@article{Bung2021,
  author    = {Bung, Daniel Bernhard},
  title     = {Extreme flooding in Western Germany: some thoughts on hazards, return periods and risk},
  series = {Hydrolink},
  journal   = {Hydrolink},
  number    = {4},
  publisher = {International Association for Hydro-Environment Engineering and Research (IAHR)},
  address   = {Madrid},
  pages     = {108 -- 113},
  year      = {2021},
  abstract  = {The low-pressure system Bernd involved extreme rainfalls in the Western part of Germany in July 2021, resulting in major floods, severe damages and a tremendous number of casualties. Such extreme events are rare and full flood protection can never be ensured with reasonable financial means. But still, this event must be starting point to reconsider current design concepts. This article aims at sharing some thoughts on potential hazards, the selection of return periods and remaining risk with the focus on Germany.},
  language  = {en}
}
@article{OertelBung2021,
  author    = {Oertel, Mario and Bung, Daniel Bernhard},
  title     = {Hochwasserschutz - eine Aufgabe f{\"u}r eine nachhaltige Wasserwirtschaft},
  series = {Wasserwirtschaft},
  volume    = {111},
  journal   = {Wasserwirtschaft},
  number    = {9-10},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  issn      = {0043-0978},
  pages     = {3 -- 19},
  year      = {2021},
  language  = {de}
}
@article{Bung2024,
  author    = {Bung, Daniel Bernhard},
  title     = {Kamerabasierte Fließtiefen- und Geschwindigkeitsmessungen},
  series = {Wasserwirtschaft},
  volume    = {114},
  journal   = {Wasserwirtschaft},
  number    = {4},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  issn      = {0043-0978},
  pages     = {47 -- 53},
  year      = {2024},
  abstract  = {In der wasserbaulichen Forschung werden neben klassischen Messinstrumenten zunehmend kamerabasierte Verfahren genutzt. Diese erlauben neben der Bestimmung von Fließgeschwindigkeiten auch die Detektion der freien Wasseroberfl{\"a}che oder zeitliche Vermessung von Kolken. Durch die hohen r{\"a}umlichen und zeitlichen Aufl{\"o}sungen, welche neueste Kamerasensoren liefern, k{\"o}nnen neue Erkenntnisse in turbulenten, komplexen Str{\"o}mungen gewonnen werden. Auch in der Praxis k{\"o}nnen diese Verfahren mit geringem Aufwand wichtige Daten liefern.},
  language  = {de}
}
@phdthesis{Bung2023,
  author    = {Bung, Daniel Bernhard},
  title     = {Imaging techniques for investigation of free-surface flows in hydraulic laboratories},
  doi       = {10.25926/BUW/0-172},
  pages     = {XXIII, 218 Seiten},
  year      = {2023},
  abstract  = {This thesis aims at the presentation and discussion of well-accepted and new imaging techniques applied to different types of flow in common hydraulic engineering environments. All studies are conducted in laboratory conditions and focus on flow depth and velocity measurements. Investigated flows cover a wide range of complexity, e.g. propagation of waves, dam-break flows, slightly and fully aerated spillway flows as well as highly turbulent hydraulic jumps. Newimagingmethods are compared to different types of sensorswhich are frequently employed in contemporary laboratory studies. This classical instrumentation as well as the general concept of hydraulic modeling is introduced to give an overview on experimental methods. Flow depths are commonly measured by means of ultrasonic sensors, also known as acoustic displacement sensors. These sensors may provide accurate data with high sample rates in case of simple flow conditions, e.g. low-turbulent clear water flows. However, with increasing turbulence, higher uncertainty must be considered. Moreover, ultrasonic sensors can provide point data only, while the relatively large acoustic beam footprint may lead to another source of uncertainty in case of relatively short, highly turbulent surface fluctuations (ripples) or free-surface air-water flows. Analysis of turbulent length and time scales of surface fluctuations from point measurements is also difficult. Imaging techniques with different dimensionality, however, may close this gap. It is shown in this thesis that edge detection methods (known from computer vision) may be used for two-dimensional free-surface extraction (i.e. from images taken through transparant sidewalls in laboratory flumes). Another opportunity in hydraulic laboratory studies comes with the application of stereo vision. Low-cost RGB-D sensors can be used to gather instantaneous, three-dimensional free-surface elevations, even in flows with very high complexity (e.g. aerated hydraulic jumps). It will be shown that the uncertainty of these methods is of similar order as for classical instruments. Particle Image Velocimetry (PIV) is a well-accepted and widespread imaging technique for velocity determination in laboratory conditions. In combination with high-speed cameras, PIV can give time-resolved velocity fields in 2D/3D or even as volumetric flow fields. PIV is based on a cross-correlation technique applied to small subimages of seeded flows. The minimum size of these subimages defines the maximum spatial resolution of resulting velocity fields. A derivative of PIV for aerated flows is also available, i.e. the so-called Bubble Image Velocimetry (BIV). This thesis emphasizes the capacities and limitations of both methods, using relatively simple setups with halogen and LED illuminations. It will be demonstrated that PIV/BIV images may also be processed by means of Optical Flow (OF) techniques. OF is another method originating from the computer vision discipline, based on the assumption of image brightness conservation within a sequence of images. The Horn-Schunck approach, which has been first employed to hydraulic engineering problems in the studies presented herein, yields dense velocity fields, i.e. pixelwise velocity data. As discussed hereinafter, the accuracy of OF competes well with PIV for clear-water flows and even improves results (compared to BIV) for aerated flow conditions. In order to independently benchmark the OF approach, synthetic images with defined turbulence intensitiy are used. Computer vision offers new opportunities that may help to improve the understanding of fluid mechanics and fluid-structure interactions in laboratory investigations. In prototype environments, it can be employed for obstacle detection (e.g. identification of potential fish migration corridors) and recognition (e.g. fish species for monitoring in a fishway) or surface reconstruction (e.g. inspection of hydraulic structures). It can thus be expected that applications to hydraulic engineering problems will develop rapidly in near future. Current methods have not been developed for fluids in motion. Systematic future developments are needed to improve the results in such difficult conditions.},
  language  = {en}
}