149544 research outputs found
Sort by
Distributed Subsurface Imaging with Tikhonov and Total Variation Regularization for Seismic Networks
Get PDFConsistency fix: Make simd reductions SIMD-generic
No full textOne design goal of the simd interface is to enable SIMD-generic code.
This is why all arithmetic operators and functions have corresponding overloads. However, arithmetic reductions are still
missing overloads for non-simd, vectorizable types
Application of BOS velocimetry to full‑scale helicopter flight tests
Get PDFTime-resolved background-oriented schlieren (BOS) data are used to calculate the two-dimensional velocity field in the wake of free-flying full-scale helicopters in ground effect. The calculation is performed based on the density gradient pattern of the helicopter engine exhaust gas passing the BOS field of view. A classical BOS evaluation allows the visualization of density gradients such as vortices and the exhaust plume. The result is the BOS displacement field. Applying the two-dimensional divergence to these data results in a pattern that is constant in shape across multiple BOS images, but convects downstream with the outwash velocity of the helicopter. Quantitative two-dimensional velocity fields are calculated using the divergence of the BOS shift as input to a second, time-resolved evaluation. Choosing an appropriate strategy for preparing and evaluating the data is critical to a reliable velocity estimation. Another important aspect is to distinguish between reliable velocity data and erroneous results in areas of reduced signal intensity due to a lack of thermal structures. The velocity data obtained are compared with an analytical outwash model and constant temperature anemometry data acquired simultaneously with the BOS images. The data show good quantitative agreement in areas of sufficient thermal structures within the field of view. This demonstrates the feasibility of BOS velocimetry to investigate large flow fields in full-scale helicopter flight tests
Opto-Mechanical Design and Testing of a Fabry-Pérot Infrared Spectrometer
No full textThis thesis presents the development of a compact infrared spectrometer designed for planetary surface characterization. The system, based on a thermopile detector and a tunable MEMS Fabry-Perot
filter, is developed based on simulated predictions of the relevant key performance parameters, which
include signal strength, spectral resolution and the noise equivalent emissivity difference. These parameters are chosen to enable the detection of diagnostic spectral features in the thermal infrared
range, which provide insights into surface composition and structure.
The mechanical design of the sensor head, including the integration of an optical system and an
external bandpass filter, is described. Design priorities include compatibility of the sensor head with
the available optical bench for testing, thermal stability and control, as well as vacuum compatibility
to allow for testing in a mission-specific relevant environment.
A data acquisition and testing setup is implemented to operate the FPF and thermopile detector
while maintaining a controlled test environment. The influence of individual components in test
setup and sensor head is depicted and analyzed with regard to the further testing strategy. The
spectrometer’s key performance parameters, including repeatability, accuracy, signal-to-noise ratio
and signal strength, are analyzed based on measured data, giving an overview of its operational
capabilities.
Lastly, a comparison between the predicted and testing results allows for the adaption of the simulation to include setup-specific factors, such as readout electronic noise and changes of sensitivity in
the detector depending on ambient pressure, facilitating further developments of the prototype with
more accurate predictions
Development of data augmentation technique for predicting strain rate effects in the mechanical behaviour of fibre-reinforced polymer (FRP) composites (Masterarbeit)
No full textThe mechanical response of Fiber Reinforced Polymers (FRPs) under dynamic loading is crucial for structural applications, particularly in aerospace, where lightweight, high-strength materials are essential. However, accurately predicting strain-rate-dependent behavior remains challenging due to the complex material structure and the limitations of experimental and numerical methods, which are often costly, time-intensive, and sensitive to material inconsistencies. This research investigates whether a Constitutive Artificial Neural Network (CANN) can effectively model and predict the strain-ratedependent behavior of FRPs while ensuring accuracy, generalizability, and computational efficiency.
A CANN model is developed and trained using dynamic compressive testing data from cross-ply IM7/8552 composites. Unlike conventional data-driven approaches, CANNs integrate constitutive laws governing anisotropic materials, enhancing model reliability and extrapolation capabilities. The methodology involves data acquisition through high-speed uni-axial compression tests, preprocessing, empirical curve fitting, and model training. The performance of the trained CANN is evaluated against experimental results and compared with existing analytical and numerical methods.
Results show that the CANN model accurately captures the stress-strain response of FRPs across varying strain rates. By incorporating physics-based constraints, the model improves extrapolation beyond the training data. These findings demonstrate that CANNs offer a viable and computationally efficient alternative for predicting strain-rate-dependent behavior in FRPs, with potential applications to full the gap of material behavior for simulations, crashworthiness analysis, and material design
A Randomized Control Trial Investigating the Effect of Different Treatment Strategies on Mitochondrial Function in Peripheral Arterial Disease: A Study Protocol.
Get PDFTowards satellite tests combining general relativity and quantum mechanics through quantum optical interferometry: progress on the deep space quantum link
Get PDFThe Deep Space Quantum Link (DSQL) is a space-mission concept that aims to explore the interplay between general relativity and quantum mechanics using quantum optical interferometry. This mission concept was formally presented to the United States National Academy of Science Decadal Survey as a research campaign for Fundamental Physics in 2022. Since then, advances have been made in the space-based quantum optical technologies required to conduct a DSQL-type mission. In addition, other research efforts have defined alternative measurement concepts to explore the same scientific questions motivating the DSQL mission. This paper serves as an update to the community on the status of the DSQL mission concept and related research and technology development efforts
The effect of fluid flow on microstructure evolution in Al-alloys within the framework of the ESA project MICAST
No full textThe new HydroSHEDS v2.0 database derived from the TanDEM-X DEM
Get PDFThe increased availability and accuracy of recent remote sensing data accelerates the development of high-quality data products for hydrological modelling. Accurate representation of the Earth's surface, including all water-related features, is crucial for simulating runoff and other hydrological processes.
HydroSHEDS v2.0, the second and refined version of the well-established HydroSHEDS dataset, provides global seamless high-resolution hydrographic information. Developed through an international collaboration involving the German Aerospace Center (DLR), McGill University, Confluvio Consulting, and World Wildlife Fund, HydroSHEDS v2.0 builds on the TanDEM-X mission's digital elevation model (DEM) to offer enhanced accuracy and expanded geographic coverage compared to its predecessor.
While the first HydroSHEDS version relied on the Shuttle Radar Topography Mission (SRTM) DEM, HydroSHEDS v2.0 benefits from the TanDEM-X DEM, which provides a higher resolution of 0.4 arc-seconds globally and includes regions beyond 60°N latitude, previously uncovered by SRTM. Advanced pre-processing techniques ensure that HydroSHEDS v2.0 preserves the high-resolution details of the TanDEM-X DEM. These techniques include the generation of a global inland water mask and its usage for filling invalid and unreliable DEM areas, delineating global coastlines with manual quality control, and reducing distortions caused by vegetation and urban areas. A sequence of automated hydrological conditioning steps further refines the DEM, incorporating void filling, outlier correction, and algorithms to optimize hydrological consistency. Finally, extensive manual corrections using various ancillary data sources improve river network delineation in areas where high uncertainties exist for DEM-derived products, such as areas with flat terrain or anthropogenically modified landscapes.
The resulting hydrologically conditioned DEM has a resolution of 1 arc-seconds and ensures accurate derivation of hydrologic flow connections, forming the basis for core products such as flow direction and flow accumulation maps. In the final HydroSHEDS product, these gridded datasets are complemented by secondary vector-based information on river networks, nested catchment boundaries, and associated hydro-environmental attributes. Together, these products create a standardized, multi-scale database in the same structure and format as the original version and supports applications ranging from local to global scales.
HydroSHEDS v2.0 offers a consistent and easy-to-use framework for hydrological and hydro-ecological research. The main release, scheduled to start in 2025 under a free license, will provide researchers and practitioners with a robust tool for diverse applications. A demonstration of the novel data products and the pre-processing workflow be presented for selected test sites