Oel-Waerme-Institut

The operational life of offshore wind turbines is in part driven by the fatigue life of key structural components, such as the substructure. In recent years, fatigue life management of operational assets has become evermore important, as older farms are closing on their design lifetime and newer farms are designed with tighter margins. To support decisions on fatigue lifetime, it is advantageous to monitor the fatigue progression in these structures through SHM. However, a full instrumentation of every asset in a farm to assess the fatigue life of the substructure is considered economically infeasible. The drive for SHM in wind has been accompanied by the increased availability of intelligent data-driven methodologies which have attempted to provide the same information without the need for additional hardware. One such cost-effective approach, as described in [1], uses the available supervisory control and data acquisition (SCADA) systems, coupled with acceleration measurements to predict the fatigue life of an offshore wind turbine. The use of acceleration measurement data has been proven critical for capturing the complex dynamics of offshore wind turbines. In this contribution, we present the results of said data-driven approach using SCADA and Internet of Things (IoT) accelerometer installed at nacelle-level to monitor the fatigue life for the entirety of a real-world offshore wind farm comprised of 23 turbines, with a specific focus on long-term damage equivalent fatigue loads (DEL) estimation [2]. The availability of acceleration measurements for all locations in particular, is fundamental, as to cover all possible differences in natural frequencies between turbines would be nearly impossible if solely relying on wave and tidal data. To achieve this goal, a neural network architecture is used, enhanced by physics-informed learning focused on long-term estimation, trained and validated on so-called fleet-leader (three turbines instrumented with strain gauges, which provide the ground truth). In this study, we pay special attention to the farm-wide validation, cross-validation and extrapolation of these models as well as the performance for different operational conditions. Finally, this study is undertook for a real-world instrumentation setup, unparalleled in its scale, and can thus be indicative of future trends for SHM in offshore wind: farm-wide instrumentation and monitoring of structural health based on acceleration measurements, enabling a greater trustworthiness on reliability and durability estimation over the serviceable lifetime.

The public funded project “AddSteel” aims to develop functionally adapted steel materials for additive manufacturing (AM). Based on the AM process laser powder bed fusion (LPBF), the holistic process chain, including alloy design, powder atomization, AM, and postheat treatment, is considered to achieve this objective. Tool steels are usually characterized by higher carbon content and limited weldability, leading to limited processability for LPBF. To extend these limitations, different approaches for tool steels are investigated: for high‐carbon tool steels, the effects of lower martensite start temperature are investigated using the alloy 1.2842 as an example. A low martensite start temperature seems to be advantageous for crack‐free processing with LPBF. In order to avoid a high hardness level after rapid cooling, the use of a hot work steel with a carbon content of 0.2 wt% is investigated. Due to the chemical composition of the material, a moderate preheating temperature <300 °C is required. In addition, very high scanning speeds are possible with an improved shielding gas flow. Finally, the experience along the process chain with the standard steels is used for a modification of the alloy 1.2344. The effects of this modification on AM and heat treatment are investigated.

HintergrundIn der COVID-19-Pandemie war es im März 2020 nötig, deutschlandweit mehrwöchige sog. Ausgangsbeschränkungen zu erlassen. Diese bedeuten massive Einschränkungen der Bevölkerung.Ziel der ArbeitEs soll untersucht werden, ob die „theory of reasoned action“ (TORA) geeignet ist, die Intention, sich an Ausgangsbeschränkungen zu halten, zu erklären und ob die Sanktionsantizipation die Intention beeinflusst: 1) Ist die TORA für die Erklärung der Intention, verordnete Ausgangsbeschränkungen einzuhalten, geeignet? 2) Beeinflusst eine Erweiterung der TORA um die Variable Sanktionsantizipation die Intention, sich an verordnete Ausgangsbeschränkungen zu halten?MethodenMittels einer Online-Umfrage wurden die Intention, Einstellungen und subjektive Norm zur Einhaltung von Ausgangsbeschränkungen sowie die Sanktionsantizipation bei Nichteinhalten mittels einer Rating Scale (1–7) ermittelt. Multiple lineare und hierarchische Regressionen wurden zur Klärung der Forschungsfragen erstellt.ErgebnisseDie Auswertung von 1269 Datensätzen ergab, dass die Intention, die Ausgangsbeschränkungen zu befolgen, mit einem Mittelwert (MW) von 6,24 und einer Standardabweichung (SD) von 1,2, hoch ist und durch die Einstellungen und subjektive Norm zu 67 % erklärt werden kann (p < 0,001). Ein Einfluss durch Sanktionsantizipation besteht nicht (∆R2 = 0,002).DiskussionDie TORA eignet sich zur Erklärung der Intention, sich an Ausgangsbeschränkungen zu halten. Politische Entscheidungsträger sollten bei Ansprachen und anderen Maßnahmen zur Erhöhung der Compliance auf das Androhen von Sanktionen verzichten und den Sinn und Nutzen der Maßnahmen (auch für das eigene soziale Umfeld) betonen, um positiv auf die Intention einzuwirken.

To date, there is still no IFDH approved dental hygienist (DH) education model in Germany. Nevertheless, opportunities to complete vocational DH education courses have substantially increased within the last two decades. However, the content and quality of these courses vary greatly and are difficult to survey. The purpose of this article therefore was to present an overview of the education programmes offered in Germany as of March 2017. A formal request was sent to all education establishments for details of such courses, and a systematic internet search was performed covering the DH education topic in Germany. Ten vocational education programmes were found, most of them organized by local dental chambers. One private provider offers a Bachelor Degree in Dental Hygiene on completion of a course which runs over 2 or 3ys. Details of contents, objectives and concise ratings or comparisons of the various courses are scarce, although in principle all should meet the same quality standards. For dental hygiene students, patients and dentists, it is hard and unsatisfactory to get a clear overview of the types and the quality of DH education which can be achieved in Germany. A solution for this dilemma would appear to be essential. However, due to the peculiarities of German legislation as well as the complex sphere of vested interests, it is impossible to predict if or when the situation will change for the better.

Microbial contamination can occur during long-term storage of domestic heating oil (DHO), thus biofouling the fuel and equipment. In this study, microorganisms in domestic heating oil storage tanks were identified, and the factors that promote growth in DHO storage tanks were examined. Notably, obligate aerobic but not strict anaerobic microbes were detected, suggesting the existence of an oxic environment. The measured and calculated diffusion rates of oxygen in the oil support the finding that oxygen limitation in a storage tank is unlikely. Fatty acid methyl ester (FAME) fostered microbial growth in DHO tanks, and fewer microorganisms were required to initiate growth in FAME-blended fuels. The results are discussed in the context of minimizing microbial contamination during long-term storage of FAME blends in domestic heating oil applications.

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Chem. Ing. Tech. 2014, 86, No. 9, 1347–1365 V1.13 Nanostrukturierte, bi-funktionale Katalysatoren fur die Fischer-Tropsch-Synthese N. Kruse (E-Mail: kruse@icvt.tu-clausthal.de), A. Machoke, W. Schwieger, R. Guttel Technische Universitat Clausthal, Clausthal-Zellerfeld, Germany Friedrich-Alexander-Universitat Erlangen-Nurnberg, Germany DOI: 10.1002/cite.201450711