Publikationen

The measurement of the transport of scalar quantities within flows is oftentimes laborious, difficult or even unfeasible. On the other hand, velocity measurement techniques are very advanced and give high-resolution, high-fidelity experimental data. Hence, we explore the capabilities of a deep learning model to predict the scalar quantity, in our case temperature, from measured velocity data. Our method is purely data-driven and based on the u-net architecture and, therefore, well-suited for planar experimental data. We demonstrate the applicability of the u-net on experimental temperature and velocity data, measured in large aspect ratio Rayleigh-Bénard convection at Pr = 7.1 and Ra = 2 x 10^5, 4 x 10^5, 7 x 10^5. We conduct a hyper-parameter optimization and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We test two application scenarios that are of interest to experimentalists. One, in which the u-net is trained with data of the same experimental run and one in which the u-net is trained on data of different Ra. Our analysis shows that the u-net can predict temperature fields similar to the measurement data and preserves typical spatial structure sizes. Moreover, the analysis of the heat transfer associated with the temperature showed good agreement when the u-net is trained with data of the same experimental run. The relative difference between measured and reconstructed local heat transfer of the system characterized by the Nusselt number Nu is between 0.3 and 14.1% depending on Ra. We conclude that deep learning has the potential to supplement measurements and can partially alleviate the expense of additional measurement of the scalar quantity.

Recent studies on the flow phenomena in stratified thermal-energy-storage (TES) systems have shown that heat conduction from the hot upper fluid layer through the vertical tank sidewall into the lower cold fluid layer leads to counterdirected wall jets adjacent to the vertical sidewalls. It was shown that these phenomena destroyed half of the total exergy content in less than a tenth of the storage time constant of a 2-m3 stratified TES system. This paper investigates short-term fluctuations of the wall jets since these fluctuations can potentially mix the hot and cold zones of the thermal stratification that are separated by the thermocline region. Using particle-image velocimetry measurements in two regions of a TES model experiment (near-wall region and far-field region) and analyzing the frequency content of the velocity fields revealed characteristic oscillations for different regions. In the near-wall region, observed fluctuations agreed well with an adjusted boundary layer frequency from the literature, showing that the wall jet is transitioning from laminar to turbulent flow. In the far-field region, the oscillations are related to the Brunt-Väisälä frequency. It is shown that the fluctuations from the boundaries of the thermocline region are most dominant and propagate into deeper regions of the thermocline. A comparison to data from the large-scale test facility for thermal energy storage in molten salt at the German Aerospace Center in Cologne showed good agreement. The consensus between the two experiments proves firstly that a small-scale model experiment with water as a storage liquid can be used to analyze the physical phenomena of large-scale molten salt storage facilities and secondly that these fluctuations are relevant for exergy destruction in real-scale TES.

Die Technische Thermodynamik versteht sich heutzutage als eine allgemeine Energielehre. In vielen Ingenieurstudiengängen gilt sie als Grundlagenfach, dem die Aufgabe zukommt, den Studierenden die vielfältigen Umwandlungsmöglichkeiten von Energieformen aufzuzeigen. Des Weiteren werden die Studierenden über die Einschränkungen bei den Umwandlungsprozessen unterrichtet, anhand derer sie die Effizienz der Prozesse beurteilen können. Die Aussagen der Thermodynamik sind methodisch in vier Hauptsätzen zusammengefasst. Trotz dieses klaren inhaltlichen Aufbaus ist die Technische Thermodynamik bei vielen Studierenden ein eher unbeliebtes Fach, wohl, weil die sichere Beherrschung abstrakter, fachspezifischer Größen wie Entropie und Exergie notwendig ist. Der vorliegende Beitrag setzt sich zum Ziel zu zeigen, dass die Kernaussagen der Thermodynamik mit dem Gedanken der Nachhaltigkeit verknüpft sind. Dadurch ist fundiertes Fachwissen in dieser Disziplin von zentraler Bedeutung für die Umsetzung nachhaltiger Ansätze in der Anwendung. Alle künftigen technischen Lösungsvorschläge im Rahmen der viel zitierten Energiewende kommen an dem grundlegenden Verständnis der thermodynamischen Zusammenhänge nicht vorbei. Weiterhin wird anhand von Praxisbeispielen analysiert, welche Herausforderungen sich daraus für die Lehre in der universitären Ingenieurausbildung ergeben. Neben den klassischen Lehrinstrumenten wie Vorlesung und Seminarübung sind auch Erfahrungssammlung durch Laborversuche und Exkursionen wichtige Schritte im Lernprozess.

We study the influence of thermal boundary conditions on large aspect ratio Rayleigh-Bénard convection by a joint analysis of experimental and numerical data sets for a Prandtl number Pr=7 and Rayleigh numbers Ra=105−106. The spatio-temporal experimental data are obtained by combined Particle Image Velocimetry and Particle Image Thermometry measurements in a cuboid cell filled with water at an aspect ratio Γ=25. In addition, numerical data are generated by Direct Numerical Simulations (DNS) in domains with Γ=25 and Γ=60 subject to different idealized thermal boundary conditions. Our experimental data show an increased characteristic horizontal extension scale ÜÞλ of the flow structures for increasing Ra , which due to an increase of the convective heat transfer also leads to an increase of the Biot number (Bi) at the cooling plate. However, we find the experimental flow structure size to range in any case in between the ones observed for the idealized thermal boundary conditions captured by the simulations: On the one hand, they are larger than in the numerical case with applied uniform temperatures at the plates. On the other hand, they are smaller than in the case of an applied constant heat flux, the latter of which leads to a structure that grows gradually up to the horizontal domain size. We are able to link this observation qualitatively to theoretical predictions for the onset of convection. Furthermore, we study the effect of the asymmetric boundary conditions on the heat transfer. Contrasting experimental and numerical data reveals an increased probability of far-tail events of reversed heat transfer. The successive decomposition of the local Nusselt number Nuloc traces this effect back to the sign of the temperature deviation ÜÞΘ, eventually revealing asymmetries of the heating and cooling plate on the thermal variance of the generated thermal plumes.

Microfluidic flows feature typically fully three-dimensional velocity fields. However, often the optical access for measurements is limited. Astigmatism or defocus particle tracking velocimetry is a technique that enables the 3D position determination of individual particles by the analysis of astigmatic/defocused particle images. The classification and position determination of particles is a task well suited to deep neural networks (DNNs). In this work, two DNNs are used to extract the class and in-plane position (object detection) as well as the depth position (regression). The performance of both DNNs is assessed by the position uncertainties as well as the precision of the size classes and the amount of recalled particles. The DNNs are evaluated on a synthetic dataset and establish a new benchmark of DNNs in defocus tracking applications. The recall is higher than compared to classic methods and the in-plane errors are always subpixel accurate. The relative uncertainty in the depth position is below 1% for all examined particle seeding concentrations. Additionally, the performance on experimental images, using four different particle sizes, ranging from 1.14 to 5.03 is analyzed. The particle images are systematically rearranged to produce comprehensive datasets of varying particle seeding concentrations. The distinction between particles of similar size is more challenging but the DNNs still show very good results. A precision above 96% is reached with a high recall above 95%. The error in the depth position remains below 1% and the in-plane errors are subpixel accurate with respect to the labels. The work shows that first, DNNs can be trained with artificially rearranged data sets based on individual experimental images and are therefore easily adaptable to various experimental setups and applicable by non-experts. Second, the DNNs can be successfully adapted to determine additional variables as in this case the size of the suspended particles.