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Machine Learning Based Prediction of Ditching Loads
No full textApproaches are presented to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstruction of the spatial loads using a convolutional autoencoder (CAE) and the transient evolution of these loads in a subsequent part. Different CAE strategies are assessed and combined with either long short-term memory (LSTM) networks or Koopman operator based methods to predict the transient behavior. The training data are compiled by an extension of the momentum method of von Karman and Wagner, and the rationale of the training approach is briefly summarized. The application included refers to a full-scale fuselage of a DLR-D150 aircraft for a range of horizontal and vertical approach velocities at 6 deg incidence. Results indicate a satisfactory level of predictive agreement for all four investigated surrogate models examined, with the combination of an LSTM and a deep decoder CAE showing the best performance
District heating network topology optimization and optimal co-planning using dynamic simulations
No full textDistrict heating networks play a critical role in the transition of the heating supply of buildings to renewable sources. The transition from coal-fired or gas-fired generation units to heat pumps requires new planning methods for district heating networks, since the efficiency of a heat pump is affected strongly by the supply temperature of the district heating network. Therefore, a co-planning approach including the operation of the district heating network in the planning process is required. This paper presents a novel co-planning approach consisting of two steps. First, an optimal district heating network topology is generated from real geo-referenced data. To determine the optimal topology, a new algorithm designed specifically for district heating networks is presented. Next, a simulation model is automatically generated from the respective topology. An optimization is used for the co-planning approach to select an optimal generation unit, find the optimal supply temperature, and dimension the pipes of the district heating network. In contrast to conventional district heating network planning procedures, the optimization includes a full-year dynamic simulation of the district heating network. The result of the planning process is a full y parameterized district heating network with a matching supply temperature. Furthermore, the use of simulation models allows the results to be reused for sensitivity analyses. This is illustrated by examining the selection of generation units under different CO2 price scenarios
Kristallisationspunkte einer Didaktik der beruflichen Fachrichtung Elektrotechnik in der Unterrichtspraxis
No full textEmpirical investigation of loudspeakers as test-targets for continuous-wave radar
No full textLoudspeakers provide a low-cost solution to implement vibrating radar targets. This contribution empirically investigates the reflection characteristics of loud-speakers used to test continuous-wave (CW) radars. Four different loudspeakers under test are analyzed. The radar receive signal only varies in phase when displacing the loud-speaker while it is not fed. In contrast, the receive signal varies in phase and amplitude when the loudspeaker vibrates. The resulting variation in radar cross section (RCS) and phase is determined by measurements at 61 GHz for different vibration frequencies and feed amplitudes
Climate shapes baseflows, influencing drought severity
No full textBaseflow, the sustained flow from groundwater, lakes, and snowmelt, is essential for maintaining surface water flow, particularly during droughts. Amid rising global water demands and climate change impacts, understanding baseflow dynamics is crucial for water resource management. This study offers new insights by assessing baseflow controls at finer temporal scales and examining their relationship with hydrological drought flows. We investigate how climatic factors influence seasonal baseflow in 7138 global catchments across five major climate regions. Our analysis identifies precipitation as the primary driver, affecting 58.3% of catchments, though its impact varies significantly across different climates. In temperate regions, precipitation dominates (61.9% of catchments), while in tropical regions, evaporative demand is the leading factor (47.3%). Snow fraction is particularly crucial in both snow-dominated (20.8%) and polar regions (48.5%). Negative baseflow trends generally emerge where the effects of evaporative demand or snow fraction outweigh those of precipitation. Specifically, in northern regions and the Rocky Mountains, where snow fraction predominantly controls baseflow changes, a negative trend is evident. Similarly, in tropical catchments, where evaporative demand drives baseflow changes, this also leads to a negative trend. Additionally, our findings indicate that baseflow changes are closely linked to hydrologic drought severity, with concurrent trends observed in 69% of catchments. These findings highlight the relationship between baseflow changes, the severity of hydrologic drought and shifts in precipitation, evaporative demand, and snow dynamics. This study provides crucial insights for sustainable water resource planning and climate change adaptation, emphasizing the importance of managing groundwater-fed river flows to mitigate drought impacts
Large deformation simulations of structure–soil-interaction in anisotropic fine-grained soils
No full textStructure–soil-interaction in fine-grained soils is strongly influenced by rate dependency, anisotropy, and overconsolidation effects. While advanced constitutive models such as Anisotropic Visco-Intergranular Strain Anisotropy (AVISA) can capture the fine-grained soil effects, the model application is typically limited to small strain problems due to numerical challenges. This study presents the first successful implementation of the AVISA model within explicit simulations in Abaqus, enabling robust modelling of large deformation problems in fine-grained soils. Two previously underexplored applications are investigated: (i) the stability assessment of a Liebherr LTR 1220 telescopic crawler crane during dynamic uppercarriage rotation under varying overconsolidation ratios (OCR), and (ii) the penetration process of an open-ended tubular pile in anisotropic, overconsolidated clay, focusing on penetration resistance and the evolution of stress and void ratio. Both problems are simulated using the Lagrangian FEM and the Coupled Eulerian–Lagrangian (CEL) approach and are qualitatively compared to available field data. The results demonstrate the capability of the AVISA model to address complex large deformation geotechnical problems realistically. The proposed approach provides new insights and practical tools for modelling structure–soil-interaction in situations where conventional methods often fail
Single-positive multi-label learning with label cardinality
No full textWe study learning a multi-label classifier from partially labeled data, where each instance has only a single positive label. We explain how auxiliary information available on the label cardinality, the number of positive labels per instance, can be used for improving such methods. We consider auxiliary information of varying granularity, ranging from knowing just the maximum number of labels over all instances to knowledge on the distribution of label cardinalities and even the exact cardinality of each instance. We introduce methods leveraging the different types of auxiliary information, study how close to the fully labeled accuracy we can get under different scenarios, and show that an easy-to-implement method only assuming the knowledge of the maximum cardinality is comparable to the state-ofthe-art single-positive multi-label learning methods when using the same base model. Our implementation is publicly available at https://github.com/shayangharib/SPMLL_with_Label_Cardinality
How students (mis)understand simple control structures - an attempt at a taxonomy
No full textBeing able to read and correctly trace code is an essential skill in computer science. This poster presents a taxonomy of the misconceptions and difficulties concerning this skill. We developed our taxonomy with the aim of creating a good basis for developing effective assessment tools and teaching interventions
Influence of pre-bending on primary fixation stability in one-segmental mandibular reconstruction
No full textBackground: The fixation of osseous free flaps for segmental mandible reconstruction after resection is most commonly performed with patient-specific 3D printed or conventional load-bearing reconstruction plates. The main challenge with conventional plates is the step of manual bending to adjust the plate to the specific mandible of the patient. To date, the influence of this permanent plate deformation on the biomechanical conditions within the healing regions remains unknown. The present study aimed to investigate the effect of plate pre-bending on intersegmental strains, known to influence the healing outcome. Methods: To achieve this, biomechanical finite element models were developed to simulate plate pre-bending and biting in a one-segmental mandibular reconstruction. The biomechanics induced within the healing region were compared between a pre-stressed conventional reconstruction plate and a customized conventional reconstruction plate. Results: Higher stresses were predicted in the pre-stressed plate. However, the mechanical strains within the healing regions were not influenced by plate pre-bending. Conclusions: The increased levels of mechanical strains under both pre-stressed and customized conventional plates in comparison to common patient-specific plates could be a reason for the higher rates of osseous union under conventional fixation. Since customized conventional reconstruction plates additionally presented elastic stresses and include the advantages of patient-specific plates, those plates are biomechanically and clinically promising
Information flow analysis – understanding the trade-offs between static and dynamic analysis
No full textInformation Flow Analysis (IFA) is an established approach to understand designs and their vulnerabilities with respect to information leaks. Dynamic simulation-based analysis methods have been proposed and are widely used due to their computational efficiency. However, they may miss to identifiy all possible information flow in a design. On the other hand less scalable formal IFA is exact, but may not be capable of analyzing larger designs. The actual comparison has not been done before. We compare formal and dynamic analysis to investigate the trade-offs. We give examples of their limitations. We explain how IFA runs using a solver for Satisfiability Modulo Theories (SMT) and show experimental results demonstrating the effects on real designs