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At least 595 records · Page 33

Commercialization of a Non-Intrusive Optical (NIO) Technology to Measure Heliostat Optical Errors in Utility-Scale Concentrating Solar Power Plants: Final TCF Report

The drone-based Non-Intrusive Optical (NIO) Technology has been developed at NREL to allow for efficient and automated optical characterization of heliostats in Concentrating Solar Power (CSP) plants. For this project, the technology will be developed into a commercial tool package, including software and user-interface (UI), operations manual, and training and support services. The project team will partner with Tietronix to perform market assessment and stakeholder engagement, develop the tool package and business model, and perform data collection and analysis to demonstrate and refine the capabilities for use at a commercial plant. The team will collaborate with a commercial plant to conduct the data collection operations and provide optical error deliverables. The goal of the project is to advance the commercialization of the technology to a stage where a beta version can be demonstrated at additional commercial plants and developed into a licensable product.

14 SOLAR ENERGY↗

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↗

HERO WEC V1.0 - WEC-Sim Model

This zip file contains the files that are needed to simulate NREL's HERO WEC (hydraulic and electric reverse osmosis wave energy converter). This requires the user to have already installed WEC-Sim. In addition to the standard toolboxes that are required to run WEC-Sim the user will also need the Simscape Fluids and Simscape Driveline packages. In the zip file you will find the following: - HEROV1_HPTO.slx: Simulink-based WEC Sim model of the first gen (V1.0) Hydraulic PTO (power take-off) that was designed for the HERO WEC - wecSimInputFile.m: Input file needed to run the model - userDefinedFunctionsMCR.m: MCR (multi condition run) script that is needed if a use wants to simulate multiple wave conditions. - geometry (folder): Includes the geometry file that is needed for visualization - hydroData (folder): Includes the required WAMIT data to run WEC-Sim

16 TIDAL AND WAVE POWER↗

HERO WEC V1.0 - WEC-Sim Model (July 2024)

**This submission supersedes submission MHKDR-483** This submission file contains the files that are needed to simulate NREL's HERO WEC (hydraulic and electric reverse osmosis wave energy converter). This requires the user to have already installed WEC-Sim. In addition to the standard toolboxes that are required to run WEC-Sim the user will also need the Simscape Fluids and Simscape Driveline packages. The zip file (HERO_V1_WECSim_2024.zip) contains the following: - HERO_HPTO_2024.slx: Simulink-based WEC Sim model of the first gen (V1.0) Hydraulic PTO (power take-off) that was designed for the HERO WEC. This model has been updated since submission #483 based on in-laboratory experimental results. - wecSimInputFile.m: Input file needed to run the model - userDefinedFunctionsMCR.m: MCR (multi condition run) script that is needed if a use wants to simulate multiple wave conditions. - geometry (folder): Includes the geometry file that is needed for visualization - hydroData (folder): Includes the required WAMIT data to run WEC-Sim -HydVisualization.mlx: Visualization script to plot simulation results (not needed to run)

16 TIDAL AND WAVE POWER↗

Discovery of unknown posttranslational modifications by top-down mass spectrometry

Post-translational modifications can be important biomarkers of function within biological systems. Top-down mass spectrometry is a valuable tool for the characterization of combinatorial modifications on proteins such as histones that have a range of complex modifications. In this chapter, we present a top-down liquid chromatography-mass spectrometry experimental and data analysis workflow for the identification of novel modifications on proteins. The data analysis workflow presented here is reliant on the combination of proteoform searching using TopPIC and data visualization and fragment ion spectral confirmation using LcMsSpectator, part of the Informed-Proteomics package of software.

Wilson, Jesse W.↗

HERO WEC V1.0 - SolidWorks Model

The attached zip file includes a SolidWorks pack-and-go assembly of NREL's HERO WEC (hydraulic and electric reverse osmosis wave energy converter) V1.0. This model does not include all aspects of the design (i.e. RO (reverse osmosis) system, electrical enclosure, hose, cable) it only includes the WEC and PTO (power take-off) design.

16 TIDAL AND WAVE POWER↗

Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging - Software and Data

Software and testing data from the OH Hinsdale Wave lab for DOE-funded project on Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging. This project will perform foundational research and testing to accelerate the sector-wide development and deployment of marine energy converters to provide Power-At-Sea. Specifically, we seek to overcome known challenges and knowledge gaps for the successful co-design of coupled Wave Energy Converter (WEC)-Autonomous Underwater Vehicles (AUV) systems; systems designed and tested for WEC array system health and environmental monitoring applications. This project brings together an experienced, multi-institution, and multi-disciplinary team to focus on the co-design of marine energy (ME) technologies and AUV docking systems, including multi-body hydrodynamic modeling, active control, autonomy, and hardware interfaces necessary to enable new WEC-focused understanding, and allow for robust and ubiquitous AUV docking and recharging in real-world conditions.

16 TIDAL AND WAVE POWER↗

HERO WEC V1 Upgrades - SolidWorks Models (WEC, RO System, and Submersible Pump Housing)

The attached zip files include SolidWorks pack-and-go assemblies of NREL's HERO WEC (hydraulic and electric reverse osmosis wave energy converter), the reverse osmosis (RO) assembly, and the submersible pump assembly that is used to provide flow to the RO assembly in the electric configuration. These 3 models were upgraded in 2023 from their baseline models. The HERO WEC model does not include all aspects of the design (i.e. RO system, electrical enclosure, hose, cable), it only includes the WEC and PTO (power take-off) design. This model supersedes the old MHKDR model submission, linked below. The RO model file includes a SolidWorks (version 2023) pack-and-go assembly of the RO assembly that was used for HERO WEC as it was upgraded in 2023. This model ONLY includes the RO assembly and not the WEC, hoses, etc. The submersible pump enclosure model includes a SolidWorks (version 2023) pack-and-go assembly of the submersible pump assembly that is used to provide flow to the RO assembly in the electric configuration HERO WECas it was upgraded in 2023. This model ONLY includes the submersible pump assembly and not the WEC, RO system, hoses, etc. This work has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗

TEAMER: Water Tunnel Data from Testing the Pterofin Skimmer Concept

Pterofin's Skimmer concept relies on a flapping and pitching hydrofoil to extract hydrokinetic energy from water flows. The concept aims to utilize unsteady fluid dynamics phenomena (added mass, shed vorticity, and unsteady boundary layer development) to achieve higher lift coefficients, enabling increased power density of the hydrokinetic device and a fundamental shift in the rpm/torque scaling of the power take off compared with turbines. The Applied Research Laboratory at Penn State, in collaboration with Pterofin, designed and built a proof-of-concept flapping/pitching mechanism which was subsequently tested in ARL's 12-inch water tunnel facility. The mechanical power supplied to or extracted from the mechanism was measured for a range of hydrofoils provided by Pterofin over operating conditions including reduced frequency, Reynolds number, and the ratio between pitching and flapping amplitudes. The power lost to friction in the mechanism was removed from the net power measurement by means of a bare hub tare, with the resultant hydrodynamic power being used to calculate a mechanism-independent and non-dimensional power coefficient. The product of this effort is a dataset describing the power coefficient of a hydrofoil having simultaneous pitching and flapping motions, both of which are approximately sinusoidal. Power coefficients were collected for a range of primary design variables including: - Reduced frequency: 0.01 to 0.95 - Pitching/flapping peak angle ratio: 1.5 to 3.0 - Chord-based Reynolds number: 60,000 to 560,000 Secondary design variables relating to the hydrofoil geometry were explored including: - Aspect ratio - Planform shape - Section thickness distribution - Hydrofoil position relative to the pitching axis - Hydrofoil sweep angle relative to the pitching axis Measured data are provided in mean and time series formats. MATLAB scripts are provided which can be used to generate figures of time-averaged and phase-averaged hydrodynamic power coefficients calculated from the measured data. A complete description of the experiment and data reduction can be found in the Post Access Report for the Pterofin Skimmer test effort which will be available on the TEAMER website. This work was supported by the Pacific Energy Ocean Trust via a TEAMER award.

16 TIDAL AND WAVE POWER↗

MASK4 Test Campaign for Sandia WaveBot Device

This data and report details the findings from a wave tank test focused on production of useful work of a wave energy converter (WEC) device. The experimental system and test were specifically designed to validate models for power transmission throughout the WEC system. Additionally, the validity of co-design informed changes to the power take-off (PTO) were assessed and shown to provide the expected improvements in system performance. These data describe the "MASK4" wave tank test of the Sandia WaveBot device. The WaveBot device has been tested a number of times in different permutations at the US Navy's Maneuvering and Sea Keeping (MASK) basin. Each test in this series is referred to as MASK1, MASK2, etc. The WaveBot device was first tested in one degree of freedom (heave) in 2016. This MASK1 test focused primarily on system identification and modeling. After MASK1, major modifications were performed to improve the overall real-time control and measurement system, improve the heave drive train, and add surge and pitch degrees of freedom. The second set of testing, which was broken up in to two stages: MASK2A and MASK2B, focused on bench testing and closed-loop control performance as well as nonlinear modeling. MASK3 then focused on multi-input, multi-output modeling and control for maximization of electrical power. The attached report presents the results from MASK4, which focuses on detailed modeling of the power conversion chain and validation co-design principles by way of the introduction of a magnetic spring. The test log, report, and data from the MASK4 test of the WaveBot augmented with a tunable magnetic spring. Processing codes can be found at the Github link below.

16 TIDAL AND WAVE POWER↗

Hawaii Wave Surge Energy Converter (HAWSEC) OSU O.H. Hinsdale Basin

The following information and metadata applies to both the Phase I (Hydrodynamics) and Phase II (Full System Power Take-Off) zip folders which contain testing data from the OSU (Oregon State University) O.H. Hinsdale Wave Research Laboratory, from both OSU and the University of Hawaii at Manoa (UH). See zip folders provided further below in the downloads section. For experimental data of the full system, including PTO, see Phase II dataset. There are two main directories in each Phases's zip folder: "OSU_data" and "UH_data". The "OSU_data" directory contains data collected from their DAQ (data acquisition system), which includes all wave gauge observations, as well as body motions derived from their Qualisys motion tracking system. The organization of the directory follows OSU's convention. Detailed information on the instrument setup can be found under "OSU_data/docs/setup/instm_locations". The experiments conducted are documented in the "OSU_data/docs/daq_logs", which provides the trial number to the corresponding data located under "OSU_data/data" in several formats (e.g., ".mat" and ".txt"). Inside the trial directory, data is provided for each of the instruments defined in "OSU_data/docs/setup/instm_locations". The "UH_data" directory contains data collected from their DAQ. The data is stored in a ".tdms" file format. There are free plug-ins for Microsoft Excel and MathWorks MATLAB to read the ".tdms" format. Below are a few links providing methods to read in the data, but a Google search should identify alternatives sources if these no longer exist (valid as of January 2024): Excel: http://www.ni.com/example/27944/en/ MATLAB: https://www.mathworks.com/matlabcentral/fileexchange/30023-tdms-reader The Excel plugin is recommend to get a quick overview of the data. The UH data is organized by directory name, in which the sub-directories for each experiment contains a directory whose name defines the wave height and period for the experimental data within. For example, a directory name "H02_T0275" corresponds to an experiment with wave height 0.1m and a period of 2.75s. For random wave data, the gamma value is also included in the directory name. For example, a directory name "H02_T0225_G18" corresponds to an experiment with a significant wave height of 0.2m, a peak period of 2.25s, and a gamma value of 1.8, with each spectra being a TMA spectrum. For the free decay experiments, the directory name is defined by the initial angular displacement. For example, a directory name "ang05_run01" corresponds to an experiment with an initial angular displacement of 5 degrees. There is a dataset in the UH data for each corresponding experiment defined in the OSU DAQ logs. The ".tdms" data is output from the DAQ at fixed intervals. Therefore, if multiple files are contained within the folder, the data will need to be stitched together. Within the UH dataset, there are two input channels from the OSU DAQ providing a random square wave signal for time synchronization ("ENV-WHT-0010") and a high/low signal ("ENV-WHT-0012") to identify when the wave maker is active (+5V). The UH data is logged as a collection of channel outputs. Channels not in use for the OSU testing (either Phase I or Phase II) are marked "nan" below. If the sensor is disconnected, it will record noise throughout the experiment. Below are the channel definitions in terms of what they measure: GPS Time = time CYL-POS-0001 = position between flap and fixed reference CYL-LCA-0001 = force between flap and hydraulic cylinder REC-LPT-0001 = nan REC-HPT-0001 = nan REC-HPT-0002 = nan REC-HPT-0003 = nan HHT-HPT-0001 = pressure at exhaust ("head" only) REC-FQC-0001 = nan REC-FQC-0002 = nan HHT-FQC-0001 = flow at exhaust ("head" only) ENV-WHT-0001 = nan ENV-WHT-0002 = nan ENV-WHT-0003 = nan ENV-WHT-0010 = random signal from OSU DAQ ENV-WHT-0012 = high/low signal from OSU DAQ Also included is a calibration curve to convert the string pot data to flap pi...

16 TIDAL AND WAVE POWER↗

First-Scatter Source Generation in GADRAS

A new scatter calculation algorithm has been implemented in the Gamma Detector Response and Analysis Software (GADRAS) package that accounts for spectral effects of scattering materials not in the line of sight of the detector and the source. Previously, GADRAS would only apply scattering effects due to materials that fall between the source and detector. This new routine will allow better modeling of various scenarios including gamma imagers, collimated detectors, or traditional gamma detectors where scattering materials are present.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Uncertainty Quantification in GADRAS Inverse Modeling

The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Analysis on Evaluations of Monterey Bay Aquarium Research Institute’s Wave Energy Converter’s Field Data Using WEC-Sim and Gazebo: A Simulation Tool Comparison

Although many studies have validated wave energy converter (WEC) numerical models against scaled prototype experimental data, there remains a notable lack of validation using data from full-scale deployed WECs. This paper compares two numerical models of Monterey Bay Aquarium Research Institute’s Wave Energy Converter (MBARI-WEC), a two-body point absorber with an electro-hydraulic power take-off system (PTO). The models are implemented in WEC-Sim/Simscape and Gazebo Simulator. A statistical analysis of the models was performed, and field results were obtained to compare the models’ accuracy in predicting the RMS piston velocity, RMS motor speed, and mean electric power compared to field data for 56 observations across varying sea states. The Gazebo model demonstrated a closer agreement across all three parameters for a majority of the observations. When compared to the field data, the Gazebo and WEC-Sim models exhibited average mean electric power overestimations of 13% and 22%, respectively.

16 TIDAL AND WAVE POWER↗

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

Andresen, Christian G.↗

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗

pvplr-python: Python package implementation of PVplr for Performance Loss Rate (PLR) analysis

Due to software fragmentation, PV system modeling teams can be limited to language specific packages, preventing cross-sectional analysis of different modeling techniques and workflows. To this end, PVplr, a popular PV performance modeling R software package, has been ported to the Python programming language. To verify and test the robustness of the port, NSRDB data has been used to simulated PV installations at native resolution (~2 million Sites), with a variety of degradation rates, degradation patterns, and modules. Performance Ratios were calculated using the ported functions from pvplr-python and compared against Rdtools YoY values. Due to the complicated nature of degradation, a new metric has been proposed to quantify the performance loss of a system. The cumulative production loss, is the total amount of energy lost due to the degrading performance of the system. Cumulative production loss alleviates the problems with fitting linear functions to non-linear degradation. Cumulative Production loss was shown to better estimate the total lost revenue for non-linear degradation patterns. $XbX + UTC$ was found to most accurately predict the total lost revenue in simulated systems.

Kumar, Suraj↗