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

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optimization of Marine Energy Conversion Systems Through Modeling, Optimization, and CHIL Validation

The work aims to achieve optimal tidal energy conversion through a comprehensive approach of modeling, optimization, and control hardware-in-the-loop (CHIL) validation. By developing accurate models and employing optimization techniques, it seeks to identify efficient system configurations and control strategies. HIL validation will ensure the performance and reliability of the optimized tidal energy conversion system. The preparation of the present manual has been supported by the U.S. Department of Energy.

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DOE OSTI2021

Tidal Energy Resource Characterization, Velocity and Turbulence Measurements, Processed Data, Cook Inlet, AK, 2021

This submission contains processed datasets from a long-term deployment of 3 moorings and a transect survey of the proposed tidal energy site off the East Forelands in Cook Inlet, AK. The long-term mooring datasets were created from 8 instruments mounted on a Terrasond High Energy Oceanographic Mooring (THEOM) bottom lander and two Mid-Water Mooring (MWM) Stablemoor buoys from 1 July 2021 to 31 August 2021 (60 days). The west-most mooring (MWM1) was deployed at 60.720225 N, 151.436196 W in ~50 m of water. The middle mooring (THEOM) was deployed at 60.720703 N, 151.429500 W in ~52 m of water. The east-most buoy (MWM2) was deployed at 60.720081 N, 151.420896 W in ~50 m of water. Each Stablemoor carried three instruments: 1. A Nortek Vector acoustic Doppler velocimeter (ADV) mounted at the Stablemoor's nose. Data were recorded at 8 Hz on a 5 minute duty cycle every 20 minutes. Data was motion-corrected using the internal IMU and external ADCP bottom-track data and then bin-averaged into 4 minute bins and converted to the Principal (streamwise, cross-stream, vertical) coordinate system. (Note: 30 seconds were trimmed from the beginning and end of each 5 minute duty cycle to account for the filter end-effects from turning on and turning off the IMU.) 2. A down-looking Nortek Signature 1000 kHz acoustic Doppler current profiler (ADCP) mounted in the first Stablemoor instrument well. Data were recorded in 2 Hz with 5-beam burst and bottom-track enabled. Processed data has been averaged into 10 minute bins and converted into the Principal coordinate system. 3. An up-looking Nortek Signature 1000 kHz acoustic Doppler current profiler (ADCP) mounted in the second Stablemoor instrument well. Data were recorded at 4 Hz with 5 beam burst enabled. Processed data has been averaged into 10 minute bins and converted into the Principal coordinate system. Note: the down-facing ADCP on MWM1 failed on July 10th, 2021, only recording 9 days of data. Because ADV motion-correction required bottom track, the ADV from MWM1 also only has 9 days processed. Additionally, only 25 days of data were processed from the MWM2 ADV because it appeared to have been impacted by debris on 7/25. Two instruments were mounted on the THEOM (see MHKDR link further below for THEOM raw data): 4. A Nortek Vector acoustic Doppler velocimeter (ADV). Data were recorded at 8 Hz on a 5 minute duty cycle every 20 minutes. Data was bin-averaged into 5 minute bins, and converted to the Principal coordinate system. 5. A Nortek Signature 500 kHz acoustic Doppler current profiler (ADCP). Data were recorded in 4 Hz in the beam coordinate system from all 5 beams. Processed data has been averaged into 10 minutes bins and converted to the Principal coordinate system.

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DOE OSTI2023

Simulink Model of a New Energy EVG-005 5kW Hydropower Turbine

Simulink model for a New Energy 5kW hydropower turbine. ADCP data ("ds_streamwise_7_13.nc") and DC voltage, DC current, and rotor rotation observed from the New Energy EVG-005 Current Energy Converter (CEC) ("electrical_7_13_10ohms.nc") were collected at the Tanana River Test Site (TRTS) near Nenana Alaska. - Data was collected on July 13th, 2023 with a constant 10 ohms resistance applied with a DC load bank. - Simulink model is meant to resemble the electrical setup at the TRTS. - Model is initialized by running the "NewEnergy_2023_10hz.m" Matlab script. Then the Simulink model ("New_Energy_Model_PMSM.slx") can be run. - Results are processed with the "NewEnergy_2023_processing.m" Matlab code. The Matlab results are also saved in the "New_Energy_7_13_model_validation_results.mat" Matlab file. This can be directly loaded into the Matlab Workspace using the "Load()" command. - The timetable variables "electrical_model_downsampled" is the model results and the "electrical_limited" is the experimental data from the TRTS.

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DOE OSTI2023

CODAS Data from Oliktok Point, Beaufort Sea, Alaska

Cryosphere/Ocean Distributed Acoustic Sensing (CODAS) data collected from the Beaufort Sea, Alaska, using ~37.4 km of dark telecommunications fiber located at Oliktok Point, Alaska. Data were collected with a Silixa iDAS, using 10 m gauge length, 2 m spatial resolution, and 1000 Hz sample rate. Provided here are the DAS-recorded time series for the rapid refreeze event described in Baker & Abbott (2022) (see link below). This covers a date range of 2021-11-10 15:00 UTC to 2021-11-11 17:00 UTC. Data have been decimated to 100 Hz and 20 m (i.e., every 10th channel for 1831 channels, total), as used in Baker & Abbott (2022). Data have been extracted from raw format into 1-hour long .sac* files and organized into directories by channel number, spanning channels 100 to 18400. Time series units are nano-strainrate (nm/m/s). For distribution, data have been compressed into .zip files containing all time series files for 100 channels. *For information on the Seismic Analysis Code (SAC) file format: https://seiscode.iris.washington.edu/projects/sac

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DOE OSTI2023

Performance of Elevated Hinge Flap Wavemaker

This data includes a sweep of regular wave runs characterizing the performance of an elevated hinge flap wavemaker. Maximum wave heights were run for 13 periods in a range from 0.5 s to 6 s. Data is saved in several formats including 'raw' analog voltages, 'mat' matlab files,'txt' csv, and 'dat' ASCII.

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DOE OSTI2024

TEAMER: Drifting Hydrophone System - Block Diagram and Pre-Amplifier Calibrations

This data release is part of TEAMER RFTS 2, where the Cooperative Institute for Marine Resources Studies (CIMRS) at Oregon State University is performing hardware and software development and integration of four newly designed drifting hydrophone systems for underwater noise measurements at marine renewable energy projects. These new acoustic systems will provide advanced technology support available for use at both tidal and wave energy deployments adding additional resources to the limited amount of drifting hydrophone technologies that are available to the marine energy community The submission data contains 2 files plus a Post-Access Report: One file containing the calibration parameters for four (4) pre-amplifier boards used on the drifting hydrophones. Pre-amplifier boards are model "WB PREAMP REV6," with serial numbers; 28, 29, 30, and 31. One image file illustrating the system functional block diagram of the drifting hydrophone system configuration.

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DOE OSTI2024

Performance data for an axial-flow turbine with passive adaptive blades

To support the development of simulation tools for passive adaptive turbine rotors, an experimental data set from a laboratory-scale axial-flow turbine with passive adaptive blades is provided. The 0.45-meter diameter turbine was tested in the Alice C. Tyler Flume at the University of Washington. Blade and rotor loads were measured at 1 kHz using six-axis force/torque sensors while deflection and twist at the blade tip were tracked using a high-speed camera. This data submission includes a technical report, the raw and processed experimental data, and a README file explaining the file/folder structure and where to find the processing/plotting scripts used to produce the figures in the technical report.

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DOE OSTI2023

Cone Penetration Tests at the PacWave South Test Site

This ZIP archive contains cone penetration test data from the PacWave South Test Site. The data were collected in September 2023 aboard the ship Seacor Lee operating out of the Port of Newport, OR. The tests were performed by ConeTec using an AP van den Berg ROSON-100. The cone itself was a standard u2-type, 10 cm^2. A total of 22 soundings were performed.

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DOE OSTI2021

Version 2 - Tidal Energy Resource Characterization, Velocity and Turbulence Measurements, Cook Inlet, AK, 2021

Version 2 of MHKDR submission #475, which also contains the metadata for these measurements. Files here are stored in netCDF4 format, which can be read in Python using the "netcdf4" or "xarray" packages or in MATLAB using the "ncread()" function. Changes are as follows: MWM1 (mid-water mooring 1) ADV - Time length of data now ends when bottom track data is lost - Improved filtering and quality control of bottom track measurements - improved motion correction of velocity - Improved Doppler noise subtraction - improved velocity, turbulent kinetic energy (TKE), Reynolds stress, time interval (TI), and dissipation rate estimations MWM1 Down-Looking ADCP - Improved filtering and quality control of bottom track measurements - improved motion correction of velocity - Improved Doppler noise subtraction - improved TI and dissipation rate estimations - Removed TKE calculations - accuracy could not be verified MWM1 Up-Looking ADCP - Improved Doppler noise subtraction - improved TI and dissipation rate estimations - Removed TKE calculations - accuracy could not be verified - Velocity unchanged MWM2 ADV - Removes data after instrument was hit by debris - Improved filtering and quality control of bottom track measurements - improved motion correction of velocity - Improved Doppler noise subtraction - improved velocity, TKE, Reynolds stress, TI, and dissipation rate estimations MWM2 Down-Looking ADCP - Improved filtering and quality control of bottom track measurements - improved motion correction of velocity - Improved Doppler noise subtraction - improved TI and dissipation rate estimations - Removed TKE calculations - accuracy could not be verified MWM2 Up-Looking ADCP - Improved filtering and quality control of bottom track measurements - improved motion correction of velocity - Improved Doppler noise subtraction - improved TI and dissipation rate estimations - Removed TKE calculations - accuracy could not be verified THEOM (bottom lander) ADV - Improved Doppler noise subtraction - improved TKE, Reynolds stress, TI, and dissipation rate estimations - Velocity unchanged THEOM ADCP (up-looking) - Improved Doppler noise subtraction - improved TKE, TI, and dissipation rate estimations - Removed vertical TKE component (w'w' bar) - Velocity unchanged

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Underwater Target Detection Software Demonstration on the RivGen Turbine

This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95). - Contents - Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format. - Requirements - Python3.8+ is required to install and run the train and validation script. The README.md provides instruction for installing the requirements from the requirements.py file. - Instructions - The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.

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DOE OSTI2019

High-Resolution Regional Wave Hindcast for Hawaii

This report summarizes modeling efforts for hindcasting of wave climate within the exclusive economic zone (EEZ) around the state of Hawaii. Specifically, the report provides detailed description of model mesh development, sensitivity analysis of the configuration of model parameters, data used for model forcing and validation, model skill assessment, and model outputs for resource characterization. The unstructured, nested-grid modeling approach incorporates a regional high-resolution SWAN (Simulating WAves Nearshore) model within the global-regional WAVEWATCH III® (WW3) wave model. The models were configured to comply with the International Electrotechnical Commission (IEC) Technical Standards for wave energy resource characterization. The SWAN model domain covers the entire EEZ with a spatial resolution of approximately 300 m in the nearshore region. Two levels of WW3 nested grids, from 0.5 arc-degree at global to 7.5 arc-minute at regional scale, were used to provide boundary conditions that drive the high-resolution nearshore SWAN model. The WW3 and SWAN models were forced by 0.5 arc-degree Climate Forecast System Reanalysis wind and 5 km regional Weather Research and Forecasting wind, respectively. Wave resource and bulk wave parameters were simulated for a 32-year period from 1979 to 2010, and were subsequently validated using wave buoy data within the model domain. Model performance was evaluated using a set of error statistics. Overall, the model shows good skills in reproducing the wave climate in the Hawaii EEZ in both spatial and temporal domains. Comparisons of time series and scatter plots for the six IEC resource parameters and the associated error statistics tables, and horizontal two-dimensional plots of annually averaged and monthly averaged IEC parameters are provided.

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DOE OSTI2019

A summary of the Advanced WEC Dynamics and Control project

This report serves as a comprehensive summary of the work completed by the "Advanced WEC Dynamics and Controls projecr during the period of 2013-2019. This project was first envisioned to simply consider the question of designing a controller for wave energy converters (WECs), without a complete recognition of the broader considerations that such a task must necessarily examine. This document describes both the evolution of the project scope and the key findings produced. The basic goal of the project has been to deliver tractable methodologies and work flows that WEC designers can use to improve the performance of their machines. Engineering solutions, which may offer 80% of the impact, but require 20% of the effort compared to a perfect result (which may be many years of development down the road) were preferred. With this doctrine, the work of the project often involved translating existing methods that have been successfully developed and applied for other fields, into the application area of wave energy.

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DOE OSTI2020

OES-Environmental 2020 State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World. Report for Ocean Energy Systems (OES)

This report summarizes the state of the science related to marine renewable energy devices and their interactions with and effects on the marine environment, the animals that live there, and the habitats that support them. This report serves an update and a complement to the 2016 Annex IV report. https://tethys.pnnl.gov/publications/state-of-the-science-2020

03 NATURAL GAS↗
DOE OSTI2020

2020 State of the Science Report, Chapter 1: Marine Renewable Energy and Ocean Energy System

Research, development, and deployment of marine renewable energy (MRE) conversion technologies that harvest all forms of ocean renewable resources are being advanced around the world. The potential benefits derived from capturing the abundant energy of tides, waves, ocean currents, as well as thermal and salinity gradients, continue to drive the development of the emerging MRE industry. Stakeholder understanding of the potential benefits of MRE as a renewable energy source is informed by increased science-based understanding of the potential effects of MRE installations worldwide. The international Ocean Energy Systems (OES)-Environmental collaboration continues to promote global technology cooperation and information exchange to accelerate environmentally acceptable development of viable ocean energy systems. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-1-MRE-OES

13 HYDRO ENERGY↗
DOE OSTI2020

2020 State of the Science Report, Chapter 3: Collision Risk for Animals around Turbines

The potential for marine animals to encounter and collide with turbines, especially tidal and river turbines, along with the biological, ecological, and regulatory consequences of any such interactions, remain active areas of research and topics of global interest. Uncertainty and knowledge gaps associated with collision risk continue to present challenges within consenting/permitting (hereafter consenting) processes for turbine developments. Consequently, collision risk continues to be the focus of significant research effort, which in recent years has included environmental monitoring of operational devices and arrays. This chapter addresses the overall progress and growth in knowledge across this topic area, and specific progress related to marine mammals, fish, and seabirds. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-3-collision

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DOE OSTI2020

2020 State of the Science Report, Chapter 8: Encounters of Marine Animals with Marine Renewable Energy Device Mooring Systems and Subsea Cables

Many marine renewable energy (MRE) technologies, including floating or midwater wave and tidal devices, require mooring systems (i.e., mooring lines and anchors) to maintain their position within the water column or on the sea surface. In the case of some devices such as tidal kites, these lines and cables can be highly dynamic. An array of non-bottom-mounted devices may also include transmission cables within the water column interconnecting devices to one another, or to offshore substations or hubs on the seabed. The potential for these lines and cables to present hazards for marine animals that may become entangled or entrapped in them, or confused by their presence remains an issue of uncertainty. The degree to which mitigation to avoid or reduce entanglement risk might be required for future MRE installations is yet to be determined, pending greater understanding of the actual nature of the risk. In this chapter, the entanglement or entrapment of a marine animal is defined as the cause to become caught in a system without possibility of escaping. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-8-moorings

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DOE OSTI2020

2020 State of the Science Report, Chapter 10: Environmental Monitoring Technologies and Techniques for Detecting Interactions of Marine Animals with Turbines

The greatest potential risk from turbine operation continues to be perceived by regulators and other stakeholders to be that of marine animals colliding with turbine blades. These potential interactions are the most difficult to observe using common oceanographic instruments and must be undertaken in parts of the ocean where fast moving water and high waves make studies challenging. However, our collective understanding of the effects of marine renewable energy (MRE) devices on marine animals and their habitats has improved since the publication of the 2016 State of the Science report. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-10-environmental-monitoring

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DOE OSTI2020

2020 State of the Science Report, Chapter 11: Marine Spatial Planning and Marine Renewable Energy

Marine spatial planning (MSP) is advocated internationally as an improved approach to managing marine activities that addresses competing sectors and balances environmental, social, and economic interests. The benefits of MSP are cited as being increased transparency and certainty for industry, improved environmental protection, reduced sectoral conflicts, and providing opportunities for synergies. Approaches to implementation of MSP vary by country and sometimes within countries. As a relatively new and novel approach to managing marine activities, it can be difficult to determine when success has occurred or what might constitute more effective and efficient management systems. The growth of marine renewable energy (MRE) will result in the increasing use of sea space and potential for conflict with existing marine uses, both of which can be addressed, in part, through implementation of MSP. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-11-marine-spatial-planning

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