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Experimental results are presented of a test of the theory of local turbulent heat transfer measurements proposed by Mocikat and Herwig in 2007. A miniaturized multi-layer heat transfer sensor was developed and employed in this study. The new heat transfer sensor was designed to work in air and liquids, and this capability enabled the simultaneous investigation of different Prandtl numbers. Two basic configurations, namely the flow past a blunt plate and the flow past an inclined square cylinder, were investigated in test sections of wind and water tunnels. Convective heat transfer coefficients were obtained through conventional testing (i.e., employing thoroughly heated test objects) and using the new miniaturized sensor approach (i.e., utilizing cold test objects without heating). The main prediction of the Mocikat-Herwig theory that a specific thermal adjustment coefficient of the employed actual miniaturized heat transfer sensor should exist in the fully turbulent flow regime was proven for developed two-dimensional flow. The observed effect of the Prandtl number on this coefficient was in good agreement with the prediction of the asymptotic expansion method. The square cylinder results indicated the inherent limits of the local turbulent heat transfer measurement approach, as suggested by Mocikat and Herwig.
Following the recent Internet of Things-induced
trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing
and production, the Internet of Production pursues the vision of
a digitized, globally interconnected, yet secure environment by
establishing a distributed knowledge base.
Background. As part of our collaborative research of advancing
the scope of industrial applications through cybersecurity and
privacy, we identified a set of common challenges and pitfalls
that surface in such applied interdisciplinary collaborations.
Aim. Our goal with this paper is to support researchers in
the emerging field of cybersecurity in industrial settings by
formalizing our experiences as reference for other research
efforts, in industry and academia alike.
Method. Based on our experience, we derived a process cycle of
performing such interdisciplinary research, from the initial idea
to the eventual dissemination and paper writing. This presented
methodology strives to successfully bootstrap further research
and to encourage further work in this emerging area.
Results. Apart from our newly proposed process cycle, we report
on our experiences and conduct a case study applying this
methodology, raising awareness for challenges in cybersecurity
research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in
applied research data management. Finally, we augment our
discussion with an industrial as well as an academic view on
this research area and highlight that both areas still have
to overcome significant challenges to sustainably and securely
advance industrial applications.
Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research.
We look forward to promising research initiatives, projects, and
directions that emerge based on our methodological work.
Das Internet of Things entfaltet erst durch die Überwindung von bestehenden Produkt- und Industriegrenzen sein volles ökonomisches Potenzial. Trotzdem werden Cyberphysische Systeme in der Forschung bisher oftmals isoliert betrachtet. Der Begriff des Internet of Production (IoP) steht für die Vision eines übergreifenden Austauschs von Daten und Informationen zwischen Produktentwicklung, Produktion und Nutzungsphase – über bestehende Organisationsgrenzen hinaus. Die Realisierung des IoP ist mit Herausforderungen im Bereich der datengetriebenen Modellierung sowie der Infrastruktur verbunden. In diesem Buchbeitrag werden die bestehenden Herausforderungen erläutert und Lösungsansätze skizziert. Der Schwerpunkt liegt auf der datengetriebenen Modellierung. Im Speziellen wird die Problematik des Lernens von kausalen Zusammenhängen, die Interpretierbarkeit von Machine-Learning-Modellen sowie die Integration von Domänenwissen in Lernalgorithmen diskutiert. Abschließend werden zwei Anwendungsbeispiele des „Digital Material Shadows“ vorgestellt. Diese veranschaulichen wie mithilfe von Machine Learning Erkenntnisse über den Materialzustand eines Werkstücks gewonnen werden können. Ziel dieser Digital Material Shadows ist es, langfristig Fertigungsprozesse adaptiv an die individuellen Materialeigenschaften des vorliegenden Werkstücks bzw. Rohmaterials anzupassen.
A user-friendly Pitot probe data reduction Excel-Refprop-Routine for non-ideal gas flow applications
(2021)