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DOE OSTI2025

Evaluating User Errors and Temporal Trends in Marine Fish Communities Using 360-Degree Underwater Photography

The use of environmental DNA (eDNA) sampling has been proposed as a complementary method to monitor fish species in marine environments, offering a non-invasive and potentially more efficient approach to marine species observations. eDNA monitoring could be especially useful in and around sites targeted for marine energy generation as these regions need regular monitoring that would be impractical with traditional techniques. Before we can fully rely upon eDNA, we must first verify its accuracy against other proven methods, such as the use of underwater photography. In this study, I deployed a 360-degree camera in the tidal channel of Sequim Bay once a month during several hours overlapping slack tide. I investigated how having multiple people identify and count fish on underwater images could affect the overall results. Using chi square tests in R, I compared my fish identifications and counts to those made by another intern on the same images recorded in August. I found significant differences in the number of species identified and the total individual counts between the two different datasets. I also tested the statistical differences in both Shannon diversity and Pielou evenness indices between the August, September, and November camera deployments using a Hutcheson t-test. Only one significant difference was found in the Shannon index comparisons, and none were found between the Pielou evenness comparisons. These findings show that if multiple identifiers are used to process underwater images, quality control checks must be made to reduce the potential for error. This also points toward the possibility to leverage more advanced image analysis processes, such as automated image analysis software. The findings from this study also show that the dynamics of marine fish communities can vary over a few months; however, further analysis is needed to determine the extent of the seasonal changes in Sequim Bay.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Distributed Resilience in High-Energy Physics Data Acquisition

Historical experience in the High-Performance Computing community teaches us that as computing systems grow, the instance of failures goes from rare to a regular occurrence. A survey of the growth in the size and complexity of Data AcQuisition (DAQ) networks in High-Energy Physics (HEP) experiments reveals that these networks are scaling exponentially, trending to a point where automated fault handling should be considered over the current manual practice, especially given the rarity of data such as in DUNE's mission to observe core-collapse supernovae. We propose a general system, DiDAQt, which is designed to provide fault detection and handling in HEP DAQs specifically, through MPI-like primitives that allow it to be added easily to existing systems. We evaluate the scalability and response time of a prototype on the FABRIC national testbed, with results indicating sufficient scalability for current and near-future DAQs as well as practical response times (under 1 microsecond decision time).

Wolosewicz, A. [IIT, Chicago]↗

DOE OSTI2026

FOCAL Campaign II/III: Applying Active Hull Controls Using Tuned-mass Dampers/Hull Flexibility and Internal Loads

Campaign II and III of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of the performance and loads of a scaled hull, with and without structural hull control. The floating platform was subjected to a variety of wave environments and controlled using tuned-mass dampers (TMDs) tuned to two of the systems natural frequencies (Platform Pitch and Tower-bending). The floating platform is fully instrumented to record a variety of parameters in real time such as platform dynamics, accelerations, and loads at different points in the structure. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. This testing was focused only on validation of wave loading, and wind conditions were not considered. As such the platform does not support a working turbine, and instead supports a structure designed to have the same mass properties as the 1:70 IEA 15MW Reference turbine. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Platform Static Offset (TMD off); LC 2.X - Platform Free-decays (TMD off); LC 3.X - Wave Cases (Regular Wave, Irregular Wave, Pink Noise Wave) with and without TMDs active. Detailed properties on the model system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaigns 2 and 3: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Hull. UMaine ASCC Report Number 23-56-1183.

17 WIND ENERGY↗

DOE OSTI2026

OC6 Project Phase IV: Validation of Numerical Models for Novel Floating Offshore Wind Support Structures

The fourth phase of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) aims to assess the ability of the tools to model a novel floating wind design. The design that was selected for this project is the TetraSpar designed by Stiesdal Offshore Technologies. The platform is a unique design that features a spar like stability with a very low center of gravity, but does not require deep-water ports. This combination is achieved with a system made of two separate steel structures, a hull at the water’s surface that supports the wind turbine tower, and a keel that is suspended below the main body. This lower keel has a very high density and drives the total platform’s low center of gravity. Scaled model tests of the TetraSpar design were performed in 2018-2019 by the University of Maine. The tests were performed with 1:43 Froude scaling. The data collected in the model tests provide a detailed description of the response of the platform in various controlled and relatively well understood conditions. Comparison with these data provides a good opportunity for validation of numerical models. The OC6 Phase IV work will focus on the specific parameters used in these scaled model tests. The load cases (LC) considered in this project are as follows: LC 1.X - Equilibrium and Static Offset Tests LC 2.X - Platform Free Decays LC 3.X - Turbine Thrust (Wind Only) LC 4.X - Regular and Irregular Waves (Wave only) LC 5.X - Combined Wind and Wave Loading

17 WIND ENERGY↗

DOE OSTI2025

Site A5 - NREL Scanning Lidar (Halo Allsky SN235) / Reviewed Data

This dataset contains lidar data that have been standardized and quality-controlled through NREL/FIEXTA/LiDARGO (https://github.com/NREL/FIEXTA/tree/main/lidargo). Standardization rearranges the lidar data into convenient range vs beamID vs scanID coordinates that facilitates data analysis. The scan geometry (i.e., azimuth, elevation) is shifted on a regular grid based on the most likely angles within the scan file. Quality control of radial wind speed is performed through a generalized version of the dynamic lidar filter (Beck and Kuhn, 2017).

17 WIND ENERGY↗

DOE OSTI2025

Site A1 - ARM Scanning Lidar (Halo XR) / Reviewed Data

17 WIND ENERGY↗

DOE OSTI2026

RHOD Site - NOAA PSL Wind Retrievals WINDoe / Derived Data

This dataset contains daily NetCDF files with horizontal wind profiles retrieved with the WINDoe retrieval (Gebauer and Bell 2024) at Rhode Island (RHOD). WINDoe retrievals datasets are also available at Nantucket Island (NANT, nant.windoe.z01.c1) and Block Island (BLOC, bloc.windoe.z01.c1). WINDoe is an optimal estimation algorithm to retrieve wind profiles combining multiple instruments. The code is available in this github repository (https://github.com/OAR-atmospheric-observations/WINDoe/tree/main) and the retrieval is described by Gebauer and Bell (2024). WINDoe allows combining the individual datasets and outputs into one profile taking into account the information and uncertainties of each dataset. The use of WINDoe minimizes data gaps and maximizes data availability, compared to using wind profiles from only one of the instruments. The regular height grid eases comparisons to numerical weather prediction models. Code modifications have been made that include reading in WFIP3 specific instruments, averaging Doppler lidar radial velocities at various azimuth angles to avoid overfitting, and allowing the user to define a height grid by the user in the vipfile. The instruments used as input to the retrieval are a radar wind profiler (low- and high resolution mode) providing data in and above the boundary layer, a scanning Doppler lidar usually providing data throughout the boundary layer, a profiling lidar providing data from 50 to 200 m at BLOC and NANT, and from 10 to 280 m at Rhode Island, and a surface tower (4 m at NANT and RHOD and 10 m at BLOC). From the scanning lidars, we used radial velocity measurements at 60 deg elevation angle at six different azimuth angles with a resolution of approximately 30 m along the line of sight and the lowest range gate at approximately 70 m. The wind profiles are retrieved with WINDoe up to 3.74 km with 10 m vertical resolution. The profiles are retrieved every 15 min at BLOC and NANT and every 60 min at RHOD.

17 WIND ENERGY↗

DOE OSTI2026

BLOC Site - NOAA PSL Wind Retrievals WINDoe / Derived Data

This dataset contains daily netcdf files with horizontal wind profiles retrieved with the WINDoe retrieval (Gebauer and Bell 2024) at Block Island (BLOC). WINDoe retrievals datasets are also available at Nantucket Island (NANT, nant.windoe.z01.c1) and Rhode Island (RHOD, rhod.windoe.z01.c1). WINDoe is an optimal estimation algorithm to retrieve wind profiles combining multiple instruments. The code is available in this github repository (https://github.com/OAR-atmospheric-observations/WINDoe/tree/main), and the retrieval is described by Gebauer and Bell (2024). WINDoe allows combining the individual datasets and outputs into one profile taking into account the information and uncertainties of each dataset. The use of WINDoe minimizes data gaps and maximizes data availability, compared to using wind profiles from only one of the instruments. The regular height grid eases comparisons to numerical weather prediction models. Code modifications have been made that include reading in WFIP3 specific instruments, averaging Doppler lidar radial velocities at various azimuth angles to avoid overfitting, and allowing the user to define a height grid by the user in the vipfile. The instruments used as input to the retrieval are a radar wind profiler (low- and high resolution mode) providing data in and above the boundary layer, a scanning Doppler lidar usually providing data throughout the boundary layer, a profiling lidar providing data from 50 to 200 m at BLOC and NANT, and from 10 to 280 m at Rhode Island, and a surface tower (4 m at NANT and RHOD and 10 m at BLOC). From the scanning lidars, we used radial velocity measurements at 60 deg elevation angle at six different azimuth angles with a resolution of approximately 30 m along the line of sight and the lowest range gate at approximately 70 m. The wind profiles are retrieved with WINDoe up to 3.74 km with 10 m vertical resolution. The profiles are retrieved every 15 min at BLOC and NANT and every 60 min at RHOD.

17 WIND ENERGY↗

DOE OSTI2026

NANT Site - NOAA PSL Wind Retrievals WINDoe / Derived Data

This dataset contains daily NetCDF files with horizontal wind profiles retrieved with the WINDoe retrieval (Gebauer and Bell 2024) at Nantucket Island (NANT). WINDoe retrievals datasets are also available at Block Island (BLOC, bloc.windoe.z01.c1) and Rhode Island (RHOD, rhod.windoe.z01.c1). WINDoe is an optimal estimation algorithm to retrieve wind profiles combining multiple instruments. The code is available in this github repository (https://github.com/OAR-atmospheric-observations/WINDoe/tree/main), and the retrieval is described by Gebauer and Bell (2024). WINDoe allows combining the individual datasets and outputs into one profile taking into account the information and uncertainties of each dataset. The use of WINDoe minimizes data gaps and maximizes data availability, compared to using wind profiles from only one of the instruments. The regular height grid eases comparisons to numerical weather prediction models. Code modifications have been made that include reading in WFIP3 specific instruments, averaging Doppler lidar radial velocities at various azimuth angles to avoid overfitting, and allowing the user to define a height grid by the user in the vipfile. The instruments used as input to the retrieval are a radar wind profiler (low- and high resolution mode) providing data in and above the boundary layer, a scanning Doppler lidar usually providing data throughout the boundary layer, a profiling lidar providing data from 50 to 200 m at BLOC and NANT, and from 10 to 280 m at Rhode Island, and a surface tower (4 m at NANT and RHOD and 10 m at BLOC). From the scanning lidars, we used radial velocity measurements at 60 deg elevation angle at six different azimuth angles with a resolution of approximately 30 m along the line of sight and the lowest range gate at approximately 70 m. The wind profiles are retrieved with WINDoe up to 3.74 km with 10 m vertical resolution. The profiles are retrieved every 15 min at BLOC and NANT and every 60 min at RHOD.

17 WIND ENERGY↗

DOE OSTI2026

FC Site 4.0 - NLR Scanning Lidar (Halo XR+ 235) / Standardized and Quality-Controlled Data

This dataset contains lidar data that have been standardized and quality-controlled through NatLabRockies/FIEXTA/LiDARGO (https://github.com/NatLabRockies/FIEXTA/tree/main/lidargo). Standardization rearranges the lidar data into convenient range vs beamID vs scanID coordinates that facilitate data analysis. The scan geometry (i.e., azimuth, elevation) is shifted on a regular grid based on the most likely angles within the scan file. Quality control of radial wind speed is performed through a generalized version of the dynamic lidar filter (Beck and Kuhn, 2017).

17 WIND ENERGY↗

DOE OSTI2026

FC Site 4.2 - NLR Scanning Lidar (Halo XR+ 199) / Standardized and Quality-Controlled Data

17 WIND ENERGY↗

DOE OSTI2026

FC Site 1.9 - NLR Scanning Lidar (Halo XR+ 200) / Standardized and Quality-Controlled Data

17 WIND ENERGY↗

DOE OSTI2026

U.S. Hydropower Development Pipeline Data, 2026

The U.S. Hydropower Development Pipeline dataset provides a comprehensive, regularly updated view of proposed and potential hydropower projects across the United States. This resource compiles information from federal agencies and other public sources to track non-powered dams considered for electrification, proposed hydropower facilities at stream reaches with no existing dams, conduit exemptions, and emerging pumped storage hydropower proposals. The dataset includes project characteristics such as location, development status, technology type, ownership category, and other attributes that support analysis of future hydropower trends. It is designed to help researchers, planners, policymakers, and stakeholders assess national‑scale development patterns, understand the evolving hydropower landscape, and explore opportunities and challenges associated with new hydropower deployment. The dataset is updated annually to reflect changes in project status, new proposals entering the pipeline, and projects that are cancelled, completed, or otherwise removed from active consideration. Note: Capacity additions to existing hydropower plants are not included in this database due to reliance on a proprietary data source.

Johnson, Megan [ORNL] (ORCID:0000000290141741)↗

DOE OSTI2021

“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to non-misinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will

5g↗

DOE OSTI2022

Hybrid quantum-classical algorithms for approximate graph coloring

We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) to MAX- k -CUT, the problem of finding an approximate k -vertex coloring of a graph. We compare this proposal to the best known classical and hybrid classical-quantum algorithms. First, we show that the standard (non-recursive) QAOA fails to solve this optimization problem for most regular bipartite graphs at any constant level p : the approximation ratio achieved by QAOA is hardly better than assigning colors to vertices at random. Second, we construct an efficient classical simulation algorithm which simulates level- 1 QAOA and level- 1 RQAOA for arbitrary graphs. In particular, these hybrid algorithms give rise to efficient classical algorithms, and no benefit arising from the use of quantum mechanics is to be expected. Nevertheless, they provide a suitable testbed for assessing the potential benefit of hybrid algorithm: We use the simulation algorithm to perform large-scale simulation of level- 1 QAOA and RQAOA with up to 300 qutrits applied to ensembles of randomly generated 3 -colorable constant-degree graphs. We find that level- 1 RQAOA is surprisingly competitive: for the ensembles considered, its approximation ratios are often higher than those achieved by the best known generic classical algorithm based on rounding an SDP relaxation. This suggests the intriguing possibility that higher-level RQAOA may be a potentially useful algorithm for NISQ devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Quantum Local Search with the Quantum Alternating Operator Ansatz

We present a new hybrid, local search algorithm for quantum approximate optimization of constrained combinatorial optimization problems. We focus on the Maximum Independent Set problem and demonstrate the ability of quantum local search to solve large problem instances on quantum devices with few qubits. This hybrid algorithm iteratively finds independent sets over carefully constructed neighborhoods and combines these solutions to obtain a global solution. We study the performance of this algorithm on 3-regular, Community, and Erdős-Rényi graphs with up to 100 nodes.

Tomesh, Teague↗

DOE OSTI2023

Binary Control Pulse Optimization for Quantum Systems

Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important to various quantum applications, including energy minimization and circuit compilation. In this paper we focus on discrete binary quantum control problems and apply different optimization algorithms and techniques to improve computational efficiency and solution quality. Specifically, we develop a generic model and extend it in several ways. We introduce a squared L 2 -penalty function to handle additional side constraints, to model requirements such as allowing at most one control to be active. We introduce a total variation (TV) regularizer to reduce the number of switches in the control. We modify the popular gradient ascent pulse engineering (GRAPE) algorithm, develop a new alternating direction method of multipliers (ADMM) algorithm to solve the continuous relaxation of the penalized model, and then apply rounding techniques to obtain binary control solutions. We propose a modified trust-region method to further improve the solutions. Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Infinite quantum signal processing

Quantum signal processing (QSP) represents a real scalar polynomial of degree d using a product of unitary matrices of size 2 × 2 , parameterized by ( d + 1 ) real numbers called the phase factors. This innovative representation of polynomials has a wide range of applications in quantum computation. When the polynomial of interest is obtained by truncating an infinite polynomial series, a natural question is whether the phase factors have a well defined limit as the degree d → ∞ . While the phase factors are generally not unique, we find that there exists a consistent choice of parameterization so that the limit is well defined in the ℓ 1 space. This generalization of QSP, called the infinite quantum signal processing, can be used to represent a large class of non-polynomial functions. Our analysis reveals a surprising connection between the regularity of the target function and the decay properties of the phase factors. Our analysis also inspires a very simple and efficient algorithm to approximately compute the phase factors in the ℓ 1 space. The algorithm uses only double precision arithmetic operations, and provably converges when the ℓ 1 norm of the Chebyshev coefficients of the target function is upper bounded by a constant that is independent of d . This is also the first numerically stable algorithm for finding phase factors with provable performance guarantees in the limit d → ∞ .

Dong, Yulong [Department of Mathematics, Universit↗