Grid generation about complex three-dimensional aircraft configurations

The problem of obtaining three dimensional grids with sufficient resolution to resolve all the flow or other physical features of interest is addressed. The generation of a computational grid involves a series of compromises to resolve several conflicting requirements. On one hand, one would like the grid to be fine enough and not too skewed to reduce the numerical errors and to adequately resolve the pertinent physical features of the flow field about the aircraft. On the other hand, the capabilities of present or even future supercomputers are finite and the number of mesh points must be limited to a reasonable number: one which is usually much less than desired for numerical accuracy. One technique to overcome this limitation is the 'zonal' grid approach. In this method, the overall field is subdivided into smaller zones or blocks in each of which an independent grid is generated with enough grid density to resolve the flow features in that zone. The zonal boundaries or interfaces require special boundary conditions such that the conservation properties of the governing equations are observed. Much work was done in 3-D zonal approaches with nonconservative zonal interfaces. A 3-D zonal conservative interfacing method that is efficient and easy to implement was developed during the past year. During the course of the work, it became apparent that it would be much more feasible to do the conservative interfacing with cell-centered finite volume codes instead of the originally planned finite difference codes. Accordingly, the CNS code was converted to finite volume form. This new version of the code is named CNSFV. The original multi-zonal interfacing capability of the CNS code was enhanced by generalizing the procedure to allow for completely arbitrarily shaped zones with no mesh continuity between the zones. While this zoning capability works well for most flow situations, it is, however, still nonconservative. The conservative interface algorithm was also implemented but was not completely validated

Choosing to Stay: Hurricane Katrina Narratives and the History of Claiming Place-Knowledge in New Orleans

Oral histories of the Hurricane Katrina experience abound in stories of conscious decisions to ride out the storm. My article explores the narrative of choosing to stay as an empowering narrative rooted in assertions of place-knowledge and traces its historical genealogy to the nineteenth century. I argue that claiming agency in New Orleans and articulating a sense of belonging and local identity through professed intimate knowledge of the local environment took shape as a strategy of resistance against dominant discourses of American progress after the Civil War. Ultimately, this counternarrative of connecting to place as homeland, drawing on knowledge arising from lived experience, defied the normative twist of modernization, simultaneously reformulating power relations within the city. Choosing to stay thus turns out to be a long-lasting narrative not only of disaster, but of place, belonging, and community;without understanding its historical layers, we cannot fully make sense of this particular Katrina narrative

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our result