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Improved wall temperature prediction for the LUMEN rocket combustion chamber with neural networks
(2023)
Accurate calculations of the heat transfer and the resulting maximum wall temperature are essential for the optimal design of reliable and efficient regenerative cooling systems. However, predicting the heat transfer of supercritical methane flowing in cooling channels of a regeneratively cooled rocket combustor presents a significant challenge. High-fidelity CFD calculations provide sufficient accuracy but are computationally too expensive to be used within elaborate design optimization routines. In a previous work it has been shown that a surrogate model based on neural networks is able to predict the maximum wall temperature along straight cooling channels with convincing precision when trained with data from CFD simulations for simple cooling channel segments. In this paper, the methodology is extended to cooling channels with curvature. The predictions of the extended model are tested against CFD simulations with different boundary conditions for the representative LUMEN combustor contour with varying geometries and heat flux densities. The high accuracy of the extended model’s predictions, suggests that it will be a valuable tool for designing and analyzing regenerative cooling systems with greater efficiency and effectiveness.
Going beyond the current trend of cooperating multiple small satellites we arrive at fractionated satellite architectures. Here the subsystems of all satellites directly self-organize and cooperate among themselves to achieve a common mission goal. Although this leads to a further increase of the advantages of the initial trend it also introduces new challenges, one of which is how to perform closed-loop control of a satellite over a network of subsystems. We present a two-fold approach to deal with the two main disturbances, data losses in the network and failure of the controller, in a networked predictive formation control scenario. To deal with data loss an event based networked model predictive control approach is extended to enable it to adapt to changing network conditions. The controller failure detection and compensation approach is tailored for a possibly large network of heterogeneous cooperating actuator- and controller nodes. The self-organized control task redistribution uses an auction-based methodology. It scales well with the number of nodes and allows to optimize for continuing good control performance despite the controller switch. The stability and smooth control behavior of our approach during a self-organized controller failure compensation while also being subject to data losses was demonstrated on a hardware testbed using as mission a formation control scenario.
Besides the integration of renewable energies, electric vehicles pose an additional challenge to modern power grids. However, electric vehicles can also be a flexibility source and contribute to the power system stability. Today, the power system still heavily relies on conventional technologies to stay stable. In order to operate a future power system based on renewable energies only, we need to understand the flexibility potential of assets such as electric vehicles and become able to use their flexibility. In this paper, we analyzed how vast amounts of coordinated charging processes can be used to provide frequency containment reserve power, one of the most important ancillary services for system stability. Therefore, we used an extensive simulation model of a virtual power plant of millions of electric vehicles. The model considers not only technical components but also the stochastic behavior of electric vehicle drivers based on real data. Our results show that, in 2030, electric vehicles have the potential to serve the whole frequency containment reserve power market in Germany. We differentiate between using unidirectional and bidirectional chargers. Bidirectional chargers have a larger potential but also result in unwanted battery degradation. Unidirectional chargers are more constrained in terms of flexibility, but do not lead to additional battery degradation. We conclude that using a mix of both can combine the advantages of both worlds. Thereby, average private cars can provide the service without any notable additional battery degradation and achieve yearly earnings between EUR 200 and EUR 500, depending on the volatile market prices. Commercial vehicles have an even higher potential, as the results increase with vehicle utilization and consumption.
An approach to aerodynamically optimizing cycling posture and reducing drag in an Ironman (IM) event was elaborated. Therefore, four commonly used positions in cycling were investigated and simulated for a flow velocity of 10 m/s and yaw angles of 0–20° using OpenFoam-based Nabla Flow CFD simulation software software. A cyclist was scanned using an IPhone 12, and a special-purpose meshing software BLENDER was used. Significant differences were observed by changing and optimizing the cyclist’s posture. Aerodynamic drag coefficient (CdA) varies by more than a factor of 2, ranging from 0.214 to 0.450. Within a position, the CdA tends to increase slightly at yaw angles of 5–10° and decrease at higher yaw angles compared to a straight head wind, except for the time trial (TT) position. The results were applied to the IM Hawaii bike course (180 km), estimating a constant power output of 300 W. Including the wind distributions, two different bike split models for performance prediction were applied. Significant time saving of roughly 1 h was found. Finally, a machine learning approach to deduce 3D triangulation for specific body shapes from 2D pictures was tested.
This paper presents a novel approach to Thrust Vector Control (TVC) for small Unmanned Aerial Vehicles (UAVs). The difficulties associated with conventional feed-forward TVC are outlined, and a practical solution to conquer these challenges is derived. The solution relies on observing boom deformations that are created by different thrust vector directions and high-velocity air inflow. The paper describes the required measurement electronics as well as the implementation of a dedicated testbed that allows the evaluation of mid-flight force measurements. Wind-tunnel tests show that the presented method for active thrust vector determination is able to quantify the disturbances due to the incoming air flow.
Innovative Software kann die Position eines Unternehmens im Wettbewerb sichern. Die Einführung innovativer Software ist aber alles andere als einfach. Denn obgleich die technischen Aspekte offensichtlicher sind, dominieren organisationale Aspekte. Zu viele Softwareprojekte schlagen fehl, da die Einführung nicht gelingt, trotz Erfüllung technischer Anforderungen. Vor diesem Hintergrund ist das Forschungsziel der Masterarbeit, Risiken und Erfolgsfaktoren für die Einführung innovativer Software in Unternehmen zu finden, eine Strategie zu formulieren und dabei die Bedeutung von Schlüsselpersonen zu bestimmen.
A new innovative satellite mission, the Innovative CubeSat for Education (InnoCube), is addressed. The goal of the mission is to demonstrate “the wireless satellite”, which replaces the data harness by robust, high-speed, real-time, very short-range radio communications using the SKITH (SKIpTheHarness) technology. This will make InnoCube the first wireless satellite in history. Another technology demonstration is an experimental energy-storing satellite structure that was developed in the previous Wall#E project and might replace conventional battery technology in the future. As a further payload, the hardware for the concept of a software-based solution for receiving signals from Global Navigation Satellite Systems (GNSS) will be developed to enable precise position determination of the CubeSat. Aside from technical goals this work aims to be of use in the teaching of engineering skills and practical sustainable education of students, important technical and scientific publications, and the increase of university skills. This article gives an overview of the overall design of the InnoCube.
Dielektrische Elastomersensoren sind aus Elastomermaterialien aufgebaute Sensoren mit einem kapazitiven Messprinzip. In ihrer einfachsten Form bestehen sie aus einer dehnbaren Elastomerfolie als Dielektrikum, die beidseitig mit leitfähigen und ebenfalls dehnbaren Schichten als Elektroden bedeckt ist.
Damit entsteht ein mechanisch verformbarer elektrischer Kondensator, dessen Kapazität mit der Dehnung der Elastomerfolie stetig ansteigt. Neben solchen Dehnungssensoren lassen sich mit einem geeigneten geometrischen Aufbau auch dielektrische Elastomersensoren realisieren, bei denen eine elektrische Kapazität mit einem angelegten Druck bzw. einer Kraft auf die Oberfläche, mit einer Scherkraft oder mit der Annäherung eines elektrisch leitfähigen oder polarisierbaren Körpers wie z. B. der menschlichen Hand messbar ansteigt.
Durch ihre vielfältige Funktion, intrinsische Verformbarkeit und flächige Ausgestaltung weisen Dielektrische Elastomersensoren erhebliches Potential in der Schaffung smarter, sensitiver Oberflächen auf. Dabei sind weitgehende und individuelle Adaptionen auf den jeweiligen Anwendungszweck durch Abstimmung geometrischer, mechanischer und elektrischer Eigenschaften möglich. Die bisherige Forschung beschränkt sich jedoch auf die Analyse und Optimierung einzelner Aspekte ohne das Potential einer übergreifenden systemischen Perspektive zu nutzen.
Diese Arbeit widmet sich daher der Betrachtung der Sensorik als Gesamtsystem, sowohl horizontal - von abstrakten Modellen bis zur Fertigung und prototypischen Anwendung - als auch vertikal über die Komponenten Material, Struktur und Elektronik.
Hierbei wurden in mehreren Teilgebieten eigenständige neue Erkenntnisse und Verbesserungen erzielt, die anschließend in die übergreifende Betrachtung des Gesamtsystems integriert wurden. So wurden in den theoretischen Vorarbeiten neue Konzepte zur ortsaufgelösten Erfassung mehrerer physikalischer Größen und zur elektrischen und mechanischen Modellierung entwickelt. Die abgeleiteten Materialanforderungen wurden in eine tiefgehende Charakterisierung der verwendeten Elastomer-Kompositwerkstoffe überführt, in der neuartige analytische Methoden in Form von dynamischer elektromechanischer Testung und nanoskaliger Computertomographie zur Aufklärung der inneren Wechselwirkungen zum Einsatz kamen.
Im Bereich der automatisierten Prozessierung wurde ein für die komplexen mehrschichtigen Elektrodenstrukturen geeigneter neuer lasergestützer substraktiver Fertigungprozess etabliert, der zudem die Brücke zu elastischer Elektronik schlägt.
In der abschließenden Anwendungsevaluierung wurden mehrere ortsaufgelöste und multimodale Gesamtsysteme aufgebaut und geeignete Messelektronik und Software entwickelt. Abschließend wurden die Systeme mit einem eigens entwickelten robotischen Testsystem charakterisiert und zudem das Potential der Auswertung mittels maschinellem Lernen aufgezeigt.
This paper proposes a 3-D local pose estimation system for a small Unmanned Aerial Vehicle (UAV) with a weight limit of 200 g and a very small footprint of 10 cm×10cm. The system is realized by fusing 3-D position estimations from an Ultra-Wide Band (UWB) transceiver network with Inertial Measurement Unit (IMU) sensor data and data from a barometric pressure sensor. The 3-D position from the UWB network is estimated using Multi-Dimensional Scaling (MDS) and range measurements between the transceivers. The range measurements are obtained using Double-Sided Two-Way Ranging (DS-TWR), thus eliminating the need for an additional clock synchronization mechanism. The sensor fusion is accomplished using a loosely coupled Extended Kalman Filter (EKF) architecture. Extensive evaluation of the proposed system shows that a position accuracy with a Root-Mean-Square Error (RMSE) of 0.20cm can be obtained. The orientation angle can be estimated with an RMSE of 1.93°.
The thermally activated formation of nanoscale CoPt alloys was investigated, after deposition of self-assembled Co nanoparticles on textured Pt(111) and epitaxial Pt(100) films on MgO(100) and SrTiO3(100) substrates, respectively. For this purpose, metallic Co nanoparticles (diameter 7 nm) were prepared with a spacing of 100 nm by deposition of precursor-loaded reverse micelles, subsequent plasma etching and reduction on flat Pt surfaces. The samples were then annealed at successively higher temperatures under a H2 atmosphere, and the resulting variations of their structure, morphology and magnetic properties were characterized. We observed pronounced differences in the diffusion and alloying of Co nanoparticles on Pt films with different orientations and microstructures. On textured Pt(111) films exhibiting grain sizes (20–30 nm) smaller than the particle spacing (100 nm), the formation of local nanoalloys at the surface is strongly suppressed and Co incorporation into the film via grain boundaries is favoured. In contrast, due to the absence of grain boundaries on high quality epitaxial Pt(100) films with micron-sized grains, local alloying at the film surface was established. Signatures of alloy formation were evident from magnetic investigations. Upon annealing to temperatures up to 380 °C, we found an increase both of the coercive field and of the Co orbital magnetic moment, indicating the formation of a CoPt phase with strongly increased magnetic anisotropy compared to pure Co. At higher temperatures, however, the Co atoms diffuse into a nearby surface region where Pt-rich compounds are formed, as shown by element-specific microscopy.