospheric conditions. As the weather mostly changes, electrical power generation fluctuates, making technical planning and control of power grids to a complex problem. Due to used materials (semiconductors e.g. silicon, gallium arsenide, cadmium telluride) the photovoltaic cells are spectrally selective. It means that only radiation of certain wavelengths converts into electrical energy. A material property called spectral response characterizes a certain degree of conversion of solar radiation into the electric current for each wavelength of solar light.