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Digital elevation models (DEMs), represent the three-dimensional terrain and are the basic input for numerical snow avalanche dynamics simulations. DEMs can be acquired using topographic maps or remote-sensing technologies, such as photogrammetry or lidar. Depending on the acquisition technique, different spatial resolutions and qualities are achieved. However, there is a lack of studies that investigate the sensitivity of snow avalanche simulation algorithms to the quality and resolution of DEMs. Here, we perform calculations using the numerical avalance dynamics model RAMMS, varying the quality and spatial resolution of the underlying DEMs, while holding the simulation parameters constant. We study both channelized and open-terrain avalanche tracks with variable roughness. To quantify the variance of these simulations, we use well-documented large-scale avalanche events from Davos, Switzerland (winter 2007/08), and from our large-scale avalanche test site, Valĺee de la Sionne (winter 2005/06). We find that the DEM resolution and quality is critical for modeled flow paths, run-out distances, deposits, velocities and impact pressures. Although a spatial resolution of ~25 m is sufficient for large-scale avalanche modeling, the DEM datasets must be checked carefully for anomalies and artifacts before using them for dynamics calculations.
Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one of the undeformed reference state of a specimen and another of the deformed target state, the relative displacement between those two states is determined. DIC is well known and often used for post-processing analysis of in-plane displacements and deformation of specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and extend the field of use of this technique.
Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether real-time analysis is possible with these methods. To reflect improvements in computing technology different hardware settings were also analysed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm such that it becomes practically slower than a suboptimal algorithm. The Newton-Raphson algorithm in combination with a modified Particle Swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss-Newton algorithm is superior. As expected, the Brute Force Search algorithm is the least effective method. We also found that the correct choice of parallelization tasks is crucial to achieve improvements in computing speed. A poorly chosen parallelisation approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode the correct choice of combinations of integerpixel and sub-pixel search algorithms is decisive for an efficient analysis. Using currently available hardware realtime analysis at high framerates remains an aspiration.
Solar Sail Kinetic Energy Impactor Trajectory Optimization for an Asteroid-Deflection Mission
(2007)