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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 OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2023

As the United States' largest source of renewable energy, wind is already playing a vital role in the nation's shift to electricity generated from sustainable resources. Scientists, engineers, and analysts at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) are propelling our country closer to this clean energy future with new efficient, reliable, and cost-competitive wind technologies and deployment strategies. In Fiscal Year 2023 (FY 2023), NREL received national acclaim for recent wind technology breakthroughs, applauded sustained decades of progress in wind energy research, and heralded new initiatives to support the next generation of wind development. This report and NREL's achievements of the last year set the stage for innovative research to come. Two NREL software innovations used to assess wind technology options were recognized with R&D 100 Awards. The Renewable Energy Potential (reV) model helps planners, developers, and researchers calculate wind energy capacity, generation, and cost for specific facilities, fleets and scenarios. NREL's Simulation and Emulation for Advanced Systems validates transmission and distribution solutions to minimize implementation risks. Both of these tools can be applied to a range of renewable solutions to cut costs and improve the reliability of systems in even the most remote locations. The laboratory commemorated landmark anniversaries for wind energy research facilities and programs managed by NREL. The National Wind Technology Center (NWTC) celebrated 30 years as a world-class hub for renewable energy research. An important component of the NWTC on NREL's Flatirons Campus, the one-of-a-kind 7-megawatt (MW) Controllable Grid Interface wind energy research apparatus, turned 10 this year. The Collegiate Wind Competition completed its tenth annual competition in May 2023. Six years after the grand challenges in wind energy science were first compiled, NREL brought together more than 100 wind energy experts from 15 countries to revisit the field's most pressing research needs. Other key new research initiatives examined ways to simultaneously address the needs for skilled wind energy workers and equitable job opportunities. NREL released the first national-level report evaluating offshore wind industry workforce gaps and ways to build a large, diverse pool of job candidates. The lab also co-hosted the International Partnering Forum's Offshore Wind Workforce Summit as part of its ongoing efforts to address the special challenges of that industry sector. NREL research and development (R&D) for the DOE Wind Energy Technologies Office (WETO) continues to build momentum in its efforts to combat climate change, create clean energy jobs, and promote energy justice. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2023 (between Oct. 1, 2022, and Sept. 30, 2023).

accomplishments↗
DOE OSTI2025

Gas Hydrate Film Growth in Microfluidic Channels for Carbon Dioxide Capture and Sequestration Applications

Gas or clathrate hydrates are a solid, crystalline compound composed of water and guest molecules that typically form at high pressure and low temperature conditions. Carbon dioxide (CO2) hydrates may be involved in several carbon dioxide capture and sequestration (CCS) applications, including CO2 pipeline transportation and CO2 offshore sequestration. Within these applications, the formation mechanism and kinetics must be well understood to manage the CCS processes, either by preventing or promoting hydrate formation. In this work, a high-pressure glass microfluidic reactor is used in tandem with visual microscopy and in-situ Raman spectroscopy to study both the morphological and kinetic behavior of gas hydrate crystals. Subcooling, pressure, and CO2 flow rate are investigated for their impact on the thickening behavior of pure CO2 hydrates, with flow rate being the only parameter to have a significant effect. Visual and Raman spectroscopy evidence show that both a dense hydrate layer and a porous hydrate layer form, and the latter may provide a path for mass transfer to continue hydrate crystallization. A first principles mass transfer model is developed to describe CO2 hydrate crystal thickening at the interface between gas and water. The impacts of gas impurities and channel wettability are also studied. This method is further applied to investigate the conversion of methane hydrate to CO2 hydrate for combined energy recovery and methane hydrate formation. The authors acknowledge the US Department of Energy Basic Energy Science award # DE-SC0022162.

Wadsworth, Lindsey [Colorado School of Mines, Gold↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2025

Expedition UT-GOM2-2 Methods

Methods used during the University of Texas (UT) Deepwater Hydrate Coring Expedition (UT-GOM2-2) include work done onboard the Helix Q4000 in the offshore Gulf of America (Gulf of Mexico), herein “the Gulf”, “dockside” in Salt Lake City, Utah, and some shore-based work in individual laboratories. The goal of this report is two-fold: to provide enough detail on the methods so they can be repeated by others; and to provide a reference document for the team to enhance cross-disciplinary understanding and knowledge. Methods include drilling operations, depth references and depth modification, downhole tool deployment, coring tool performance assessment, core processing, lithostratigraphy, biostratigraphy, as well as physical properties, including core logging and imaging, rock magnetism, dissolved methane concentration, hydrate saturation, microbiology, and geochemistry. An extensive amount of operational work and planning was required before mobilization of the expedition to permit, build mobile labs, and test downhole tools for deepwater drilling.

03 NATURAL GAS↗
DOE OSTI2022

Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow

This work presents a new engineering analytical model that predicts the effect of both the turbine yaw misalignment and the inflow wind veer on the wake flow distribution downwind of a wind turbine. To consider the veered inflow, two methods were examined. In the first method, the curled shape of the wake due to the yaw offset is initially modelled. The wake shape is then laterally skewed at each height due to the wind veer based on the assumption that the turbine wake is transported downstream by the incoming flow. The second method is a more realistic approach that accounts for the effect of wind veer on the wind velocity direction and the yaw angle seen by the wind turbine. This models the wake region in a local coordinate system defined based on the wind direction at each height. A coordinate transformation is then performed to represent the wake flow distribution in the global coordinate system attached to the ground. The results show that while the two methods provide similar outputs for small variations in the wind direction across the rotor, the difference becomes more evident with an increase in wind veer. High-fidelity simulations for a turbine subject to a neutral atmospheric boundary layer were employed to validate model predictions for different operating conditions.

17 WIND ENERGY↗
DOE OSTI2024

OpenFAST Modeling of the T-Omega Wind Floating Offshore Wind Turbine System

To reduce the cost of offshore wind energy through a more efficient design of the floating support structure, T-Omega Wind developed a novel lightweight and shallow-draft platform concept that aims to achieve a wave-following behavior without resonance amplification. The rotor and generator are carried by four tower legs with each leg supported by a shallow-draft float at the base. In preparation for a more detailed analysis, a coupled aero-hydro-elastic model of the proposed concept is developed in OpenFAST. The detailed modeling approach is presented, including a novel application of the SubDyn substructure dynamics module of OpenFAST to approximate the loads on the axle tube at the tower top from the rotor hub bearings. Preliminary results obtained with the OpenFAST model by rigidifying the substructure and blades indicate that the original sizing of the design can lead to large hub acceleration in the axial/surge and pitch directions due to platform-pitch motion. The high axial acceleration also potentially leads to large bending moments in the four tower legs. To address these issues, an updated design is developed with, among other changes, increased float spacing at the tower base and improved shear-transmitting geometry of the tower legs. The new design suggests significantly reduced extreme hub accelerations under the same conditions even with structural flexibility and will be further analyzed in the future through full aero-hydro-servo-elastic simulations.

ENGINEERING,WIND ENERGY↗
DOE OSTI2023

Intracycle RPM control for vertical axis wind turbines

The wind energy market is currently dominated by horizontal axis wind turbines (HAWTs); however, vertical axis wind turbines (VAWTs) are emerging as a design alternative, especially for deep-water offshore siting due to their low center of gravity, ease of access to drivetrain components, and overall simplicity. Due to the absence of a pitch mechanism in large-scale Darrieus VAWTs, stall control has often been used to manage power and loads. Introducing a pitching mechanism in H-type VAWTs has been studied, but this diminishes the mechanical simplicity advantage, and the use of a pitching mechanism in a large-scale Darrieus-type VAWT is not practical. This work examines an innovative, alternative method to control the rotor dynamics of a large-scale 5 MW VAWT to maximize power while constraining loads without introducing any new or complex mechanical elements. This control strategy is termed intracycle revolution per minute (RPM) control, where the rotational speed of the turbine is allowed to vary in an optimal fashion with the azimuthal location of blades as opposed to typical constant RPM operation. An optimization framework is formulated for an open-loop optimal control problem and solved to maximize power subject to constraints on aerodynamic design loads. Results are presented to demonstrate the benefits and the performance limits of intracycle RPM control for large-scale 5 MW Darrieus VAWTs, namely, (1) power production (quantified in terms of AEP) that can be increased subject to baseline load limits and (2) opportunities to significantly increase AEP or decrease loads via intracycle RPM control that are examined for both two-bladed and three-bladed VAWTs.

17 WIND ENERGY↗
DOE OSTI2025

Electro-Thermo-Mechanical (ETM) Study on Submarine Dynamic Power Cables (SDPC) for Offshore Wind Transmission

Understanding the failure mechanisms of submarine dynamic power cables (SDPC) is critical for innovative design to meet the 2035 cost reduction target of U.S. DOE Floating Offshore Wind Shot. This is important because the current design suffers a significant failure rate in the field. This project conducted a systematic electro-thermo-mechanical (ETM) experimental study on the power cores extracted from a 15kV power cable with three cores of copper conductor, ethylene propylene rubber (EPR) insulation, and continuously corrugated welded aluminum armor (CCWA). An ETM testing system was developed by integrating a high voltage (HV) amplifier, two ceramic heaters, and a rod-plate transverse compression setup into a mechanical testing machine. Increasing temperature from room temperature (RT, 22 degree C) to 90 degree C resulted in the 67% decrease in the failure mechanical load as defined by the dielectric breakdown. Under creep mechanical loading, the dielectric breakdown time was decreased by 70% for a given mechanical load when the specimen temperature increased from RT to 90oC. The failure strain in both monotonic and creep mechanical loading modes was related to the maximum mechanical load applied. Although the dielectric breakdown occurred in the ETM test, the post-test measurement revealed an impressive recovery of electric resistance. The failure analysis on cross section of tested specimens indicated a sizable gap across insulation layer near the area between copper conductor and loading rod, which apparently resulted from online dielectric failure and offline elastic recovery of components including conductor and insulation layer.

17 WIND ENERGY↗
DOE OSTI2026

Harnessing Ocean Thermal Gradients Using Thermoelectric Based Submersibles for Ocean Power Applications

The urgent need for energy solutions in marine environments has accelerated the development of innovative technologies capable of leveraging natural resources for power generation. This study introduces a buoyancy-driven submersible system designed to harness ocean thermal gradients using thermoelectric generators (TEGs) and phase change materials (PCMs). The technology aims to provide autonomous power to offshore aquaculture farms, unmanned underwater vehicles (UUVs), offshore platform illumination, and ocean sensors, significantly reducing dependence on fossil fuels. Ocean thermal gradients, especially prevalent in mid-latitude regions, exhibit temperature differences between surface and deep waters ranging from 7 degrees Celsius to 30 degrees Celsius depending on seasonal variations. The proposed submersible technology utilizes TEGs to convert thermal energy from these gradients into electrical power, generating between 0.2 and 0.5 watts, while PCMs are employed to store and regulate this energy, ensuring a stable and continuous power supply. The buoyancy-driven mechanism of the submersible enhances its capability to navigate through varying depths, optimizing its exposure to different thermal gradients and maximizing energy harvesting. The performance of this submersible system is analyzed through detailed thermodynamic assessments and computational fluid dynamics (CFD) modeling focused on heat transfer. These analyzes consider real-world ocean temperature profiles and seek to refine the interaction between TEGs and PCMs to optimize energy extraction. The evaluation encompasses several key performance metrics, including power output and energy efficiency. Results confirm the potential of this innovative technology to provide a continuous and reliable power source for marine applications. By demonstrating the feasibility of using ocean thermal gradients for energy generation, this study contributes to the broader efforts of innovation in energy technologies for harsh, remote marine environments. The implementation of such promises is significant advancements in the autonomy of marine operations. The ongoing research will further investigate scalability ensuring that the system can be effectively adapted to various marine settings and operational demands.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

2024 Incubator Final Report: Active Hybrid Mooring

This Incubator project has defined and developed a representation of an active hybrid mooring (AHM) system in Simulink to assess whether the speed and accuracy of AHM is sufficient to warrant further investigation in a larger project. We first built a representative mooring system for the VolturnUS-S 15MW floating offshore wind turbine (FOWT) in 1,000-meter water depth to provide baseline loads and displacements needed in the AHM system, with the results showing off-the-shelf actuators would be practical. We then defined model configurations for the numerical and physical substructures, using standalone MoorDyn v2 with a novel input strategy and chained hydraulic actuators represented in Simulink Simscape, respectively. The remainder of the project has focused on verifying the existing models for speed and accuracy against OpenFAST simulations, increasing their complexity to enable six degree of freedom (6DOF) operation, and connecting the numerical and physical substructures into a single holistic model. The verification phase of the project has shown promising results, though further refinement is needed to resolve accuracy discrepancies and for robust performance across a wider range of metocean conditions. The numerical model we developed is computationally stable and operates faster than real time using a time step of 0.01 seconds or shorter, indicating the model is likely fast enough for our AHM design to operate successfully. Additionally, the Simscape model of a hydraulic actuator operates with less than 0.3% relative error when actuating mooring loads from an OpenFAST simulation of the VolturnUS-S 1,000-meter mooring system. The aforementined refinements will be performed in the next year as a Continuing Incubator, with key remaining tasks include programming the predictor-corrector loop to minimize movement error, testing against more complex nonlinear wave cases, and expanding to enable loads from connected shared mooring lines.

17 WIND ENERGY↗
DOE OSTI2024

Synergistic Integration of Multiple Wave Energy Converters with Adaptive Resonance and Offshore Floating Wind Turbines through Bayesian Optimization

We developed a synergistic ocean renewable system where an array of Wave Energy Converters (WEC) with adaptive resonance was collocated with a Floating Offshore Wind Turbine (FOWT) such that the WECs, capturing wave energy through the resonance adapting to varying irregular waves, consequently reduced FOWFT loads and turbine motions. Combining Surface-Riding WECs (SR-WEC) individually designed to feasibly relocate their natural frequency at the peak of the wave excitation spectrum for each sea state, and to obtain the highest capture width ratio at one of the frequent sea states for annual average power in a tens of kilowatts scale with a 15 MW FOWT based on a semi-submersible, Bayesian Optimization is implemented to determine the arrangement of WECs that minimize the annual representation of FOWT’s wave excitation spectra. The time-domain simulation of the system in the optimized arrangement is performed, including two sets of interactions: one set is the wind turbine dynamics, mooring lines, and floating body dynamics for FOWT, and the other set is the nonlinear power-take-off dynamics, linear mooring, and individual WECs’ floating body dynamics. Those two sets of interactions are further coupled through the hydrodynamics of diffraction and radiation. For sea states comprising Annual Energy Production, we investigate the capture width ratio of WECs, wave excitation on FOWT, and nacelle acceleration of the turbine compared to their single unit operations. We find that the optimally arranged SR-WECs reduce the wave excitation spectral area of FOWT by up to 60% and lower the turbine’s peak nacelle acceleration by nearly 44% in highly occurring sea states, while multiple WECs often produce more than the single operation, achieving adaptive resonance with a larger wave excitation spectra for those sea states. The synergistic system improves the total Annual Energy Production (AEP) by 1440 MWh, and we address which costs of Levelized Cost Of Energy (LCOE) can be reduced by the collocation.

Engineering↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗