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Automatic Generation of Simulation Models for Digital Twins from State-of-the-Art Simulation Frameworks
No full textSimulations are an important part of Digital Twins
due to their ability to forecast behavior of a physical asset
or perform what-if analysis. Especially for products with long
life cycles, there is a lack of sufficient data for the generation
of such simulations and information has to be digitized in
tedious manual work and adapted to state-of-the-art frameworks.
Thus, there is the urgent need for automation of this process
including the digitization and generation of simulations using
these frameworks. In this work, we propose a workflow that
is able to completely automate these steps by combining different digitization methods for in situ measurement data and
engineering diagrams into one generalized system description
form. From this system description a set of multiple simulations
from different frameworks can be generated automatically. The
workflow is successfully demonstrated with the example of two
different frameworks for pedestrian flow simulations, one agent-based and one using finite element methods
Deploying a Feedback Loop-Based Training Strategy for Deep Learning-Based Drone Detection
No full textDetecting drones in real-world scenarios with high reliability (e.g., for protecting critical infrastructures) is an essential yet challenging computer vision task due to the intricate and continuously evolving nature of drone technology. In this paper, we consider a feedback loop-based training strategy to address the need for robust drone detection systems. Leveraging game engine-based simulations within three-dimensional environments, our approach facilitates the application-oriented refinement of synthetic training data in an iterative manner, effectively narrowing the simulation-reality gap. By incorporating a small amount of real-world data into the training process, our strategy demonstrates its efficacy across multiple real-world datasets, surpassing the performance of models derived via zero-shot sim-to-real transfer learning. Our findings highlight the practical relevance of this approach, especially in surveillance settings, and emphasize its potential to enhance deep learning models for drone detection
Pixels, Chisels and Contours - Technical Variations in European Road Traffic Noise Exposure Maps
Full text linkMotorized traffic often causes road noise directly in front of our homes and windows. Yet long-term exposure to noise impact life’s quality and can potentially cause negative effects on human health. Furthermore, social and behavioral effects have been measured. To protect people’s health and well-being from such noise, the European Noise Directive (END, 2002/49/EC) obliges countries to produce strategic noise maps every five years for large agglomerations and along major roads, which are then used for noise action planning. Besides that, the official noise maps are a valuable data source for environmental exposure analyses. However, the END has some limitations. The definition of urban agglomerations is vague, different input parameterizations lead to data inconsistencies across administrative units, undefined post processing methods introduce geometric artifacts, and topological errors incompliant to the common Simple Features Implementation Specification hinder working with the published geodata. The aim of this article is to provide practical insights for end-users and stipulate for concise regulations. Moreover, we highlight that these variations limit the comparability of maps in environmental impact assessments. We compile 84 separate noise assessments in Germany reported according to the END to review shape and structure of the geographic data. Graphical representations are used to show in particular how vertices are connected to polygons in noise contour maps and that these geometric alterations effect the eventual statistics on exposed population shares. We aggregate spatial metrics to assess the reported data’s spatial properties in an automatic manner, e.g. when receiving data in future mapping rounds. Along with our quality assessment, a nation-wide dataset on road traffic noise was produced. Depicting the yearly averaged noise level indicator Lden, which integrates exposure at day, evening and night, for 2017, it serves as common ground for environmental health analyses. The examination of different raster to polygon conversion implementations is fundamental to other geodata managers outside the domain of noise mapping, as well
Comparison of Models for Aerothermal Load Prediction using Coupled Trajectory Simulations of a High Lift Reentry Vehicles
No full textLoad and Temperature Influence on a GW-SHM System for a Composite Fuselage
Full text linkA full-scale composite door surrounding aircraft structure was instrumented with a GW-SHM system and subjected to three representative quasi-static load cases using a hydraulic test rig. The test was performed in a hangar under uncontrolled temperature environment, resulting in broad temperature variations throughout the experiment. This work focuses on differentiating between benign environmental and operational conditions and barely visible impact damage. A data-driven approach based on Gaussian Processes is used to detect barely visible impact damage introduced during the test campaign, differentiating between benign environmental/operational conditions and barely visible impact damage
Public acceptance of civilian drones and air taxis in Germany: A comprehensive overview
Full text linkThe technology of unmanned aerial vehicles (i.e., civilian drones) continues to improve and concepts of operations are being developed. Against this background, the importance of public acceptance has grown and the number of studies on the subject has increased in recent years. In this context and as a follow-up to the study by Eißfeldt and colleagues (Eißfeldt et al. in CEAS Aeronaut. J. 11:665–676, 2020), a representative telephone survey on the acceptance of civilian drones in Germany was conducted at the end of 2022. In addition to re-evaluating the attitude towards civilian drones in general, the current study particularly examined the use case of air taxis. It was found that the attitude towards civilian drones tended to be slightly more positive than in the first study. For air taxis, attitudes were revealed to be relatively balanced, with a slight negative tendency. Moreover, extensive inferential statistical analyses showed several factors such as active experience with drones, general sensitivity to noise, and interest in environmental protection to be significantly associated with the attitudes towards civilian drones and air taxis. The application of three different prediction models revealed that a person’s attitude towards civilian drones could be predicted with up to 71% accuracy by drone-related concerns. In this context, concerns about the violation of privacy and animal welfare had the highest predictive value. In sum, the current study provides a comprehensive overview on the acceptance of civilian drones and air taxis in Germany. Its findings underline that citizens’ opinions and concerns must be considered when designing future air mobility concepts
U-space TWICS: A meta U-space service concept to aid developing communications between autonomous uncrewed systems
Full text linkU-space is a regulatory concept for an upcoming air space targeted towards unmanned aerial systems (UAS).
It requires an automatic communication of operators with mandatory services in a centralized structure. However,
communication directly between UAS also needs to be anticipated. A U-space could provide a basis to foster a cooperative,
collective understanding of a shared ecosystem and thereby aid in risk avoidance. We propose the concept of a meta "To Whom
It Concerns Service (TWICS)" in the context of U-space to advance safe operations of UAS with a decentralized information
structure. To that extent, we define an open, modifiable information exchange service between participants. TWICS provides
an experimental communication infrastructure for automated information exchange and a forum targeted to create, modify,
and evaluate those exchanges. Lastly, we provide an example and further discuss our proposal, its advantages and
potential pitfalls
Ego-Migration Compensation for GNSS-Based Passive Radar
No full textOnly the chairs can edit It is commonly known that global navigation satellite system (GNSS)-based passive radars require a target migration compensation processing stage to compensate for the target energy migration effects arising from the use of a long integration time. Moreover, when the receiver is placed on a moving platform, the extent of the migration is known to increase, making the classic target migration compensation processing approaches ineffective in practice. In this work, we address the possibility of compensating for the receiver movement, hereinafter referred to as ego-migration, as a means of reducing the overall perceived migration. To this end, we develop the set of equations that describe the ego-migration and propose an algorithm for compensating it. Simulation results demonstrate the applicability of the concept and showcase the performance of the implementation
Assessment of a Wake Vortex Retrieval Method Using Large-Eddy Simulations and a Lidar Simulator
Full text linkWake vortex studies using light detection and ranging (lidar) measurements constitute a decisive element for determining appropriate and efficient aircraft separations. Algorithms for characterizing the position and strength of wake vortices within lidar scans are available, for example, the radial velocity (RV) method. However, due to the lack of a ground truth from field measurements, no reliable accuracy bound for these algorithms could be given so far. Thus, we perform virtual lidar measurements via large-eddy simulation (LES) lidar simulators (LLSs) employing high-fidelity LESs of landing aircraft, where the position and strength of vortices is fully known. Lidar measurements are simulated to realistic detail by including spatial averaging along a line-of-sight and flow field distortions caused by the measurement geometry. Previous studies either focused on the wake vortex simulation or the lidar simulation, but never both aspects in full detail. Through simulations under various atmospheric conditions, the accuracy of the RV method can be defined as a 4% strength overestimation and 6% dislocation for vortices within an altitude range of below 2.5 initial vortex separations. Within a highly turbulent atmosphere, the RV method performs worse. The main driver of RV method inaccuracy is revealed as the lack of modeling mirror vortices, i.e., imaginary vortices caused by walls. This work enables rating the accuracy of studies by employing the RV method realistically. Furthermore, the LLSs allow generating a labeled dataset for evaluating further algorithms and developing new ones which may increase the data accuracy and thus reduce the effort of costly field measurements
