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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.
In this paper, modeling of piston and generic type gas compressors for a globally convergent algorithm for solving stationary gas transport problems is carried out. A theoretical analysis of the simulation stability, its practical implementation and verification of convergence on a realistic gas network have been carried out. The relevance of the paper for the topics of the conference is defined by a significance of gas transport networks as an advanced application of simulation and modeling, including the development of novel mathematical and numerical algorithms and methods.
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.
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
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.
Modeling of Creep Behavior of Particulate Composites with Focus on Interfacial Adhesion Effect
(2022)
Evaluation of creep compliance of particulate composites using empirical models always provides parameters depending on initial stress and material composition. The effort spent to connect model parameters with physical properties has not resulted in success yet. Further, during the creep, delamination between matrix and filler may occur depending on time and initial stress, reducing an interface adhesion and load transfer to filler particles. In this paper, the creep compliance curves of glass beads reinforced poly(butylene terephthalate) composites were fitted with Burgers and Findley models providing different sets of time-dependent model parameters for each initial stress. Despite the finding that the Findley model performs well in a primary creep, the Burgers model is more suitable if secondary creep comes into play; they allow only for a qualitative prediction of creep behavior because the interface adhesion and its time dependency is an implicit, hidden parameter. As Young’s modulus is a parameter of these models (and the majority of other creep models), it was selected to be introduced as a filler content-dependent parameter with the help of the cube in cube elementary volume approach of Paul. The analysis led to the time-dependent creep compliance that depends only on the time-dependent creep of the matrix and the normalized particle distance (or the filler volume content), and it allowed accounting for the adhesion effect. Comparison with the experimental data confirmed that the elementary volume-based creep compliance function can be used to predict the realistic creep behavior 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.
Bionik
(2022)
Wie machen die das… kann angesichts der erstaunlichen Fähigkeiten mancher Lebewesen gefragt werden. Die Bionik fragt noch weiter …und wie kann man das nachmachen? Hier liegt ein Schwerpunkt dieses Lehrbuches, das die Bionik nicht nur an zahlreichen Beispielen erklärt, sondern auch eine Vorgehensweise für die Identifizierung biologischer Lösungen und deren Übertragung auf technische Anwendungen vermittelt. Basisinformationen der Biologie und Grundlagen der Konstruktionstechnik gewährleisten einen leichten Zugang zum Stoff. Mit dem 3D-Druck als Schlüsseltechnologie und der Thematisierung der Nachhaltigkeit geht das Buch zudem auf aktuelle Entwicklungen ein. Dieser ganzheitliche Blick auf die Bionik soll den Leser zur Durchführung bionischer Projekte befähigen und motivieren. Die vorliegende Auflage wurde überarbeitet und um aktuelle Forschungserkenntnisse und Entwicklungen ergänzt. (Verlagsangaben)
This paper investigates the effect of voltage sensors on the measurement of transient voltages for power semiconductors in a Double Pulse Test (DPT) environment.We adapt previously published models that were developed for current sensors and apply them to voltage sensors to evaluate their suitability for DPT applications. Similarities and differences between transient current and voltage sensors are investigated and the resulting methodology is applied to commercially available and experimental voltage sensors. Finally, a selection aid for given measurement tasks is derived that focuses on the measurement of fast-switching power semiconductors.
While many proteins are known clients of heat shock protein 90 (Hsp90), it is unclear whether the transcription factor, thyroid hormone receptor beta (TRb), interacts with Hsp90 to control hormonal perception and signaling. Higher Hsp90 expression in mouse fibroblasts was elicited by the addition of triiodothyronine (T3). T3 bound to Hsp90 and enhanced adenosine triphosphate (ATP) binding of Hsp90 due to a specific binding site for T3, as identified by molecular docking experiments. The binding of TRb to Hsp90 was prevented by T3 or by the thyroid mimetic sobetirome. Purified recombinant TRb trapped Hsp90 from cell lysate or purified Hsp90 in pull-down experiments. The affinity of Hsp90 for TRb was 124 nM. Furthermore, T3 induced the release of bound TRb from Hsp90, which was shown by streptavidin-conjugated quantum dot (SAv-QD) masking assay. The data indicate that the T3 interaction with TRb and Hsp90 may be an amplifier of the cellular stress response by blocking Hsp90 activity.
Approximately 45% of global greenhouse gas emissions are caused by the construction and use of buildings. Thermal insulation of buildings in the current context of climate change is a well-known strategy to improve the energy efficiency of buildings. The development of renewable insulation material can overcome the drawbacks of widely used insulation systems based on polystyrene or mineral wool. This study analyzes the sustainability and thermal conductivity of new insulation materials made of Miscanthus x giganteus fibers, foaming agents, and alkali-activated fly ash binder. Life cycle assessments (LCA) are necessary to perform benchmarking of environmental impacts of new formulations of geopolymer-based insulation materials. The global warming potential (GWP) of the product is primarily determined by the main binder component sodium silicate. Sodium silicate's CO2 emissions depend on local production, transportation, and energy consumption. The results, which have been published during recent years, vary in a wide range from 0.3 kg to 3.3 kg CO2-eq. kg-1. The overall GWP of the insulation system based on Miscanthus fibers, with properties according to current thermal insulation regulations, reaches up to 95% savings of CO2 emissions compared to conventional systems. Carbon neutrality can be achieved through formulations containing raw materials with carbon dioxide emissions and renewable materials with negative GWP, thus balancing CO2 emissions.
In her recent article, Bender discusses several aspects of research–practice–collaborations (RPCs). In this commentary, we apply Bender's arguments to experiences in engineering research and development (R&D). We investigate the influence of interaction with practice partners on relevance, credibility, and legitimacy in the special engineering field of product development and analyze which methodological approaches are already being pursued for dealing with diverging interests and asymmetries and which steps will be necessary to include interests of civil society beyond traditional customer relations.
This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.
In this paper, an analysis of the error ellipsoid in the space of solutions of stationary gas transport problems is carried out. For this purpose, a Principal Component Analysis of the solution set has been performed. The presence of unstable directions is shown associated with the marginal fulfillment of the resistivity conditions for the equations of compressors and other control elements in gas networks. Practically, the instabilities occur when multiple compressors or regulators try to control pressures or flows in the same part of the network. Such problems can occur, in particular, when the compressors or regulators reach their working limits. Possible ways of resolving instabilities are considered.
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 electricity grid of the future will be built on renewable energy sources, which are highly variable and dependent on atmospheric conditions. In power grids with an increasingly high penetration of solar photovoltaics (PV), an accurate knowledge of the incoming solar irradiance is indispensable for grid operation and planning, and reliable irradiance forecasts are thus invaluable for energy system operators. In order to better characterise shortwave solar radiation in time and space, data from PV systems themselves can be used, since the measured power provides information about both irradiance and the optical properties of the atmosphere, in particular the cloud optical depth (COD). Indeed, in the European context with highly variable cloud cover, the cloud fraction and COD are important parameters in determining the irradiance, whereas aerosol effects are only of secondary importance.
Intention: Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster.
Context: In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.
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.
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.
Microarray-based experiments revealed that thyroid hormone triiodothyronine (T3) enhanced the binding of Cy5-labeled ATP on heat shock protein 90 (Hsp90). By molecular docking experiments with T3 on Hsp90, we identified a T3 binding site (TBS) near the ATP binding site on Hsp90. A synthetic peptide encoding HHHHHHRIKEIVKKHSQFIGYPITLFVEKE derived from the TBS on Hsp90 showed, in MST experiments, the binding of T3 at an EC50 of 50 μM. The binding motif can influence the activity of Hsp90 by hindering ATP accessibility or the release of ADP.
Novel methods for contingency analysis of gas transport networks are presented. They are motivated by the transition of our energy system where hydrogen plays a growing role. The novel methods are based on a specific method for topological reduction and so-called supernodes. Stationary Euler equations with advanced compressor thermodynamics and a gas law allowing for gas compositions with up to 100% hydrogen are used. Several measures and plots support an intuitive comparison and analysis of the results. In particular, it is shown that the newly developed methods can estimate locations and magnitudes of additional capacities (injection, buffering, storage etc.) with a reasonable performance for networks of relevant composition and size.
The design of a fully superconducting wind power generator is influenced by several factors. Among them, a low number of pole pairs is desirable to achieve low AC losses in the superconducting stator winding, which greatly influences the cooling system design and, consecutively, the efficiency of the entire wind power plant. However, it has been identified that a low number of pole pairs in a superconducting generator tends to greatly increase its output voltage, which in turn creates challenging conditions for the necessary power electronic converter. This study highlights the interdependencies between the design of a fully superconducting 10 MW wind power generator and the corresponding design of its power electronic converter.
Process-induced changes in the morphology of biodegradable polybutylene adipate terephthalate (PBAT) and polylactic acid (PLA) blends modified with various multifunctional chainextending cross-linkers (CECLs) are presented. The morphology of unmodified and modified films produced with blown film extrusion is examined in an extrusion direction (ED) and a transverse direction (TD). While FTIR analysis showed only small peak shifts indicating that the CECLs modify the molecular weight of the PBAT/PLA blend, SEM investigations of the fracture surfaces of blown extrusion films revealed their significant effect on the morphology formed during the processing. Due to the combined shear and elongation deformation during blown film extrusion, rather spherical PLA islands were partly transformed into long fibrils, which tended to decay to chains of elliptical islands if cooled slowly. The CECL introduction into the blend changed the thickness of the PLA fibrils, modified the interface adhesion, and altered the deformation behavior of the PBAT matrix from brittle to ductile. The results proved that CECLs react selectively with PBAT, PLA, and their interface. Furthermore, the reactions of CECLs with PBAT/PLA induced by the processing depended on the deformation directions (ED and TD), thus resulting in further non-uniformities of blown extrusion films.
Typically, plastic packaging materials are produced using additives, like e.g. stabilisers, to introduce specific desired properties into the material or, in case of stabilisers, to prolong the shelf life of such packaging materials. However, those stabilisers are typically fossil-based and can pose risks to both environmental and human health. Therefore, the present study presents more sustainable alternatives based on regional renewable resources which show the relevant antioxidant, antimicrobial and UV absorbing properties to successfully serve as a plastic stabiliser. In the study, all plants are extracted and characterised with regard to not only antioxidant, antimicrobial and UV absorbing effects, but also with regard to additional relevant information like chemical constituents, molar mass distribution, absorbance in the visible range et cetera. The extraction process is furthermore optimised and, where applicable, reasonable opportunities for waste valorisation are explored and analysed. Furthermore, interactions between analysed plant extracts are described and model films based on Poly-Lactic Acid are prepared, incorporating analysed plant extracts. Based on those model films, formulation tests and migration analysis according to EU legislation is conducted.
The well-known aromatic and medicinal plant thyme (Thymus vulgaris L.) includes phenolic terpenoids like thymol and carvacrol which have strong antioxidant, antimicrobial and UV absorbing effects. Analyses show that those effects can be used in both lipophilic and hydrophilic surroundings, that the variant Varico 3 is a more potent cultivar than other analysed thyme variants, and that a passive extraction setup can be used for extract preparation while distillation of the Essential Oils can be a more efficient approach.
Macromolecular antioxidant polyphenols, particularly proanthocyanidins, have been found in the seed coats of the European horse chestnut (Aesculus hippocastanum L.) which are regularly discarded in phytopharmaceutical industry. In this study, such effects and compounds have been reported for the first time while a valorisation of waste materials has been analysed successfully. Furthermore, a passive extraction setup for waste materials and whole seeds has been developed. In extracts of snowdrops, precisely Galanthus elwesii HOOK.F., high concentrations of tocopherol have been found which promote a particularly high antioxidant capacity in lipophilic surroundings. Different coniferous woods (Abies div., Picea div.) which are in use as Christmas trees are extracted after separating the biomass in leafs and wood parts before being analysed regarding extraction optimisation and drought resistance of active substances. Antioxidant and UV absorbing proanthocyanidins are found even in dried biomasses, allowing the circular use of already used Christmas trees as bio-based stabilisers and the production of sustainable paper as a byproduct.
The utilization of simulation procedures is gaining increasing attention in the product development of extrusion blow molded parts. However, some simulation steps, like the simulation of shrinkage and warpage, are still associated with uncertainties. The reason for this is on the one hand a lack of standardized interfaces for the transfer of simulation data between different simulation tools, and on the other hand the complex time-, temperature- and process-dependent material behavior of the used semi crystalline polymers. Using a new vendor neutral interface standard for the data transfer, the shrinkage analysis of a simple blow molded part is investigated and compared to experimental data. A linear viscoelastic material model in combination with an orthotropic process- and temperature-dependent thermal expansion coefficient is used for the shrinkage prediction. A good agreement is observed. Finally, critical parameters in the simulation models that strongly influence the shrinkage analysis are identified by a sensitivity study.
Jet engines of airplanes are designed such that in some components damage occurs and accumulates in service without being critical up to a certain level of damage. Since maintenance, repair, and component exchange are very cost-intensive, it is necessary to predict efficiently the component lifetime with high accuracy. A former developed lifetime model, based on interpolated results of aerodynamic and structural mechanics simulations, uses material parameters estimated from literature values of standard creep experiments. For improved accuracy, an experimental procedure is developed for the characterization of the short-time creep behavior, which is relevant for the operation of turbine blades of jet engines. To consider microstructural influences resulting from the manufacturing of thin-walled single crystal turbine blades, small-scale specimens from used turbine blades are extracted and tested in short- and medium-time creep experiments. Based on experimental results and literature values, a creep model, which describes the fracture behavior for a wide range of creep loads, is calibrated and is now used for the lifetime prediction of turbine blades under real loading conditions.
Characterization methods of pressure sensitive adhesives (PSA) originate from technical bonding and do not cover relevant data for the development and quality assurance of medical applications, where PSA with flexible backing layers are adopted to human skin. In this study, a new method called RheoTack is developed to determine (mechanically and optically) an adhesion and detaching behavior of flexible and transparent PSA based patches. Transdermal therapeutic systems (TTS) consisting of silicone-based PSAs on a flexible and transparent backing layer were tested on a rotational rheometer with an 8 mm plate as a probe rod at retraction speeds of 0.01, 0.1, and 1 mm/s with respect to their adhesion and detaching behavior in terms of force-retraction displacement curves. The curves consist of a compression phase to affirm wetting; a tensile deformation phase intercepting stretching, cavity, and fibril formation; and a failure phase with detaching. Their analysis provides values for stiffness, force, and displacement of the beginning of fibril formation, force and displacement of the beginning of a failure due to fibril breakage and detaching, as well as corresponding activation energies. All these parameters exhibit the pronounced dependency on the retraction speed. The force-retraction displacement curves together with the simultaneous video recordings of the TTS deformation from three different angles (three cameras) provide deeper insight into the deformation processes and allow for interpreting the properties’ characteristics for PSA applications.
Comparing Armature Windings for a 10 MW Fully Superconducting Synchronous Wind Turbine Generator
(2022)
How self-reliant Peer Teaching can be set up to augment learning outcomes for university learners
(2022)
In this paper, a gas-to-power (GtoP) system for power outages is digitally modeled and experimentally developed. The design includes a solid-state hydrogen storage system composed of TiFeMn as a hydride forming alloy (6.7 kg of alloy in five tanks) and an air-cooled fuel cell (maximum power: 1.6 kW). The hydrogen storage system is charged under room temperature and 40 bar of hydrogen pressure, reaching about 110 g of hydrogen capacity. In an emergency use case of the system, hydrogen is supplied to the fuel cell, and the waste heat coming from the exhaust air of the fuel cell is used for the endothermic dehydrogenation reaction of the metal hydride. This GtoP system demonstrates fast, stable, and reliable responses, providing from 149 W to 596 W under different constant as well as dynamic conditions. A comprehensive and novel simulation approach based on a network model is also applied. The developed model is validated under static and dynamic power load scenarios, demonstrating excellent agreement with the experimental results.