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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hydropower Biological Evaluation Toolset Best Practice Guide

Field studies using live fish are necessary for the evaluation of turbine biological performance, but they cannot determine the specific hydraulic conditions or physical stresses experienced by the fish, the locations where deleterious conditions occur, or the specific causes of the biological response. Using the Sensor Fish (SF) sensing technology, this deficiency can be overcome because the SF can be released independently or concurrently with live fish directly into operating infrastructure, and it takes high-frequency measurements of hydraulic conditions such as pressure, acceleration, and rotation acting on a body in situ during downstream passage. The Hydropower Biological Evaluation Tools (HBET; Hou et al. 2018) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by the PNNL-developed SF and by live fish. HBET was developed specifically to design SF field studies, process the raw data, and analyze the processed data efficiently and scientifically. Its objectives are to facilitate SF studies focused on characterizing hydraulic conditions and to apply SF data for evaluating the impacts on fish from passage through hydro-structures. HBET allows users to design new studies, analyze data, perform statistical analyses, and evaluate predicted biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to evaluate hydro-structures to enhance environmental sustainability in a cost-effective manner.

13 HYDRO ENERGY↗
DOE OSTI2023

Balloon Tag Manufacturing Technique for Sensor Fish and Live Fish Recovery

Fish may experience injuries and mortality when they pass through hydraulic conveyances at hydropower dams, even if these conveyances are designed to be fish-friendly, such as downstream bypass systems, modified spillways and turbines. The main methods used to study fish passage conditions in hydraulic structures involve direct, in situ testing using Sensor Fish technology and live fish. Sensor Fish data helps identify physical stressors and their locations in the fish passage environment, while live fish are assessed for injuries and mortality. Balloon tags, which are self-inflating balloons attached externally to Sensor Fish and live fish, aid in their recovery after passing through hydraulic structures. This article focuses on the development of balloon tags with varying numbers of dissolvable, vegetable-based capsules containing a mixture of oxalic acid, sodium bicarbonate powders, and water at two different temperatures. Our research determined that balloon tags with three capsules, injected with 5 mL of water at 18.3 °C, consistently achieved the desired balloon volume. These tags had a mean inflation volume of 114 cm 3 with a standard deviation of 1.2 cm 3 . Among the balloon tags injected with water at 18.3 °C, it was observed that the two-capsule balloon tags took the longest time to reach full inflation. In addition, the four-capsule balloon tags demonstrated a faster inflation start time, while the three-capsule balloon tags demonstrated a faster deflation start time. Overall, this approach proves to be effective for validating the performance of new technologies, improving turbine design, and making operational decisions to enhance fish passage conditions. Importantly, it serves as a valuable tool for research and field evaluations, aiding in the refinement of both the design and operation of hydraulic structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Safe passage of American Eels through a novel hydropower turbine

Abstract Objective Study the effects of downstream passage through a novel turbine designed for fish safety, the Restoration Hydro Turbine (RHT), on American Eels Anguilla rostrata in a recirculating turbine test facility. Methods A 55‐cm‐diameter RHT was operated under 10 m of hydraulic head and 667 revolutions/min. In total, 131 eels were passed through the turbine and 43 eels were used as experimental controls (length = 33.9–65.5 cm). High‐speed video of passage through the runner region was captured for 89% of turbine‐passed eels, and injury and behavioral effects were recorded immediately before and after passage, as well as after a 48‐h holding period. A subset of 37 eels was additionally examined with X‐ray imaging for internal injuries. Result The 48‐h survival rate for both treatment and control groups was 100%, with no major internal or external injuries present after the holding period. Conclusion This is a substantial improvement over eel survival rates through conventional Kaplan and Francis turbines, which may range from 40% to 95%, and suggests that hydropower turbines designed for safe downstream fish passage could be implemented without major impacts to eels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗