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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.
Tribological performance of biodegradable lubricants under different surface roughness of tools
(2019)
Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.
Thermal and Optical Study on the Frequency Dependence of an Atmospheric Microwave Argon Plasma Jet
(2019)
Production and Characterization of Porous Fibroin Scaffolds for Regenerative Medical Application
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Kyphoplasty of Osteoporotic Fractured Vertebrae: A Finite Element Analysis about Two Types of Cement
(2019)
In this paper the results of a techno-economic analysis of improved and optimized molten salt solar tower plants (MSSTP plants) are presented. The potential improvements that were analyzed include different receiver designs, different designs of the HTF-system and plant control, increased molten salt temperatures (up to 640°C) and multi-tower systems. Detailed technological and economic models of the solar field, solar receiver and high temperature fluid system (HTF-system) were developed and used to find potential improvements compared to a reference plant based on Solar Two technology and up-to-date cost estimations. The annual yield model calculates the annual outputs and the LCOE of all variants. An improved external tubular receiver and improved HTF-system achieves a significant decrease of LCOE compared to the reference. This is caused by lower receiver cost as well as improvements of the HTF-system and plant operation strategy, significantly reducing the plant own consumption. A novel star receiver shows potential for further cost decrease. The cavity receiver concepts result in higher LCOE due to their high investment cost, despite achieving higher efficiencies. Increased molten salt temperatures seem possible with an adapted, closed loop HTF-system and achieve comparable results to the original improved system (with 565°C) under the given boundary conditions. In this analysis all multi tower systems show lower economic viability compared to single tower systems, caused by high additional cost for piping connections and higher cost of the receivers.
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