Abstract
Emrgy Inc. sought to work with Alden Research Laboratory LLC to progress performance testing of their hydrokinetic turbines, and advance the state of their modeling capabilities. The testing was done with two primary objectives: to calibrate the current predictive models, and to evaluate the mechanical durability of the system.
The first object, hydraulic model improvement, was accomplished by two parallel efforts. The Emrgy turbines were tested at Alden’s large recirculating flume facility under a range of configurations, water depths, and flow speeds. In parallel to this effort, a selection of cases were simulated using Computational Fluid Dynamics (CFD). The results of these efforts were then compiled and compared. This gave insight into the current state of the model and potential need for future improvements and modifications. The large amount of data collected also served as a benchmark for the performance of the Emrgy turbines, as well as making clear some complex trends and interactions of the turbines with the flow through and around them.
The second objective, mechanical durability assessment, was accomplished by repeating a selection of the hydrokinetic tests with one of the turbines outfitted with strain gauges. These provided high speed data giving a look at the forces on the blades, spokes and shaft as the turbine rotated. Work in processing this data is still ongoing, but the trends evaluated so far line up well with those predicted in the corresponding CFD models.
The turbines were tested in four configurations, each varying turbine position or the amount of flow entering the units. In configuration 1, with the turbines spread apart, the expected power production lined up well with the theoretical trends.
In configuration 2, with the turbines pushed together, the expected power production again lined up well with the theoretical trends, but now included some deviations based on how flow split between the bypass and the turbines. Whether the flow was subcritical or supercritical in either the turbines or in the bypass biased the flow one way or the other. This showed that the flow split is sensitive to the resistance of each path, and power is sensitive to the flow through the turbines.
Configuration 3 showed the effect of a high blockage ratio, and the highest power of any tests were produced in this configuration. When the flow was supercritical, the surface level change was so significant that it altered the interaction between the front and rear of the rotor leading to an increase in power production for some flow rates.
Configuration 4, with the turbine staggered upstream and downstream, showed the effect of the turbine in a low blockage ratio configuration. The upstream turbine produced less power than the other configurations because of the low blockage. The rear turbine produced even less power, being in the wake of the upstream turbine. The interaction between them can provide some insight into array optimization, and how this may differ from operating each turbine at its best efficiency point.
The CFD models all provided close correlation to the experimental data, as well as provided several key learnings which will result in future refinement and calibration of the models. For configurations 1 and 3, there was an overprediction of power, suggesting that losses not yet accounted for by the code need to be included. Configuration 2 showed lower than expected power, but had more flow going through the bypass than expected. Since power production is highly dependent on this flow distribution, more work is needed to make sure the flow distribution is accurate. Configurations 1 through 3 showed good agreement with experimental data for the depth of the flow, but improvements to the hydrokinetic model and resistance of the flow through and around the units will only serve to make these predictions more accurate. Configuration 4 showed the impact of the downstream turbine in the wake of the upstream turbine.
The experimental data shows clearly that turbine submergence is a key factor in efficient operation of these turbines. Both over-submergence and under-submergence result in a reduction in power extracted at the same current speed, with under-submergence being substantially worse for power production. This understanding of the effect of turbine submergence is key to efficient operating strategies for the turbines.
The CFD model is in a state where it can provide valuable insight into turbine performance and mechanical loads, however the learnings from the experimental data highlight a path for further refinement and development. Tip loss corrections need to be included into the code. Dynamic stall behavior needs to be further investigated, as the power generated low tip speed ratio cases were consistently over-predicted by the CFD model. These two additions are anticipated to greatly improve the model accuracy with high confidence as there is now a large amount of data to validate the changes against.
The data acquired and lessons learned in this effort provide an excellent foundation for continuing this work. The developed detailed performance models of the hydrokinetic turbines from this testing and data will enable further expansion and aggregation of performance modeling at Emrgy, Inc. Further plan will be to expand this effort to full array performance models integrating both turbine hydrokinetic and canal hydraulic models to fully understand the impacts on turbine performance, canal operation, and overall system efficiency. We will seek to progress this further development through other RFTS periods in the Teamer program or through other research grants.