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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-MOz Missouri Ozark Site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-MOz Missouri Ozark Site. This is the FLUXNET version of the carbon flux data for the site US-MOz Missouri Ozark Site produced by applying the standard ONEFlux (1F) software. Site Description - The site is located in the University of Missouri Baskett Wildlife Research area, situated in the Ozark region of central Missouri. The site is uniquely located in the ecologically important transitional zone between the central hardwood region and the central grassland region of the US. The land has been publically owned since the 1930s, and is on a land tract that was forested with the same dominant species before settlement in the early 1800s.

Wood, Jeffrey↗
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL’s 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis of the causes of high wind speed bias in the 20-year wind resource data set for offshore California that the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California Outer Continental Shelf, near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a 1-year period.

17 WIND ENERGY↗
DOE OSTI2020

Application of a Heat Integrated Post-combustion CO 2 Capture System with Hitachi Advanced Solvent into Existing Coal-Fired Power Plant (Final Technical Report)

The goal of this final project report is to comprehensively summarize the work conducted on project DE-FE0007395. In accordance with the Project Management Plan (PMP), Revision F dated 5/10/2019, and Statement of Project Objectives (SOPO) within, the University of Kentucky (UK) Center for Applied Energy Research (CAER) (Recipient) has successfully demonstrated a unique, versatile CO 2 capture system (CCS) using a heat integrated process combined with two-stage stripping for process intensification, heat recovery and demineralized (DM) water generation. This project involved the design, fabrication, installation, testing of and data analysis from the UK CAER 0.7 MWe small pilot scale CO2 capture process installed at Kentucky Utilities (KU) E.W. Brown Generating Station in Harrodsburg, KY. During each of the four project Budget Periods (BPs), UK CAER met all project deliverables, all project milestones, with National Energy Technology Laboratory (NETL) approved adjustments made to the campaign long-term hours during BP4. The CCS was constructed in modular skids. Two solvent campaigns were initially conducted; the first with a 30 wt% monoethanolamine (MEA) as a baseline, and the second with the Hitachi H3-1 advanced solvent. Additional tests were performed with two advanced solvents including CAER and Proprietary Solvent C. Short-period testing with a higher concentration of 40 wt% MEA was conducted to evaluate the potential saving with high alkalinity. From the various solvent campaigns, unique aspects of the UK CAER CCS technology, as well as its flexibility and versatility were experimentally validated and demonstrated. With respect to solvent evaluation efforts in identifying candidates with significant operational and capital cost savings potential, performance of solvents were evaluated to determine the energy requirements for regeneration; environmental impacts from secondary emissions and degradation products; degradation rates, solvent make-up rates and stability. The assessments were done from parametric tests that determined optimum operating conditions for the individual solvents to maximize process efficiency and minimize the parasitic load of the power plant, and from long term campaigns (1000 hours for 30 wt% MEA and 1000 hours for H3-1) which collectively informed the techno-economic analyses (TEA) of the process. The long term campaigns included corrosion studies which used three types of metal coupons in different sections of the process: (absorber, primary stripper, lean carbon-loaded and rich carbon loaded flow streams in process) to mimic heat and flow dynamics process equipment were exposed to. The estimated corrosion rates were used to elucidate corrosion mechanisms and to further guide process material selection for potential capital cost savings.

gas separation↗
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗
DOE OSTI2020

An iterative learning and inference approach to managing dynamic cyber-vulnerabilities of complex systems

As modern systems become increasingly reliant on cyber-technologies and continue to be integrated with physical systems, managing risks from deliberate and non-deliberate sources is a significant research challenge. Unlike strictly physical systems, cyber-enabled physical systems are influenced by dynamic and evolving technologies, environments, and attack mechanisms. As a result, vulnerabilities are rapidly changing and difficult to detect and manage. While there is recent interest in the dynamic properties of performance through resilience analysis, limited research addresses the dynamic nature of cyber-system vulnerability. This paper presents a dynamic iterative learning approach to evaluate the state of health for systems as related to cyber-vulnerabilities. These time-varying system health characteristics may not be visible, but can be inferred using observable characteristics. The approach recognizes that multiple types of vulnerabilities need to be included in a holistic system health assessment. The methods are applied to the Common Vulnerability Scoring System database containing over 100,000 documented cybersecurity vulnerabilities. This work is the first to acknowledge the dynamic properties of cyber-vulnerability, while also inferring system health using observable data and hidden states of system health. This work will be of interest to managers of large-scale cyber-enabled physical systems who are seeking to prioritize system health investments.

Chatterjee, Samrat↗
DOE OSTI2023

Initial Delayed Critical Configuration of SHEBA I and Subcritical Measurements by Californium Source-Driven Noise Analysis

In September 1980, the initial delayed critical configuration of the Solution High-Energy Burst Assembly (SHEBA I) was assembled, and the US Department of Energy’s Oak Ridge National Laboratory (ORNL) staff performed near critical and subcritical measurements using the californium source-driven noise analysis (CSDNA) method at the Los Alamos National Laboratory (LANL) Critical Experiments Facility. An unreflected 56 cm outside diameter stainless-steel cylindrical tank was partially filled with uranyl fluoride solution (235U enrichment was 4.95 wt %) until delayed criticality was achieved. Then, measurements were performed for various fuel solution heights from delayed critical to 60% of the height required for delayed criticality. The stainless-steel tank had an inside diameter of 54.6 cm and a height of 105 cm and was partially (20–36.5 cm) filled with an aqueous solution of uranyl fluoride (with a density of 2.162 g/cm 3 ). The density of the uranium in solution was 1.042 g/cm 3 , and the solution had a H/U atomic ratio of 20.43. The tank had a 6.35 mm wall thickness and an axial reentrant tube with an inside diameter of 6.02 cm and wall thickness of 0.165 cm. The tank was essentially unreflected on the top and sides because it was in a thin metal shed. The reactivity of the near–delayed critical configuration was −10.4 cents, which corresponds to a k eff value of 0.99922. In addition to the CSDNA measurements, the prompt neutron decay constants were determined from break frequency noise analysis (BNFA) measurements. The subcritical neutron multiplication factors from CSDNA and BFNA compared extremely well. These data can be used as the basis of International Nuclear Criticality Safety Evaluation benchmark for the near-critical configuration, and the k eff values at various subcritical configurations can be used as subcritical benchmarks. Furthermore, the measured prompt neutron decay constants can be used as reactor physics benchmarks. This report documents the experimental information for the measurements performed so that, at a later date, researchers could perform the required uncertainty and calculational analyses and documentation to use these data for an International Criticality Safety Benchmark Evaluation Program (ICSBEP) or Nuclear Energy Agency benchmarks. The measured prompt neutron decay constants can be used as the basis of a benchmark for the International Reactor Physics Evaluation Program (IRPhEP). The data from these measurements are available from the ORNL Records Management Services Department, and the logbook is available from ICSBEP at Idaho National Laboratory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Solar PV, Wind Generation, and Load Forecasting Dataset for ERCOT 2018: Performance-Based Energy Resource Feedback, Optimization, and Risk Management (P.E.R.F.O.R.M.)

This report describes the Advanced Research Projects Agency-Energy Performance-Based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) Electric Reliability Council of Texas (ERCOT) dataset consisting of load, solar, and wind deterministic and probabilistic forecasts at three timescales. This dataset consists of 1 year of time-coincident load, wind, and solar actuals and probabilistic forecasts for a region similar to ERCOT. All the data are stored in Hierarchical Data Format 5 (HDF5) files and have been uploaded to an Amazon Web Services repository. The ERCOT data set has 2 years (2017, 2018) of actuals and 1 year (2018) of probabilistic forecasts. These data are provided at various spatial (i.e., site-level, zone-level, and system-level) and temporal scales (i.e., day-ahead, intraday, and intra-hour). Specifically, data are provided for 125 existing wind sites, 22 existing solar sites, 139 proposed wind sites, and 204 proposed solar sites.

14 SOLAR ENERGY↗
DOE OSTI2024

Baseline Characterization Database Verification Report - NBG-18 Billet 635-14

The purpose of this Engineering Calculations and Analysis Report is to present the data being collected in the Baseline Graphite Characterization program, which is directly tasked with supporting the Idaho National Laboratory’s (INL’s) research and development efforts on the Next Generation Nuclear Plant (NGNP)/Very High Temperature Reactor (VHTR)/Advanced Reactor Technology (ART). This program is populating a comprehensive database that will reflect the baseline properties of nuclear-grade graphite regarding individual grade, billet, and position within individual billets. The physical and mechanical property information being collected will be transferred to the NGNP Data Management and Analysis System (NDMAS), and from that database will help populate handbook of property data available to member nations of the Generation IV International Forum (GIF).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Radio Frequency Spectrum Audit to Inventory Private Cellular Base Station Infrastructure

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize cellular base stations. Localizing cellular base stations provides various advantages, including information security, cybersecurity, spectrum management, and interference detection. For example, the MITRE ATT&CK® (Adversarial Tactics, Techniques, and Common Knowledge architecture) [1] and Common Attack Pattern Enumeration and Classification [2] emphasize the importance of being able to minimize the cyber security threat presented by unregulated private cellular base stations (PCBS). The majority of published research looks at the malicious use of PCBSs and focuses on using data retrieved from user equipment (UE), data obtained from an application on the UE, or data shared between the UE and a mobile network to locate it. This innovative strategy, however, focuses on the passively discovered uniqueness of radio frequency (RF) transmissions from commercial cellular infrastructure received in a designated monitoring position (DMP).

42 ENGINEERING↗
DOE OSTI2026

Wireless High-Temperature Sensor Network for smart boiler systems

This final project report describes the research data and findings. This project aims to develop a new wireless high-temperature sensor network for real-time continuous boiler condition monitoring in harsh environments. Such a wireless high-temperature sensor network enables network-based automatic temperature sensing and data collection, which combined with artificial intelligent (AI) algorithms allow the construction of smart boiler systems with boiling condition management and optimization for significant energy-saving and reliability improvement

42 ENGINEERING↗
DOE OSTI2023

Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data

Vehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT) is responsible for performing inspections, and fleet managers are encouraged to maintain their fleet and participate in regular inspections, it is uncertain whether these inspections are occurring at a frequency that is necessary to prevent incidents. The Federal Motor Carrier Safety Administration (FMCSA) of the DOT manages and maintains the Motor Carrier Management Information System (MCMIS) dataset, which contains all incident and inspection data regarding commercial vehicles in the U.S. The purpose of this preliminary analysis was to explore the MCMIS dataset through spatiotemporal analyses, to uncover findings that may hint at potential improvements in the DOT inspection process and highlight location-specific trends in the dataset. These analyses are novel, as previous research using the MCMIS dataset only examined the data at the state or county level, not at a national scale. The results from the analyses pinpointed specific major metropolitan areas, namely Harris County (Houston), Texas, and three of the New York boroughs (Kings, Queens, and the Bronx), which were found to have increasing incident rates during the study period (2016–2020). An overview of potential causal factors contributing to this increase are provided as well as an overview of the inspection process, and suggestions for improvement relative to the highlighted locations in Texas and New York are also provided. Ultimately, it is suggested that the incorporation of advanced technology and automation may prove beneficial in reducing the occurrence of events that lead to incidents and may also help in the inspection process.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Autonomous Tools for Attack Surface Reduction (Final Report)

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic wellbeing. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyberattacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever-growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal was to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The following are the specific tasks that have been successfully completed two phases (2016-2020). Phase I: Task 1: Developed and implemented a robust Project Management and Data Management Plan, coupled with a well thought out Risk Mitigation Plan. Task 2.1: Developed a comprehensive framework that continually assesses and autonomously reduces the attack surface for the power grid control environment spanning across substations, control center and the SCADA network to significantly reduce the risks of cyber attacks. Task 2.2: Developed attack surface analysis techniques, metrics, and tools that assess the attack surface at multiple levels including the control center, substations, and the SCADA network. Task 2.3: Developed attack surface reduction techniques and tools that dynamically reduce attack surface and hence increase attacker’s cost without interfering in the critical functions of the system. Task 2.4: Prototyped, implemented, and quantitatively evaluated/validated the techniques and tools on a realistic industrial CPS security testbed environment by leveraging the unique resources of the team. Task 3: Developed Commercialization plan to transition the developed tools into power system industry stakeholders for a broader adoption by leveraging the expertise of our industrial members. Phase II: Task 4: Successfully completed field demonstration, verification, and evaluation of the effectiveness of the attack surface analysis and reduction techniques on a realistic utility testbed environment. This also involved the development of realistic scenarios, sound metrics, data sets, evaluation criteria, and documentation. Technology integration & Field demonstration: The project had significantly advanced the state-of-the-art research and practice in improving the cybersecurity of our nation’s power grid infrastructure against cyber threats. In particular, the proposed, designed, and deployed attack surface analysis and reduction algorithms and tools have contributed to significantly reducing the exposure and risk of the devices, substations, and the integrated SCADA/EMS/ DMS grid environment to cyber threat. Strong demonstration and evaluation techniques have verified the feasibility of the developed techniques on realistic cyber-physical testbeds and utility partner's real grid environment, and collaborative research and evaluation of attack surface reduction techniques (for wide-are monitoring and control) within a vendor (GE) EMS platform. The Attack Host Analyzer (AHA) tool that was developed through this project was made available through GitHub.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-CF2 CAF-LTAR Cook West

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-CF2 CAF-LTAR Cook West. This is the FLUXNET version of the carbon flux data for the site US-CF2 CAF-LTAR Cook West produced by applying the standard ONEFlux (1F) software. Site Description - CF2 has operated since May 2017 at the R.J. Cook Agronomy Farm outside of Pullman, Washington, and monitors the conventionally tilled side of a paired-catchment study that is part of the Longterm Agroecosystem Research (LTAR) site common experiment. CF2 is located in “Cook West,” a field that has been in conventional tillage since prior to 1998 and constitutes the Prevalent treatment of the common experiment. In this case, conventional tillage involves chisel plowing after the winter wheat phase of rotations (approximately every 3-4 years) and occasional harrowing. CF2 is paired with the CF1 tower location in “Cook East,” which has been in no-till since 1998 and represents the Alternative treatment of the common experiment. Both fields are planted in the same crops every year. Cook Agronomy Farm is in the high precipitation agroecological zone of the Columbia Plateau’s dryland cropping region. Crop rotations are wheat-based and include winter wheat, spring wheat, chickpea, spring canola, and winter peas. Wheat is the principal cash crop, with other crops grown in rotation for diversity, nutrient, and pest management. Soils are predominantly silt loam texture Mollisols in the Palouse, Thatuna, Naff soil series. The CF2 tower replaced US-RC2, which operated 2012-2016 in a neighboring field that was also conventionally tilled. CF2 and RC2 have distinct footprints, aspects, and soil series compositions.

Huggins, Dave↗
DOE OSTI2026

Supplement Analysis for the Proposed Shipment of High Burnup Research Cask to Idaho National Laboratory for Research and Development Purposes

DOE needs to continue with the HBUSFDP and transfer the HBURC to a facility capable of examination and testing of HBU SNF. These types of facilities do not exist at NAPS but are available at DOE national laboratories. The removal and eventual opening of the TN-32B research cask will provide DOE with the opportunity to gather data that will support continued safe storage of SNF at existing nuclear power plants, or a future federal consolidated storage facility. This effort will also help build public trust and confidence by demonstrating DOE’s ability to safely and securely transport commercial SNF using the certified Atlas rail consist.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency

Many workflows in high-energy-physics (HEP) stand to benefit from recent advances in transformer-based large language models (LLMs). While early applications of LLMs focused on text generation and code completion, modern LLMs now support orchestrated agency: the coordinated execution of complex, multi-step tasks through tool use, structured context, and iterative reasoning. We introduce the HEP Toolkit for Agentic Planning, Orchestration, and Deployment (HEPTAPOD), an orchestration framework designed to bring this emerging paradigm to HEP pipelines. The framework enables LLMs to interface with domain-specific tools, construct and manage simulation workflows, and assist in common utility and data analysis tasks through schema-validated operations and run-card-driven configuration. To demonstrate these capabilities, we consider a representative Beyond the Standard Model (BSM) Monte Carlo validation pipeline that spans model generation, event simulation, and downstream analysis within a unified, reproducible workflow. HEPTAPOD provides a structured and auditable layer between human researchers, LLMs, and computational infrastructure, establishing a foundation for transparent, human-in-the-loop systems.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
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↗