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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 55 records · Page 3

Ultrasonic deterrents provide no additional benefit over curtailment in reducing bat fatalities at an Ohio wind energy facility

Wind energy is important for achieving net-zero greenhouse gas emissions but also contributes to global bat mortality. Current strategies to minimize bat mortality due to collision with wind-turbine blades fall broadly into two categories: curtailment (limiting turbine operation during high-risk periods) and deterrence (discouraging bat activity near turbines). Recently, there has been interest in combining these strategies to achieve greater reductions in bat fatalities than either strategy might achieve in isolation. To investigate the effectiveness of combining curtailment with ultrasonic deterrent minimization strategies, we deployed six ultrasonic deterrents at nacelle height on 16 experimental turbines at Avangrid Renewables’ Blue Creek Wind Energy Facility. We rotated between four conditions (normal operations, curtailment only, deterrent only, curtailment and deterrent) randomly assigned to four wind turbines each night between 15 June and 3 October 2017. We found that bat mortality at wind turbines was independent of wind speed. The effectiveness of ultrasonic acoustic deterrents varied between high-frequency-calling species (eastern red bats) and low-frequency-calling species (hoary bats, silver-haired bats, and big brown bats). When deterrents were active, mortality was twice as high for eastern red bats compared to the control. Conversely, deterrents had a weak dampening effect on bat mortality for low-frequency species. We found no additive effects on mortality reduction for turbines operating both curtailment and deterrents compared to either approach in isolation. Our findings suggest that ultrasonic acoustic deterrents may not be effective for both high and low frequency echolocating bats. The increase in fatalities of eastern red bats is alarming and underscores the importance of considering site- and species-specific effects of minimization solutions.

60 APPLIED LIFE SCIENCES↗

Motion Tracking Above and Below Water Dataset

This data set includes one trial of above and under water motion tracking measurements from a Qualysis motion tracking system. The Qualisys native .qtm file can be opened by the Qualisys Track Manager software. Data from this file has been exported to a tab separated value (.tsv) file which is a generic ASCII file format that can be read by a text editor, MATLAB, Excel, etc... Also exported is a native MATLAB formatted file (.mat) which can be loaded directly into MATLAB. The trial is from a three body wave energy converter device, using the underwater system for the central nacelle data, and above water system for the fore and aft floats.

16 TIDAL AND WAVE POWER↗

TEAMER: Results of Investigating Structural Design Concepts and Alternative Materials for a Wave Power System

Included here are materials from a study on the design of a three-body Wave Energy Converter (WEC) utilizing a heave plate, dual Power Take Offs (PTOs), and single point mooring. A material trade study has been conducted to evaluate the effects of introducing various metallic and fiber-reinforced polymer (FRP) elements to the design. The baseline design is C-Power's patented k2 Wave Power System (WPS). This Testing Expertise and Access for Marine Energy Research (TEAMER) Request for Technical Support (RFTS) 9 effort resulted in alternative designs of the WPS center member, the nacelle. The analysis resulting from this study will inform the design of a lightweight structure with lower manufacturing and operating costs that increases energy conversion and power-to-weight ratio of the device. The dataset contains several components: finite element analysis (FEA) data, hand calculations, and CAD models. FEA files includes those used for and generated by finite element simulations. Calculations include hand calculations performed using Microsoft Excel, which assess core laminate properties, buckling, and other structural aspects in accordance with risk management DNV-RP-C201 standards. CAD materials include Solidworks files for parts, assemblies, and drawings of the evaluated models.

16 TIDAL AND WAVE POWER↗

Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

This exploratory analysis characterizes the balance-of-system (BOS) cost reduction opportunity for small, commercial, medium, and large distributed wind systems. To do this, we used the National Renewable Energy Laboratory’s (NREL’s) Land-based Balance of System Systems Engineering (LandBOSSE) model (Eberle et al. 2019). This model calculates the capital expenditures (CapEx) associated with installation and the system components (e.g., foundation and electrical infrastructure) other than the rotor nacelle assembly and tower. Then, building on the cost reduction potential assessment, we provide a more qualitative evaluation of prospective technology innovation concepts that may enable realization of those cost reductions for distributed wind systems.

17 WIND ENERGY↗

A Comparative Study of Direct and Indirect Additive Manufacturing Approaches for the Production of a Wind Energy Component

Additive manufacturing (AM) was developed in the 1980s to create three-dimensional prototypes through layer-wise approaches to fabrication. Since then, these approaches have seen improvements in both materials and processing technologies. To date, there are now 7 types of additive manufacturing processes and hundreds of materials, which can be directly printed – going directly from digital design to fabricated components. In this project, Oak Ridge National Laboratory (ORNL), Vestas Wind Systems, and The National Renewable Energy Laboratory (NREL) collaborated to evaluate the effectiveness of state-of-the-art large-scale AM processes in the production of a structural component for use in a wind turbine nacelle, through both direct and indirect manufacturing approaches. Here, experienced AM design engineers detail techniques for AM design, including topology optimization (TO), support minimization, reverse engineering, and techniques for mitigating poor interlaminar performance. Fabrication of the components is presented, including printing parameters and postprocessing, and followed with full-scale component testing by a 3rd party testing laboratory. To evaluate the potential of the developed approaches, a complete techno-economic analysis is provided which evaluates the cost of these techniques given current and near to long-term projections of AM system capabilities.

17 WIND ENERGY↗

Advanced Collision Detection and Site Monitoring for Avian and Bat Species for Offshore Wind Energy (Final Technical Report)

This final technical report summarizes the outcomes from a project that aimed to design, build, and test a persistent and autonomous monitoring system for avian and bat collisions with offshore wind turbines blades and structures. The system comprises four primary sensor modules: 1) on-blade sensor modules for collision detection and dual-vision image capture on each blade with both visible light and near-infrared imagers; 2) additional on-blade collision sensors mounted further from the root; 3) a nacelle-mounted unit including a 360º camera and ultrasonic microphone array; and, 4) an on-blade, high-performance infrared camera module. Primary targeted outcomes were high sensitivity for the detection of blade strikes from bats and small birds, and automatically captured visual confirmation of the striking object; these features are critical for monitoring offshore wind turbine installations, where ground-based methods are not viable. In addition, local recording will provide a long-term sensor recording database. Following laboratory validation, field testing was conducted on an operational wind turbine in collaboration with the National Wind Technology Center at the NREL Flatirons campus over two planned field tests.

17 WIND ENERGY↗

Modeling the Yaw Behavior of Tail Fins for Small Wind Turbines: November 22, 2021 - May 21, 2024

This report describes the aerodynamic modelling of the yaw behavior of tail fins for small wind turbines (SWTs). The analysis is based on unsteady slender body theory (USBT) and is formulated to be used with the proposed tail fin module to be added to OpenFAST. Linearized USBT, which has been applied in the past to SWT tail fins, is developed in three main ways. First, USBT is extended to high yaw angles and aspect ratios by modelling the associated nonlinear vortex dynamics and the chordwise load distribution respectively. Second, we consider the effect of time-varying wind speed. The extended theory is compared to recent unpublished measurements at the University of Perugia, Italy, for the yaw behavior of delta, elliptical, and rectangular tail fins without a rotor and nacelle. The fins were released from initial yaw angles of -40 degrees and -80 degrees ; the latter is of sufficient magnitude to show the importance of the nonlinear yaw dynamics. Further, the friction in the model tail fin bearings was measured and modelled. The constants in the aerodynamic models were taken from the literature but significant improvement in accuracy is achieved by using system identification techniques to optimize the values. This leads to the third main finding: extended USBT is accurate for all generic tail fin shapes that we considered and is, therefore, very suitable for inclusion in aeroelastic codes for SWTs. The final development is of a general nonlinear equation for yaw response that is simpler than the extended USBT and is also suitable for inclusion in OpenFAST. The two nonlinear models are used to highlight the geometric requirements for good tail fin design.

distributed wind↗

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

An Operations and Maintenance Roadmap for U.S. Offshore Wind: Enabling a Cost-Effective and Sustainable U.S. Offshore Wind Energy Industry Through Innovative Operations and Maintenance

The United States is currently targeting 30GW of offshore wind to be installed by 2030, and 150GW by 2050. Even considering future turbine sizes, this represents thousands of new turbines installed in a diverse set of environments, each with their unique design, installation, and maintenance challenges. While much can be learned from European and Asian experience with offshore wind over the past two decades, it is important to understand the unique circumstances of the U.S. This document explores operations and maintenance of offshore wind energy, specific to the U.S. and attempts to lay out a roadmap for needed activities to ensure reliability of future installations. The roadmap was informed through dozens of interviews with a wide cross-section of the industry, including representatives from OEMs, owner/operators, service companies, certification agencies, service providers, and researchers. The roadmap first describes the problem by component - blades, drivetrain and nacelle, structures and foundations, and electrical systems - through a look at current practices and opportunities for improvement in the areas of Failure Mode Analysis and Mitigation; Monitoring, Sensing, and Inspection; and Maintenance Execution. Crosscutting areas of Digitalization, Robotics and Automation, Prognostics and Health Management and O&M Optimization, Experimentation and Demonstration, Standardization, and Design Optimization Considering Reliability and O&M are then discussed. Finally, the roadmap summarizes all of these topics with recommendations for short (1-3 years), medium (4-7 years), and long term (8-12 years) activities, with a description of needed public and private sector contributions.

17 WIND ENERGY↗

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence E - Raw Data

Sequence E: Yaw Releases (P) This test sequence used a downwind, rigid turbine with a 3.4° cone angle. The wind speeds ranged from 7 m/s to 17 m/s. Initial yaw angles of ±90° were achieved. The blade tip pitch was 3°. The rotor rotated at 72 RPM. Blade and probe pressure measurements were collected. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to –99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. The turbine was positioned at each specified yaw angle. A 30- second data set was collected with the yaw brake engaged. These points used a letter for the last digit beginning with A and proceeding through the alphabet as repeat points were needed. These points were plotted and compared to ascertain the functionality of the instrumentation. The files ending in numbers beginning with 0 and increasing with each repetition represent yaw release points. Once the fixed-position yaw test was complete, the yaw drive was engaged to hold the turbine. The yaw brake was released, and the yaw drive was disabled allowing the turbine to yaw freely. The yaw drive was disabled about 5 seconds into the campaign, and the turbine was allowed to yaw freely for the rest of the 30-second duration. When the yaw drive is disabled, no torque is applied to the motor, and the inertia and yawing force of the nacelle and rotor overcomes the friction and inertia of the yaw gear and motor. The yaw releases were repeated five times at each condition. Four additional campaigns were collected to determine the turbine’s natural yaw error by releasing the brake at 0° yaw error. These file names use the E designation, followed by two digits for wind speed, followed by XXXX, followed by the repetition digit.

17 WIND ENERGY↗

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence 4 - Raw Data

Sequence 4: Static Pressure Calibration (P) This test sequence used a downwind, teetered turbine with a 3.4° cone angle. The wind speed ranged from 5 m/s to 25 m/s. Yaw angles of 0° to 30° were achieved. The blade tip pitch was 3°. The rotor rotated at 72 RPM. Blade and probe pressure measurements were collected. A static pitot probe was installed on a boom 2.3 m upwind of the nacelle at hub height and connected to the reference side of the Mensor digital differential pressure transducer through the pneumatic rotary coupling. This provided the reference pressure for all blade-mounted pressure transducers, as opposed to the NASA atmospheric pressure reference that was used for all other test sequences. This simulates the pressure system connections used in field measurements where the static pressure was unknown.

17 WIND ENERGY↗

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence 7 - Raw Data

Sequence 7: Shroud Operating (P) This test sequence used a downwind, rigid turbine with a 3.4° cone angle. The wind speed ranged from 5 m/s to 25 m/s. Turntable angles from 0° to 30° were achieved, but the yaw error angle was maintained at 0° by adjusting the nacelle yaw. The blade tip pitch was 3°. The rotor rotated at 72 RPM. Blade and probe pressure measurements were collected. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to –99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. An airfoil-shaped aluminum shroud was placed on the tower below the 34% span five-hole probe. It extended below the 91% span five-hole probe. The turntable angle was adjusted to position the shroud at various angles relative to the flow, but the turbine yaw error angle was maintained at 0°. Two additional points were collected where the turntable angle was 0°, and the turbine yaw angle was 20° (71000200) or -20° (710M0200).

17 WIND ENERGY↗