Anadromous fish often must pass through hydroelectric facilities during their migration to the ocean. Fish may pass a facility over the spillway, through the turbines, or using an engineered by-pass route (Schilt, 2007). Even at facilities where by-pass routes are present, a significant number of fish pass through the turbines (Hockersmith et al., 2005, Ploskey et al., 2006 and Hansel et al., 2008). Field studies generally indicate that turbine passage is hazardous, with mortality rates ranging between 2% and 19% (Whitney et al., 1997). This incremental mortality is magnified when fish have to pass through multiple hydropower facilities during their downstream migration, as occurs on the Columbia and Snake River systems in the Pacific Northwest region of the USA (Ham et al., 2005).
Over the past decade, many studies have described injury mechanisms associated with turbine passage, the response of various fish species to these mechanisms, and the probability of survival through specific dams under certain conditions. But transforming and integrating these data into tools to design turbines that improve survival by minimizing impacts to fish during passage has been difficult and slow. Although identifying the locations and hydraulic conditions where injuries occur is challenging, a more robust quantification of the turbine environment has emerged through integration of balloon tag and sensor fish data with computational fluid dynamics (CFD) modeling (Dauble et al., 2007). Field-testing new hydro turbines is very expensive, so engineering design tools that improve the linkage between fish injury data and turbine characteristics are needed to identify the most promising designs before full-scale construction begins.
Past attempts to predict the risk to fish passing through the turbine environment have focused on identifying the locations and sizes of potentially hazardous regions (Garrison et al., 2002, Keller et al., 2006 and ?ada et al., 2006). Improving passage survival was a matter of reducing the volume and number of these regions. However, the presence of dangerous zones within the turbine may be biologically inconsequential if few fish experience them. For example, the undersides and tip regions of runner blades generally have very low pressures, which can be harmful to fish, but only a small fraction of the population may pass through these locations.
More recent work has described the use of minimum pressure threshold criteria to guide turbine design (Brown et al., 2012a). An advantage of using minimum pressure criteria is that it is straightforward to implement because it need not consider the non-uniform distribution of pressure within the turbine environment. However, minimum pressure criteria may have a limitation of assuming, when calculating an estimate of mortal injury rates, that all fish passing the turbine are exposed to the same minimum pressure value. In some cases it is possible that minimum pressure design criteria could be overly conservative and lead to the selection of more costly (e.g., lowering the centerline elevation of the unit through civil structure modifications) and less hydraulically-efficient designs.
The Pacific Northwest National Laboratory (PNNL) has developed a new probabilistic design method, the biological performance assessment (BioPA), for bridging this gap between laboratory studies on fish injury and turbine design. With this method, a suite of biological performance indicators for injury and mortality are computed based on data from a CFD model of a proposed turbine design. Each performance indicator is a measure of the probability of exposure to a certain dose of an injury mechanism. If the relationship between the magnitude of exposure to an injury mechanism and frequency of injury is known from laboratory or field studies, the likelihood of fish injury for a turbine design can be computed from the performance indicator. By comparing the values of the indicators from various turbine designs, the engineer can identify the more-promising designs.
In this work, we introduce the BioPA method with a description of its theory, assumptions, and implementation. To illustrate the concepts, we apply the BioPA to estimate fish mortal injury caused by rapid pressure changes in a Kaplan-type hydro turbine.
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