Search NASA⌕ Search

Engineering topics

Zang, Xiaoqin

Publications and source records attributed to Zang, Xiaoqin.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Linking international technical specifications for acoustic characterization of marine energy converter sounds with environmental compliance criteria

As new ocean energy technologies emerge and are deployed for testing and operations, sound emissions are a potential concern for environmental effects to marine life. Consistent acoustic measurement and data analysis methods can help promote comparisons of technologies and transferability between project sites. In 2022, acoustic emissions from a prototype scale wave energy converter (WEC) were characterized for a range of environmental conditions and power generation states in the coastal waters off southern California using a set of international technical specifications. Results from the international technical specification analyses were applied to United States regulatory threshold criteria for acoustic impacts to marine mammals and examined in the context of European underwater noise monitoring guidelines. Weighted 24 hour cumulative sound exposure levels SEL24h calculated from the highest power generation state WEC sound pressure levels were often more than 20 dB below threshold criteria for temporary threshold shifts in five relevant marine mammal hearing groups. Following European Union recommendations for analyses and reporting, WEC sound characterization in third octave bands centered at 63 Hz and 125 Hz show clear spatial decay of WEC-generated noise, with more pronounced attenuation at 63 Hz, and a less marked but still detectable gradient at 125 Hz, collectively suggesting a relatively confined acoustic footprint under the observed conditions. The value of the international technical specification approach is highlighted by the isolation of WEC sounds from the surrounding soundscape. This allows for a robust characterization of acoustic emissions through a range of device power generation and sea states. Furthermore, in threshold-based regulatory contexts like the U.S., this facilitates direct evaluation of source contributions, while in broader monitoring frameworks used in the E.U. it provides a reproducible foundation for assessing the contribution of emerging ocean energy technologies to the underwater acoustic environment.

Haxel, Joseph H. (ORCID:0000000273864761)↗
DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗
DOE OSTI2023

Online and noninvasive monitoring of battery health at negative-half cell in all-vanadium redox flow batteries using ultrasound

Hydrogen evolution is one of the major side reactions that is detrimental to the health of all-vanadium redox flow batteries, especially for long-term cycling. Effective, low-cost, and accurate online prediction and detection methods for hydrogen generation are not yet available. In this work, we designed an online, noninvasive ultrasonic probing approach for monitoring the state of charge (SoC), predicting the hydrogen generation, and detecting hydrogen gas bubbles in anolyte solutions. The technique employs a pulse-echo method to measure the sound speed and the acoustic attenuation coefficient of the anolyte solution. Through static offline experiments and online in operando experiments, we have demonstrated that when hydrogen gas is generated in anolyte solutions, large variations are observed in both sound speed and acoustic attenuation coefficient measurements. We found that the variations of acoustic attenuation coefficient are highly correlated (correlation coefficients >0.9) with the gas flow rate. In conclusion, the designed acoustic method can monitor the SoC of anolyte, predict the hydrogen generation, and detect the presence of gas bubbles in an anolyte solution and, thus, provide information about the state of health for operation and management of flow battery systems.

25 ENERGY STORAGE↗
DOE OSTI2023

Towards assessing the impact of anthropogenic sound on fishes: Gaps, perspectives, and a case study of a large floating bridge

Underwater anthropogenic sound can cause physical, physiological, and behavioral impacts on fishes. With the development of marine energy facilities, the impact of man-made underwater sound on fishes has become a growing regulatory concern. Large gaps remain in how to perform an underwater sound assessment. Such gaps mainly reside in scientific evidence, regulatory criteria, sound characterization metrics, sound field modeling, and instrumentation for field measurement. Here, this paper reviewed existing work on underwater sound assessment for fishes to identify critical gaps and provide perspectives on how to deal with these gaps, demonstrated with a case study on the impact of the Hood Canal Bridge traffic sound on migrating steelhead smolts in Washington State, U.S. We also provide perspectives on future research directions to bridge the identified gaps for aquatic ecological assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A New Miniaturized Acoustic Transmitter for Marine Animal Tracking

Most marine renewable energy (MRE) technologies are still in their infancy. Many uncertainties remain regarding MRE systems’ impacts on the marine environment and species where they are deployed, which is one of the major hurdles for these systems to be widely adopted at a commercial scale. Currently, the primary technologies to monitor marine animals’ behavior around existing or potential MRE deployment sites are optical and acoustic imaging. However, these methods cannot identify individual animals and thus are less useful in identifying protected species and studying their behavior. Implantable acoustic transmitters with unique identification codes could serve as a complementary technology. To minimize potential bias introduced to the study results, the implanted transmitters must be sufficiently small and light to not affect the animals’ behavior while still having acceptable signal strengths and service life. Recently, we developed a new miniaturized acoustic transmitter that offers a significantly improved signal strength and service life (140 days [projected value] at a 5-s transmission interval) in a smaller and lighter package (0.45 g in the air), compared to the existing technologies. The detection range and detection efficiency of the new transmitter were tested in an actual marine environment and demonstrated a detection range of up to 330 m, a 65% improvement over the existing commercial counterparts operating at similar frequencies. Here, we report the design principles, performance, and manufacturing procedure of the transmitter. Finally, theoretical estimates of the detection range, the actual range, and detection efficiency results from the field testing are presented.

acoustic telemetry↗
DOE OSTI2022

In operando, non-invasive state-of-charge monitoring for redox flow batteries

This document describes techniques and systems for in operando, non-invasive SOC monitoring of redox flow batteries. The described techniques and systems allow for accurate, inexpensive, portable, and real-time methods to measure the SOC of redox flow batteries. System operators can monitor the SOC by measuring an acoustic attenuation coefficient of the electrolyte in the redox flow battery. The acoustic attenuation coefficient is measured using an ultrasonic transducer attached to a probing cell, which is connected to an electrolyte flow of a redox flow battery. The acoustic attenuation coefficient provides an accurate, real-time SOC measurement that is generally insensitive to varying operational temperatures of the electrolyte solution.

25 ENERGY STORAGE↗
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 OSTI2021

A miniaturized long-life acoustic transmitter for animal tracking in marine environments

Marine and hydrokinetic (MHK) energy devices are early-stage technologies and their impacts to ocean life and environment are largely unknown, which consequently limits the progress of deploying these devices at commercial scale. Optical and acoustic imaging have been the primary technologies for monitoring marine animals’ behavior at existing or potential MHK sites. However, these technologies lack the ability to identify individual animals, which makes them less useful in identifying protected species as well as studying the animals’ long-term behavior. Implantable acoustic transmitters with unique identification codes could be used as a complementary technology. To minimize the tag burden on the tagged animal (and hence the resulting bias) and to satisfy the long-term monitoring needs, it is desired that the implanted transmitter be as small and light as possible and can last for as long as possible. To date, the smallest acoustic transmitters suitable for marine animal tracking have relatively short service life and detection range (up to 200 m). In this report, we introduce a new miniaturized acoustic transmitter which operates at 208.3 kHz. It offers a smaller dry weight (0.45 g), a significantly longer service life (140 days at a 5-second ping rate) as well as a greater detection range than the existing commercial counterparts, which will help obtain more comprehensive movement and behavioral information of animals around MHK energy devices.

Li, Huidong↗
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↗
DOE OSTI2019

Does the Hood Canal Bridge traffic noise affect migrating steelhead smolts?

Recent research conducted by NOAA’s Manchester Research Laboratory has provided strong evidence of a substantial migration interference and increased mortality risk associated with the Hood Canal Bridge , which drives the need to investigate the sound and vibration generated by the vehicles crossing the bridge. A month-long monitoring of in-water sound pressure and the vibration of the bridge pontoons was performed at the Hood Canal Bridge by three sets of autonomous sound and vibration recording systems deployed along the bridge. At each deployment site, sound pressure in water was recorded by a hydrophone submerged three meters under water, and the vibration of the bridge pontoon was recorded by an array of three orthogonally mounted single-axis accelerometers. Both sound pressure and acceleration data were analyzed and compared to the threshold levels suggested by previous studies. Results show that the observed sound pressure levels and particle accelerations are strongly correlated with traffic volume across the bridge, and particle accelerations occasionally exceeded the threshold levels that could elicit the avoidance behavior of juvenile salmon. It is concluded that the Hood Canal Bridge traffic noise can potentially affect migrating salmonids due to the intense particle accelerations caused by the bridge pontoons in water.

Zang, Xiaoqin↗