Computational Fluid Dynamics (CFD) is widely used in manufacturing and engineering from product design to testing. CFD requires intensive computational power and typically needs high performance computing to reduce potentially long experimentation times. Dedicated high performance computing systems are often expensive for small-to-medium enterprises (SMEs). Cloud computing claims to enable low cost access to high performance computing without the need for capital investment. The CloudSME Simulation Platform aims to provide a flexible and easy to use cloud-based Platform-as-a-Service (PaaS) technology that can enable SMEs to realize the benefits of high performance computing. Our Platform incorporates workflow management and multi-cloud implementation across various cloud resources. Here we present the components of our technology and experiences in using it to create a cloudbased version of the TransAT CFD software. Three case studies favourably compare the performance of a local cluster and two different clouds and demonstrate the viability of our cloud-based approach. Index Terms-Computational fluid dynamics, cloud computing, modeling and simulation, high performance computing, fluid mechanics.
We have described an algorithm to perform automatic dip picking on borehole images. One key element of our method is a statistical validation, based on the a contrario theory, which is used to decide whether each candidate dip is to be accepted or not. Our method also uses a randomized Hough transform, which greatly improves the processing speed, allowing for a real-time detection of dips during image visualization. In addition, the same algorithm can be applied at different scales to provide a multiresolution analysis of the structures. Our experiments determine that our algorithm produces reliable dip picking by an evaluation on three manually annotated boreholes: Our method detects from 60% to 90% of the dips annotated by an expert, depending on the complexity of the data.
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