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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Survival, growth and tag retention of juvenile European eel ( Anguilla anguilla L.) with implanted 12 mm passive integrated transponder tags and acoustic tags

To evaluate the efficiency of tagging juvenile European eels with implanted 12 mm passive integrated transponder (PIT) tags or Eel/Lamprey acoustic transmitters (ELATs), the authors studied tag retention, survival and growth of eels (7–25 g). Experimental eels were obtained from an eel farm, tagged and then released in a series of shallow dug-out ponds with a surface area of c. 200 m 2 . Tagged and control eels were distributed evenly, with 50 tagged and 50 control eels in each of four ponds, giving a total of 200 tagged and 200 control eels mixed. After 76 days, the ponds were drained, and eels were sampled and measured. A total of 344 eels (86%) were recaptured, indicating high survival. Tag retention was 99% as only one of the recaptured PIT-tagged eels had lost the tag and none of the ELAT tagged. The results demonstrated that tagging juvenile eels >16 cm with these small tags is indeed feasible. The growth of tagged and control fish was differentiated but generally low in length and negative in mass but did not differ between the three groups.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Energy Snapshot - Anguilla

This profile provides a snapshot of the energy landscape of Anguilla, a British overseas territory in the Caribbean.

44 EE - Strategic Programs, Energy Transition Init↗

DOE OSTI2020

Anguilla Energy Snapshot 2020

This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Anguilla.

ETI, Energy Transitions Initiative, Anguilla, Snap↗

DOE OSTI2021

Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

13 HYDRO ENERGY↗

DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗

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

American eel resilience to simulated fluid shear associated with passage through hydroelectric turbines

American eel (Anguilla rostrata) populations have declined within their native range along the eastern coast of North America due to factors such as commercial fishing, habitat alteration, and dams. American eel are catadromous fish species, and high mortality rates (>40%) have been observed for freshwater life-stage adult eel passing downstream through hydropower turbines. Lacerations and sectioning of fish have been observed downstream of turbines and these injuries are commonly associated with direct contact with the turbine runner, whether through blade strike or pinching and grinding. Exposure to fluid shear may also be a source of injury, however, little is known about American eel susceptibility to this physical stressor. Eels are considerably flexible when compared to other fish species and lack other morphological characteristics that would make them susceptible to fluid shear, such as protruding eyes, large scales, and large operculum. European eel, which have previously been tested for susceptibility to fluid shear, were found to be resilient. To determine if American eel are also resilient to fluid shear, forty American eel were exposed to a water jet, simulating severe fluid shear (strain rate > 800 s –1 ) that fish may experience when passing downstream through turbines. No immediate or delayed (48 h) signs of injury were observed after exposure to severe fluid shear. Based on this study, and a previous study conducted on American eel susceptibility to barotrauma, the source of injury and mortality of American eel passing through turbines is likely attributed to blade strike or pinching and grinding.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of Modular and Scalable Downstream Passage Systems for Silver American Eels

Implementation of effective downstream passage for silver-phase American Eel (Anguilla rostrata) at hydropower dams using methods that are economically feasible has been a challenge for many project owners. Due to the potential for high mortality rates during turbine passage, resource agencies have focused on mandatory prescriptions that prevent entrainment by physically excluding eels (i.e., narrow trash rack bar spacing) or eliminating turbines as a passage route (i.e., unit shutdowns at night when eel movement is greatest). Both of these options can significantly impact the economic viability of a project. Consequently, there is a need for alternative technologies that can provide safe and effective downstream passage for silver eels without having a major impact on the ability of a project to generate power during the eel downstream migration period. This need is of particular concern now because about 300 hydropower projects within the range of American Eel freshwater habitats are scheduled to be relicensed by the Federal Energy Regulatory Commission (FERC) in the next 15 years

13 HYDRO ENERGY↗

DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗