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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Verification and Validation of Model-Scale Turbine Performance and Control Strategies for the IEA Wind 15 MW Reference Wind Turbine

To enable the fast growth of the floating offshore wind industry, simulation models must be validated with experimental data. Floating wind model-scale experiments in wind–wave facilities have been performed over the last two decades with varying levels of fidelity and limitations. However, the turbine controls in these experiments have considered only limited control strategies and implementations. To allow for control co-design, this research focuses on implementing and experimentally validating more advanced turbine control actions and strategies in a wind–wave basin for a 1:70-scale model of the International Energy Agency’s wind 15 MW reference wind turbine. The control strategies analyzed include torque control, collective pitch control, and transition region control (setpoint smoothing). Our experimental and numerical results include the effects of varying rotor speeds, blade pitches, and wind environments on the turbine thrust and torque. Numerical models from three different software tools are presented and compared to the experimental results. Their ability to effectively represent the aero-dynamic response of the wind turbine to the control actions is successfully validated. Finally, turbine controller tuning parameters based on the derivatives of thrust and torque are derived to allow for improved offshore wind turbine dynamics and to validate the ability of modeling tools to model the dynamics of floating offshore wind turbines with control co-design.

17 WIND ENERGY↗
DOE OSTI2025

Ocean-Powered Oyster Tumbling: A Review of Techniques and Opportunities for Emission Reductions

Oysters perform critical roles in shoreline ecosystems by improving water quality, providing habitat for species, and preventing erosion. These ecosystem functions are present even when oysters are farmed. Because of this, and the lack of need for nutrient inputs, oyster farming is often viewed as environmentally friendly. However, fossil fuels play a large part in oyster farming practices. Fossil fuels are used to power boats, tools, and farming equipment. Oyster tumbling machines, which are used to control biofouling and produce a desirable shape and size, use a significant amount of energy and are often powered by diesel generators. As the oyster farming industry grows and practices such as integrated multi-trophic aquaculture expand, decarbonization of the industry becomes more important. One solution may be “ocean-powered” tumbling, whereby oyster grow-out gear is designed to use a range of ocean movements to tumble oysters gradually as they grow. This solution eliminates the need for fossil fuel-powered tumblers and tends to be less labor intensive. A wide range of ocean-powered gear is used by farms across the United States. New approaches and designs are being explored, making ocean-powered oyster tumbling accessible in different environments. Water movements at oyster farms are primarily driven by tidal exchange, currents, wind waves, or a combination. This paper compares methods of ocean-powered tumbling, explores the transition from standard fossil fuel-powered tumbling techniques to ocean-powered tumbling, and estimates the emission reductions of decarbonizing oyster tumbling practices.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

IEA Wind Task 49: Reference Site Conditions for Floating Wind Arrays

The commercial-scale deployment of floating offshore wind (FOW) projects is expected to take place in a diverse range of sites that may differ significantly from existing fixed-bottom projects. FOW farms are particularly sensitive to the water depth and the meteorological and oceanographic (metocean) and geotechnical conditions at the project site due to the wave-induced system motions and loads as well as the anchoring system constraints imposed by the seafloor conditions. Uncertainty around the site conditions will permeate through all aspects of project design, leading to suboptimal and overly conservative designs, increased costs, and adversely affected performance. As FOW expands into a global industry, metocean and geotechnical conditions will increasingly vary for projects located in different geographic regions or in far-from-shore, deep-water sites. This study represents the outputs of work package 1 of International Energy Agency Wind Task 49, which focuses on the integrated design of floating wind arrays. The primary goal of this study is to establish the type of parameters and constraints required to characterize FOW array reference sites; provide a realistic and publicly available set of reference site conditions to the FOW community as a baseline set of data for individual research projects; identify and categorize any critical gaps in the existing data or methodologies required to define reference site characteristics; and inform and support the design of reference FOW arrays. A building block concept was developed for synthesizing reference sites for the design of FOW arrays. The building blocks include three classes of site conditions focusing on the techno-economic design of FOW projects: metocean conditions, seabed conditions, and coastal infrastructure. All reference site data produced and collected in this study are publicly available.

17 WIND ENERGY↗
DOE OSTI2024

Animal displacement from marine energy development: Mechanisms and consequences

For marine wave and tidal energy to successfully contribute to global renewable energy goals and climate change mitigation, marine energy projects need to expand beyond small deployments to large-scale arrays. However, with large-scale projects come potential environmental effects not observed at the scales of single devices and small arrays. One of these effects is the risk of displacing marine animals from their preferred habitats or their migration routes, which may increase with the size of arrays and location. Many marine animals may be susceptible to some level of displacement once large marine energy arrays are increasingly integrated into the seascape, including large migratory animals, non-migratory pelagic animals with large home ranges, and benthic and demersal mobile organisms with more limited ranges, among many others. Yet, research around the mechanisms and effects of displacement have been hindered by the lack of clarity within the international marine energy community regarding the definition of displacement, how it occurs, its consequences, species of concern, and methods to investigate the outcomes. This review paper leveraged lessons learned from other industries, such as offshore development, to establish a definition of displacement in the marine energy context, explore which functional groups of marine animals may be affected and in what way, and identify pathways for investigating displacement through modeling and monitoring. In the marine energy context, we defined displacement as the outcome of one of three mechanisms (i.e., attraction, avoidance, and exclusion) triggered by an animal's response to one or more stressors acting as a disturbance, with various consequences at the individual through population levels. The knowledge gaps highlighted in this study will help the regulatory and scientific communities prepare for mitigating, observing, measuring, and characterizing displacement of various animals around marine energy arrays in order to prevent irreversible consequences.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

How an Autonomous Offshore Power System Can Transform the Ocean Economy - A Hypothetical Case Study Utilizing an Autonomous Offshore Power System in Northern Lights Carbon Capture and Storage Project

An Autonomous Offshore Power System (AOPS) provides in-situ power, energy storage, real-time data and communications support, asset management, and other capabilities at sea. It has applications for all offshore industries: energy, defense and security, aquaculture, science and research, and communications. Furthermore, this paper highlights how an AOPS can reduce cost, complexity, and carbon-intensity for existing offshore operations and enable new capabilities for offshore industry leaders. This new AOPS technology has two primary advantages. First, it unlocks the autonomous, electric future of the ocean economy via ‘local’ power generation and energy storage, in addition to real-time connection to the data cloud. Second, it helps enable a material change in the global energy mix through cost-effective, reliable generation and storage technology for use cases including mobile/static data-gathering and reporting systems, operating equipment, and charging networks for uncrewed surface vessels. AOPS technology will help transform the ocean economy, and thus has implications for offshore industry leaders as they push to reduce costs today and make an autonomous and decarbonized future possible.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Time-Lapse Vertical Seismic Profiling (VSP) for CO 2 Storage in a Depleted Oil Field in Northern Michigan

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. This report describes the Dover 33 VSP study to test the effectiveness of time-lapse Vertical Seismic Profile (VSP) for detecting and delineating a plume of more than 271,000 tonnes of CO2 injected into the Brown Niagaran and A-1 Carbonate formations within the Dover 33 reef between March 2013 and September 2016. Five 2D walkaway VSP (WVSP) source lines were acquired by SIGMA3 in September 2016 to investigate the possible time-lapse response in both P-wave and PS-wave seismic data. The data was compared to the same survey geometry acquired in March 2013 by SR2020. The seismic energy was recorded into an 80-level, three-component geophone array deployed into the Dover 1-33 well and placed just above the reef. P-wave and PS-wave reflection images were produced for each of the three source lines from both the 2013 baseline and 2016 monitor surveys. The images were then compared to look for changes in the reflectivity at and around the injection location that might indicate how the CO 2 has moved over this time period. This technique revealed several localized areas with sizable impedance differences inside the reef where CO 2 would be expected; however, a large number of similar impedance “hotspots” were also detected outside the reef in areas where injected CO 2 would not be expected. Therefore, a second analysis that involved calculating P-wave and S-wave travel time differences between the 2013 and 2016 VSPs was conducted to look for a change that could be caused by the CO 2 plume.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

First High-Gradient Results of UED/UEM SRF Gun at Cryogenic Temperatures

Benefiting from the rapid progress on RF photogun technologies in the past two decades, the development of MeV range ultrafast electron diffraction/microscopy (UED and UEM) has been identified as an enabling instrumentation. UEM or UED use low power electron beams with modest energies of a few MeV to study ultrafast phenomena in a variety of novel and exotic materials. SRF photoguns become a promising candidate to produce highly stable electrons for UEM/UED applications because of the ultrahigh shot-to-shot stability compared to room temperature RF photoguns. SRF technology was prohibitively expensive for industrial use until two recent advancements: Nb₃Sn and conduction cooling. The use of Nb₃Sn allows to operate SRF cavities at higher temperatures (4K) with low power dissipation which is within the reach of commercially available closed-cycle cryocoolers. Euclid is developing a continuous wave (CW), 1.5-cell, MeV-scale SRF conduction cooled photogun operating at 1.3 GHz. In this paper, we present first high gradient results of the gun conducted in liquid helium.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2021

Short term collaborations of the NEAMS Center of Excellence: third wave

A primary goal of the Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to develop the next generation of simulations tools to support the design and deployment of commercial nuclear power. The mission of the NEAMS Center of Excellence for Thermal-fluids Applications in Nuclear Energy is to advance this goal by providing leadership, best practices, research, and support and training for computational thermal-fluids applications in nuclear energy. This is accomplished by leveraging expertise in the NEAMS tools to solve challenging problems in fluid flow and heat transfer. The NEAMS tools provide simulation capabilities covering a range of temporal and spatial scales and the Center connects stakeholders in industry with the expertise to use these tools to their fullest. This addresses a pressing need, facilitating advanced reactor development and commercialization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Evaluation of New Opportunities for Marine Energy To Power the Blue Economy: Green Hydrogen and Marine Carbon Dioxide Removal

This report examines the intersection of marine energy and the sectors of green hydrogen and marine Carbon Dioxide Removal, taking into account potential synergies, market maturity, barriers, policy enablers, and sustainability perspectives. Through desk research and interviews with a diverse range of subject matter experts from academia, government, industry, venture capital, and non-profit sectors, these emergent sectors were identified as the most promising new opportunities for marine energy integration beyond those explored in the 2019 Powering the Blue Economy Report. The findings presented are the combined result of desk research and information gathered through these interviews.

13 HYDRO ENERGY↗
DOE OSTI2023

UMERC Materials and Manufacturing Workshop

On October 4, 2023, DOE's Water Power Technology Office (WPTO) hosted a half-day workshop as part of the annual UMERC conference. The workshop brought together universities, industry, DOE offices, and national labs sharing their work, outcomes, capabilities, and critical gaps around materials and manufacturing (M&M) in the marine energy industry. There were breakout sessions for groups to make connections and explore paths forward to advance marine energy. The stated objectives included the following: 1) identify developers and universities highest priority M&M needs to reduce cost and improve performance to inform future DOE funding priorities; 2) raising awareness across the industry, around universities' M&M capabilities and key M&M projects, around developers' M&M needs, around national labs' M&M projects, capabilities, and opportunities to support research and deployment, and around TEAMER, IEC TC114, and other DOE offices that can support M&M research through resources and financial assistance.

manufacturing↗
DOE OSTI2025

Feasibility of Multi-Use Ocean Thermal Energy Conversion (OTEC) Platforms

Tropical islands and coastal communities in the United States and internationally suffer from high energy costs, unreliable electrical supplies, poverty, and underemployment, which are all being exacerbated by climate change. Multi-use Ocean Thermal Energy Conversion (OTEC) systems could align with the goals and values of these underserved and remote communities as scalable platforms that can provide baseload power, freshwater, and food, as well as potentially support new industries. The cold, nutrient-rich deep seawater needed for OTEC’s power cycles has many promising byproduct applications such as seawater air conditioning (SWAC) for residential and industrial cooling, enhancing aquaculture farms (e.g., cooling and nutrients for seaweed, shellfish, finfish), producing freshwater through desalination, and generating efuels such as ammonia and hydrogen.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Can an Efficient Nuclear Energy Industry Still be Resilient? On the Nature of Efficiency and Resiliency in Complex Systems

At the time of writing, the nuclear industry is on the precipice of a long-hoped for, but as-yet-unrealized, “nuclear renaissance.” Nuclear power plants in the United States and around the globe are aging, emphasizing the criticality of this moment. The importance of carbon free power sources combined with advancements in nuclear technology and innovation have brought a new potential landscape. As existing reactors and utilities cope with increased economic pressure by seeking out efficiencies the push towards novel nuclear platforms like microreactors and fission batteries will only increase this quest for efficiency gains, potentially at the detriment of safety. This paper argues that the concepts of efficiency and resiliency are fundamentally linked and that the change in one necessarily affects the other. As nuclear power is known for being incredibly resilient, this dichotomy needs attention from the current wave of research towards nuclear power efficiencies. This paper argues for resiliency at any cost. We argue that this resilience-first approach is the right path for nuclear power to take in this net-zero future. The paper is laid out as follows. Section 1 introduces the economic context and resilient nature of nuclear power. Section 2 discusses efficiency, resiliency, and the struggle between the two in nuclear power. Section 3 exemplifies our argument through case studies in the aviation and manufacturing industries and extends the argument to the nuclear industry. Section 4 concludes with a discussion of the implications of this idea.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Clearing a Path to Commercialization of Marine Renewable Energy Technologies Through Public–Private Collaboration

Governments are increasingly turning toward public–private partnerships to bring industry support to improving public assets or services. Here, we describe a unique public–private collaboration where a government entity has developed mechanisms to support public and private sector advancement and commercialization of monitoring technologies for marine renewable energy. These support mechanisms include access to a range of skilled personnel and test facilities that promote rapid innovation, prove reliability, and inspire creativity in technology development as innovations move from concept to practice. The ability to iteratively test hardware and software components, sensors, and systems can accelerate adoption of new methods and instrumentation designs. As a case study, we present the development of passive acoustic monitoring technologies customized for operation in energetic waves and currents. We discuss the value of testing different systems together, under the same conditions, as well as the progression through different test locations. The outcome is multiple, complementary monitoring technologies that are well suited to addressing an area of high environmental uncertainty and reducing barriers to responsible deployment of low-carbon energy conversion systems, creating solutions for the future.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

A Technical-Economic Assessment of Brackish Water Treatment for Fossil Power Plant Cooling with Reduced Environmental Impact

The power sector accounts for approximately 40% of freshwater use in the U.S. Reusing non-traditional water resources is critical for alleviating water stress in the western United States. The purpose of this study is to conduct a techno-economic system analysis to evaluate the potential of brackish water treatment for wet cooling towers at existing thermo-electric generating units (EGUs). It aims to address current knowledge gaps in support of non-traditional water resources planning and the electric power industry's water decision- and policy making. An engineering-economic model was developed and combined with DuPont's water application value engine (WAVE) v1.82 to evaluate the viability of retrofitting reverse osmosis-based brackish water treatment systems to individual existing EGUs and then aggregating the unit-level results to the fleet level. In the base case, brackish water treatment can reduce freshwater consumption of electric generation units by 94–100% while increasing the cost of electric generation by approximately 10% and have marginal impacts on the units' parasitic loads. When zero liquid discharge systems are applied to brackish water brine treatment, the cost of electric generation and freshwater consumption savings and energy penalty to the power plant significantly increase. These costs are even higher than dry cooling deployment in lieu of wet cooling towers. The choice of freshwater savings between brackish water desalination and dry cooling deployment depends greatly on how the concentrated brines are managed. Multiple factors must be considered when supporting decision-making in water management and planning in power sectors, which include non-traditional water quantity and quality as well as trade-offs among technical performance, economics, and environmental impacts.

Wu, Zitao↗
DOE OSTI2021

NREL Programs Channel Students Toward the Future Water Power Workforce

Water power is a flourishing segment of the renewable energy industry, and resilient, reliable technologies like marine energy and hydropower are poised to play a significant role in our energy demands in the years to come. Continuing this trajectory of growth, however, requires new talent and innovation. As part of the effort to thoughtfully attract the best and most qualified workers, the National Renewable Energy Laboratory's (NREL's) Science, Technology, Engineering, and Mathematics (STEM) and workforce development programs raised student awareness of and made connections to water power career opportunities throughout fiscal year 2021.

HYDRO ENERGY,TIDAL AND WAVE POWER↗
DOE OSTI2020

Design of Stand-Alone Cryomodule based on Superconducting Quarter-Wave Resonator

Present superconducting accelerators require a central refrigeration system and piping to distribute liquid helium and nitrogen. Stand-alone cryocooler-based systems are of significant interest both to scientific and industrial customers, as they allow applications at facilities where no cryogenic equipment and expertise exists, where it is undesirable to develop additional cryogenic systems, or where a mobile system that can be transported between beamlines or facilities is required. The technical challenge of this approach is to minimize the heat losses to the liquid helium so that commercially available, self-contained cryocoolers are able to handle the thermal load. This paper presents an improved design for a stand-alone quarter-wave resonator (QWR) cryomodule a based on commercially available cryocoolers. This cryomodule has a particular application in the ATLAS intensity upgrade project where it will house a 72.75 MHz QWR for beam energy adjustments to experimental areas, where no helium cryogenic systems exist. The described cryomodule may also be the critical part of a turnkey system with application to material processing, semiconductor manufacturing, food irradiation and homeland security. The development of low heat loss peripheral components reduces cost and widens applicability of the system. We will discuss the cryomodule and RF coupler designs, including numerical thermal and structural analyses.

43 PARTICLE ACCELERATORS↗