Computational Fluid Dynamics Framework for Turbine Biological Performance Assessment

Transcription

1 Computational Fluid Dynamics Framework for Turbine Biological Performance Assessment Marshall Richmond, John Serkowski and Tom Carlson Pacific Northwest National Laboratory Laurie Ebner Portland District, US Army Corps of Engineers Mirjam Sick Andritz Hydro Glenn Cada Oak Ridge National Laboratory 1

2 Background and Objectives Challenges and Barriers Mortality of downstream migrating fish, particularly as a result of passing through hydropower turbines, remains a serious problem at many sites. We need to refine our understanding of turbine and reservoir passage stresses and the responses of a wide range of fish species to those stresses such that systems can be designed to minimize these impacts Objectives Integrate the use of computer models, laboratory/field experimentation, and information about physical and behavioral characteristics of fish to develop a systematic approach design tools to improving turbine design and hydropower project operations Deliver tools to industry and agencies 2

3 Framework for biological performance assessment 1. Identify injury mechanisms 2. Define stressor variables 3. Compute exposure probability distributions 4. Relate exposure to dose-response 5. Compute performance indices 3

4 Injury Mechanisms The most commonly identified mechanisms for fish injury are: Pressure changes Strike/Collision/Gaps Shear Turbulence 4

5 Stressor Variables STRESSOR INJURY MECHANISM pressure nadir pressure changes strike index strike/collision strain rate shear turbulent kinetic energy turbulence 5

6 Stressor and Mortality Probability Distribution 10% 100% Exposure Probability 9% 8% 7% 6% 5% 4% 3% 2% Original AHT Mortality 90% 80% 70% 60% 50% 40% 30% 20% Mortality Probability 1% 10% 0% 0% Stressor Value 6

7 Procedure Build CFD model Get dose responses from literature Generate streamtraces Compute exposure for each path exposure PDF mortality PDF performance indicator 7

8 CFD Model Build CFD model Get dose responses from literature Generate streamtraces Compute exposure for each path exposure PDF mortality PDF performance indicator 8

9 Computing Streamtraces Build CFD model Get dose responses from literature Generate streamtraces Compute exposure for each path exposure PDF mortality PDF performance indicator 9

10 Path Exposure Build CFD model Get dose responses from literature Generate streamtraces Compute exposure for each path exposure PDF mortality PDF performance indicator nadir 10

11 Probability Density Build CFD model Get dose responses from literature % % Generate streamtraces Frequency Frequency Probability 30% 25% 20% 15% 10% Probability Density Compute exposure for each path exposure PDF performance indicator mortality PDF 20 5% % Strike Index (m/s) 11

12 Dose Response Experiments Build CFD model Get dose responses from literature shear exposure Generate streamtraces Compute exposure for each path exposure PDF mortality PDF performance indicator pressure exposure 12

13 Mortality Probability Build CFD model Get dose responses from literature Generate streamtraces Compute exposure for each path exposure PDF mortality PDF performance indicator 13

14 Performance Indicator Calculation Details 50% 100% Exposure Probability (Pe) 40% 30% 20% 10% 80% 60% 40% 20% Exposure Mortality (Pm) 0% 0% Stressor Value (x) P = ( Pe Pm ) dx mortality x x 14

15 Performance Indicators Build CFD model Get dose responses from literature Stressor Rank (Mortality Probability) Original Design Alternate Design Strike 1 2 Pressure Nadir 2 1 Shear 2 1 Turbulence 2 1 Generate streamtraces Compute exposure for each path exposure PDF performance indicator mortality PDF Challenges: How to perform the ranking? Uniform or non-uniform weighting? By operating point and frequency 15

16 Data Analysis Tools 1. Tecplot for streamtrace generation Scripting automates process Stressor variable calculation from CFD values Results output as ASCII files and plots of critical streamtrace point locations 2. Excel for probability calculation Scripting automates process Outputs include exposure and mortality probabilities and plots

17 17 John Day Dam Case Study

18 John Day Turbine Operations Simulated Operational Conditions 1. BP01 (OP1) Peak cfs 2. BP02 (OP2) Lower 1% cfs 3. BP05 (OP5-C) Upper 1% cfs

19 19 John Day Streamtrace Examples

20 Streamtrace Seed Distribution in Intake seed plane just below intake screen Looking downstream 1. Uniform Distribution 0.2 m horizontal and vertical spacing 5088 seeds 2. Sigmoid Distribution 0.2 m vertical spacing horizontal spacing based on hydroacoustic studies and modeled by sigmoid function 8640 seeds 20

21 Sample Tecplot Outputs Blade-Plane Intersections (BP01) uniform sigmoid

22 Pressure Distribution in the Runner Zone BP01 operating point 22 Suction side pressure

23 Analysis Results

24 24 Mortality Estimates for Pressure Exposure

25 Future Plans and Research Needs 25 In progress inclusion of strike, shear, and turbulence Model confirmation test cases Laboratory and field data hydraulic and biological Need to identify test cases (Francis, Bulb) with adequate biological data Enhanced model physics Mass, turbulence effects on particle paths Will these enhancements reduce uncertainty in estimates? Extend framework to unstudied fish species Continue to develop partnerships with hydro-project operators (federal and non-federal) and turbine manufacturers Field and lab test opportunities

26 Acknowledgements U.S. Army Corps of Engineers Portland District U.S. Department of Energy Wind and Water Power Program