Evaluation of blade-strike models for estimating the biological performance of Kaplan turbines

Bio-indexing of hydro turbines is an important means to optimize passage conditions for fish by identifying operations for existing and new design turbines that minimize the probability of injury. Cost-effective implementation of bio-indexing requires the use of tools such as numerical and physical turbine models to generate hypotheses for turbine operations that can be tested at prototype scales using live fish. Numerical deterministic and stochastic blade-strike models were developed for a 1:25-scale physical turbine model built by the U.S. Army Corps of Engineers for the original design turbine at McNary Dam and for prototype-scale original design and replacement minimum gap runner (MGR) turbines at Bonneville Dam’s first powerhouse. Blade-strike probabilities predicted by both models were comparable with those observed in both prototype-scale live fish survival studies and a physical turbine model using neutrally buoyant beads. Predictions from the stochastic model were closer to experimental data than predictions from the deterministic model because the stochastic model considered the aspects of fish approaching to the leading edges of turbine runner blades. Therefore, the stochastic model should be the preferred method for the prediction of blade strike and injury probability for juvenile salmon and steelhead using numerical blade-strike models for evaluating the biological performance of Kaplan hydro turbines.

[1]  H. Stefan,et al.  Validation of a fish habitat model for lakes , 1995 .

[2]  First step of a modeling approach to evaluate spatial heterogeneity in a fish (Cottus gobio) population dynamics , 2006 .

[3]  Eicher.  Turbine-related fish mortality: Review and evaluation of studies: Final report. [Contains glossary] , 1987 .

[4]  E Monten,et al.  Fish and Turbines: Fish Injuries During Passage Through Power Station Turbines , 1985 .

[5]  John R. Skalski,et al.  Turbine passage survival estimation for chinook salmon smolts (Oncorhynchus tshawytscha) at a large dam on the Columbia River , 1996 .

[6]  Paul G. Heisey,et al.  A Reliable Tag-Recapture Technique For Estimating Turbine Passage Survival: Application to Young-of-the-Year American Shad (Alosa sapidissima) , 1992 .

[7]  Daniel P. Loucks,et al.  Forecasting 3-D fish movement behavior using a Eulerian-Lagrangian-agent method (ELAM) , 2006 .

[8]  S. Schmutz,et al.  Fish Migration and Fish Bypasses , 1998 .

[9]  Glenn F. Cada,et al.  The Development of Advanced Hydroelectric Turbines to Improve Fish Passage Survival , 2001 .

[10]  Timothy D. Scheibe,et al.  Fish individual-based numerical simulator (FINS): a particle-based model of juvenile salmonid movement and dissolved gas exposure history in the Columbia River basin , 2002 .

[11]  Marshall C. Richmond,et al.  Survival estimates for juvenile fish subjected to a laboratory-generated shear environment , 2004 .

[12]  Charles C. Coutant,et al.  Fish Behavior in Relation to Passage through Hydropower Turbines: A Review , 2000 .

[13]  John R. Skalski,et al.  Salmonid smolt survival relative to turbine efficiency and entrainment depth in hydroelectric power generation , 2000 .

[14]  M. Odeh,et al.  New Design Concepts for Fish Friendly Turbines , 2000 .

[15]  Melvin J. Dubnick.  Army Corps of Engineers , 1998 .

[16]  John R. Skalski,et al.  Effects of turbine operating efficiency on smolt passage survival , 2002 .

[17]  A W.H Turnpenny.  Mechanisms of Fish Damage in Low Head Turbines: An Experimental Appraisal , 1998 .

[18]  W. V. Winkle,et al.  Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration , 1998 .

[19]  Craig A. McKinstry,et al.  Evaluation of fish-injury mechanisms during exposure to turbulent shear flow , 2005 .