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Flow separation is a phenomenon that occurs in all kinds of supersonic nozzles sometimes during run-up and shut-down operations. Especially in expansion nozzles of rocket engines with large area ratio, flow separation can trigger strong side loads that can damage the structure of the nozzle. The investigation presented in this paper seeks to establish measures that may be applied to alter the point of flow separation. In order to achieve this, a supersonic nozzle was placed at the exit plane of the conical nozzle. This resulted in the generation of cross flow surrounding the core jet flow from the conical nozzle. Due to the entrainment of the gas stream from the conical nozzle the pressure in its exit plane was found to be lower than that of the ambient. A Cold gas instead of hot combustion gases was used as the working fluid. A mathematical simulation of the concept was validated by experiment. Measurements confirmed the simulation results that due to the introduction of a second nozzle the pressure in the separated region of the conical nozzle was significantly reduced. It was also established that the boundary layer separation inside the conical nozzle was delayed thus allowing an increased degree of overexpansion. The condition established by the pressure measurements was also demonstrated qualitatively using transparent nozzle configurations.
In this paper the way to a 5-day-car with respect to a modular valve train systems for spark ignited combustion engines is shown. The necessary product diversity is shift from mechanical or physical components to software components. Therefore, significant improvements of logistic indicators are expected and shown. The working principle of a camless cylinder head with respect to an electromagnetical valve train (EMVT) is explained and it is demonstrated that shifting physical diversity to software is feasible. The future design of combustion engine systems including customisation can be supported by a set of assistance tools which is shown exemplary.
Germany is a frontrunner in setting frameworks for the transition to a low-carbon system. The mobility sector plays a significant role in this shift, affecting different people and groups on multiple levels. Without acceptance from these stakeholders, emission targets are out of reach. This research analyzes how the heterogeneous preferences of various stakeholders align with the transformation of the mobility sector, looking at the extent to which the German transformation paths are supported and where stakeholders are located.
Under the research objective of comparing stakeholders' preferences to identify which car segments require additional support for a successful climate transition, a status quo of stakeholders and car performance criteria is the foundation for the analysis. Stakeholders' hidden preferences hinder the derivation of criteria weightings from stakeholders; therefore, a ranking from observed preferences is used. This study's inverse multi-criteria decision analysis means that weightings can be predicted and used together with a recalibrated performance matrix to explore future preferences toward car segments.
Results show that stakeholders prefer medium-sized cars, with the trend pointing towards the increased potential for alternative propulsion technologies and electrified vehicles. These insights can guide the improved targeting of policy supporting the energy and mobility transformation. Additionally, the method proposed in this work can fully handle subjective approaches while incorporating a priori information. A software implementation of the proposed method completes this work and is made publicly available.
Up in the clouds and above fuels and construction materials must be very carefully selected to ensure a smooth flight and touchdown. Out of around 38,000 single and dual-engined propeller aeroplanes, roughly a third are affected by a new trend in the fuel sector that may lead to operating troubles or even emergency landings: The admixture of bio-ethanol to conventional gasoline. Experiences with these fuels may be projected to alternative mixtures containing new components.
Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.
GHEtool is a Python package that contains all the functionalities needed to deal with borefield design. It is developed for both researchers and practitioners. The core of this package is the automated sizing of borefield under different conditions. The sizing of a borefield is typically slow due to the high complexity of the mathematical background. Because this tool has a lot of precalculated data, GHEtool can size a borefield in the order of tenths of milliseconds. This sizing typically takes the order of minutes. Therefore, this tool is suited for being implemented in typical workflows where iterations are required.
GHEtool also comes with a graphical user interface (GUI). This GUI is prebuilt as an exe-file because this provides access to all the functionalities without coding. A setup to install the GUI at the user-defined place is also implemented and available at: https://www.mech.kuleuven.be/en/tme/research/thermal_systems/tools/ghetool.
In general aviation, too, it is desirable to be able to operate existing internal combustion engines with fuels that produce less CO₂ than Avgas 100LL being widely used today It can be assumed that, in comparison, the fuels CNG, LPG or LNG, which are gaseous under normal conditions, produce significantly lower emissions. Necessary propulsion system adaptations were investigated as part of a research project at Aachen University of Applied Sciences.
This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.
This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.