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DOE OSTI2023

Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics—Near-Real-Time Cyber-Physical Resiliency Through Machine Learning

The Situational Awareness of Grid Anomalies (SAGA) project built upon foundational power system tools developed at the National Renewable Energy Laboratory (NREL) integrated with an ever-increasing set of Gridmetrics data extracted from the cable television (CATV) broadband network infrastructure while assimilating other time-series geospatial data and information, such as weather and cyber-physical phenomena, to demonstrate a disruptive technology for power system data analytics relying on existing infrastructure. Three research thrusts supported (1) visual analytics, (2) cyber-physical power system simulation, and (3) anomaly detection. SAGA created technology that leverages, couples, and fortifies two vastly different realms - power and broadband - to increase the resiliency of the power grid in the face of increasing cyberattacks and operational challenges related to integrating DERs. The exploration of potential synergies of broadband-enabled grids resulted in identifying a mutually beneficial symbiosis that can increase the resiliency of both power and broadband services. Broadband networks perform better with reliable power and are good at providing real-time measurements that identify where the grid is under attack, is failing, or is weak. Likewise, sensor-starved distribution grids perform better and can be more reliable when their operation is buttressed with observations of broadband-detected anomalies. Future research can explore broadband's contribution to continuing to improve grid resiliency, reliability, and cost-effective operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Assessing wind power operating costs in the United States: Results from a survey of wind industry experts

This paper draws on a survey of wind industry professionals to clarify trends in the operational expenditures (OpEx) of U.S. land-based wind power plants. We find that average all-in lifetime OpEx has declined from approximately $80/kW-yr (~$35/MWh) for projects built in the late 1990s to a level approaching $40/kW-yr (~$11/MWh) for projects built in 2018. Turbine operations and maintenance costs represent the single largest component of overall OpEx and the primary source of cost reductions. We observe wide ranges of OpEx over time; respondents cite a range in average expected costs for recently commissioned projects from $33/kW-yr to $59/kW-yr. We also use historical OpEx learning rates, showing a 9% OpEx reduction for each doubling of global wind capacity, to project a further $5-$8/kW-yr reduction from 2018 to 2040. These findings suggest that continued OpEx reductions may contribute 10% or more of the expected reductions in land-based wind's levelized cost of energy. Moreover, these estimates understate the importance of OpEx owing to the multiplicative effects through which operational advancements influence not only operations and maintenance costs but also component reliability, performance, and plant-level availability - thereby also affecting levelized costs by enhancing annual energy production and plant lifetimes. Given the limited quantity and comparability of previously available OpEx data, the trends reported here may inform OpEx assumptions used by electric planners, analysts, and modelers. The results may also provide useful benchmarks to the wind industry, helping developers and asset owners compare their expectations with historical experience and other industry projections.

17 WIND ENERGY↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Turnaround Time Modeling for Conceptual Rocket Engines

Recent years have brought about a paradigm shift within NASA and the Space Launch Community regarding the performance of conceptual design. Reliability, maintainability, supportability, and operability are no longer effects of design; they have moved to the forefront and are affecting design. A primary focus of this shift has been a planned decrease in vehicle turnaround time. Potentials for instituting this decrease include attacking the issues of removing, refurbishing, and replacing the engines after each flight. less, it is important to understand the operational affects of an engine on turnaround time, ground support personnel and equipment. One tool for visualizing this relationship involves the creation of a Discrete Event Simulation (DES). A DES model can be used to run a series of trade studies to determine if the engine is meeting its requirements, and, if not, what can be altered to bring it into compliance. Using DES, it is possible to look at the ways in which labor requirements, parallel maintenance versus serial maintenance, and maintenance scheduling affect the overall turnaround time. A detailed DES model of the Space Shuttle Main Engines (SSME) has been developed. Trades may be performed using the SSME Processing Model to see where maintenance bottlenecks occur, what the benefits (if any) are of increasing the numbers of personnel, or the number and location of facilities, in addition to trades previously mentioned, all with the goal of optimizing the operational turnaround time and minimizing operational cost. The SSME Processing Model was developed in such a way that it can easily be used as a foundation for developing DES models of other operational or developmental reusable engines. Performing a DES on a developmental engine during the conceptual phase makes it easier to affect the design and make changes to bring about a decrease in turnaround time and costs.

Nix, Michael↗

DOE OSTI2013

Framework For Human-Automation Collaboration: Project Status Report

The Human-Automation Collaboration (HAC) research project within the Department of Energy’s Advanced Small Modular Reactor (AdvSMR) program is investigating how advanced technologies that are planned for AdvSMRs might affect the performance and the reliability of the plant from a human factors and human performance perspective. The HAC research effort investigates the consequences of allocating functions between the operators and automated systems. More specifically, the research team is addressing how to best design the collaboration between the operators and the automated systems in a manner that has the greatest positive impact on overall plant performance and reliability. As one step to accomplish this goal, the research team reviewed available information on AdvSMR designs and identified plant functions and operator tasks that might impact the design of the HAC. The identified functions and tasks will later be vetted against insights learned from the current fleet of Light Water Reactors (LWR), other industries where humans are collaborating with highly automated systems, and the results from the activities in the AdvSMR Concepts of Operations project to inform a high-level generic design model. This generic model will be used as a basis for experimental studies to study the effects of different HAC concepts as well as evaluating new human-system interface (HSI) solutions to support a well-functioning HAC. Another important part needed to accomplish the research goal is a model of the collaboration between the human operator and the automated system. The team has developed a model of HAC that defines: (1) the important design dimensions of automation that impact automation’s use by personnel and integrated human-automation performance, (2) what aspects of human cognition, behavior, and performance mediate automation’s use by personnel, and (3) when and how the above factors affect the use of automation and the overall performance of the HAC system. The HAC model is utilized to identify areas in need of additional research, which forms the basis for planned HAC research activities. By detailing the relationships between characteristics of HAC, automation, and human cognition, the HAC model identifies what aspects of the human-automation interaction are important to consider in developing automation for AdvSMR systems. Three analytical studies have been initiated to address questions related to the HAC model. These studies are 1) Models of Teamwork, 2) Standardized HAC Performance Measurement Battery, and 3) Initiators and Triggering Conditions for Adaptive Automation. Additionally, a number of field studies have been identified and are in progress, and a set of experimental studies that are on schedule to be conducted later in the project to address the main research question. This report consists of three main parts: the preliminary identification of AdvSMR functions and tasks, a description of the updates and extensions to the HAC model, and a description of ongoing and planned analytical, field, and experimental research efforts that the research team has developed to accomplish the project’s overall goal of establishing a technical basis for designing the collaboration of personnel and automation.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2019

Hazards Analysis and Failure Modes and Effects Criticality Analysis (FMECA) of Four Concept Vehicle Propulsion Systems

The primary objective of this research effort is to identify failure modes and hazards associated with the concept vehicles and to perform functional hazard analyses (FHA) and failure modes and effects criticality analyses (FMECA) for each. Boeing also created a Fault Tree Analysis (FTA) for each of the concept vehicles, as the FTA contains the connectivity between systems and is an accepted, top-down method to analyze the safety of an air-vehicle. Conceptual design of notional powertrain configuration for each of four (4) NASA RVLT (Revolutionary Vertical Lift Technology) Concept Vehicles were developed in as much detail as was necessary to support the reliability and safety analysis for this project. Functional block diagrams from each of the conceptual powertrain configurations were created and used to order the FHA, FMECA, and FTA. Hazards were identified and the severity of each were categorized in the FHA for use in a follow-up FMECA. The FTA took inputs from the FMECA and the functional block diagrams to develop the connectivity and develop a quantitative architecture that could be used to perform sensitivity studies, as related to vehicle safety.Guidelines for reliability targets for both the air vehicle and the operation in the UAM (Urban Air Mobility) mission are discussed. An industry literature search was performed in order to assess gaps in existing government regulations and industry specifications. The industry literature search led to air-vehicle and operational reliability discussions, as related to Distributed Electric/Hybrid-Electric Propulsion (DE/HEP) system operating in the UAM role. A discussion of results and recommendations for future work is also provided.

Hazards Analysis↗

DOE OSTI2016

Clemson University Wind Turbine Drivetrain Test Facility

In November of 2009, Clemson University was awarded a competitive grant from the U.S. Department of Energy to design, build and operate a facility for full-scale, highly accelerated mechanical testing of next-generation wind turbine drivetrain technologies. The primary goal of the project was to design, construct, commission, and operate a state-of-the-art sustainable facility that permits full-scale highly accelerated testing of advanced drivetrain systems for large wind turbines. The secondary goal was to meet the objectives of the American Recovery and Reinvestment Act of 2009, especially in job creation, and provide a positive impact on economically distressed areas in the United States, and preservation and economic recovery in an expeditious manner. The project was executed according to a managed cooperative agreement with the Department of Energy and was an extraordinary success. The resultant new facility is located in North Charleston, SC, providing easy transportation access by rail, road or ship and operates on an open access model such that it is available to the U.S. Wind Industry for research, analysis, and evaluation activities. The 72 m by 97 m facility features two mechanical dynamometer test bays for evaluating the torque and blade dynamic forces experienced by the rotors of wind turbine drivetrains. The dynamometers are rated at 7.5 MW and 15 MW of low speed shaft power and are configured as independent test areas capable of simultaneous operation. All six degrees of freedom, three linear and three rotational, for blade and rotor dynamics are replicated through the combination of a drive motor, speed reduction gearbox and a controllable hydraulic load application unit (LAU). This new LAU setup readily supports accelerated lifetime mechanical testing and load analysis for the entire drivetrain system of the nacelle and easily simulates a wide variety of realistic operating scenarios in a controlled laboratory environment. The development of these two dynamometer test rigs is the first significant achievement for the project. These test rigs embody a new manner of test due to the system configuration and completely new design with a free floating loading hub in the LAU. This project provided the catalyst for the advancement to this new test rig configuration that has been adopted by every significant wind turbine test rig constructed since the inception of this project. There are currently two different vendors supplying these new systems. Catalyzing this new design is the second major success of the project. With the increased market penetration of wind energy over the past decade, many regions and countries have developed specific electrical grid specifications and performance codes for large wind farms to ensure operational reliability and stability. These grid codes provide requirements for interconnection with the grid during low or high voltage phenomena, typically encountered during and after system fault events. Given the installed infrastructure of the Wind Turbine Drivetrain Testing Facility (WTDTF), a natural expansion of facility capability was to include the necessary equipment for performing fault ride-through evaluations of wind turbines to the Low Voltage Ride Through (LVRT) codes. Once the decision was made to expand the scope of the original grant into fault ride-through testing, it was clear that there are several markets, not just wind, which could benefit from this type of test and that simple fault ride-through testing could be extended into a broader scope of electrical testing capabilities. It was at this point that Clemson University was awarded a second grant to build a 15 MW Hardware-In-the-Loop (HIL) Grid Simulator in order to establish world class electrical testing capabilities to compliment the mechanical testing at the WTDTF. This third significant achievement resulted in the 15 MW HIL Grid Simulator as the corner stone of the Duke Energy eGRID Center and is collocated with the WTDTF in the SCE&G Energy Innovation Center at the new Facility in North Charleston, SC, USA. Before the eGRID was completed, it was recognized that the ability to test solar farm equipment was but a small step away thru the addition of enhanced equipment to provide for DC testing. In yet another expansion/success, a 2.5 MW rectifier system was designed and implemented by Clemson staff to enhance the Center’s capabilities. The program required over 250,000 man-hours of on-site construction labor reworking the brownfield facility on the former Navy Base, clearly satisfying one of the major goals of the Reinvestment Act. This was done while winning numerous awards for design and construction of the facility, including the Top US Project for 2014 from the Trade Journal Engineering News Record. The project was a major collaborative developmental activity managed by Clemson University staff that involved the DOE and many partners and organizations.

17 WIND ENERGY↗

DOE OSTI2014

Chisel: reliability- and accuracy-aware optimization of approximate computational kernels

The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy. We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification. We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Light Water Reactor Sustainability Program: Advanced Instrumentation, Information, and Control Systems Technologies Technical Program Plan for 2013

Reliable instrumentation, information, and control (II&C) systems technologies are essential to ensuring safe and efficient operation of the U.S. light water reactor (LWR) fleet. These technologies affect every aspect of nuclear power plant (NPP) and balance-of-plant operations. In 1997, the National Research Council conducted a study concerning the challenges involved in modernizing existing analog-based instrumentation and controls with digital instrumentation and control systems in NPPs. Their findings identified the need for new II&C technology integration. The objective of these efforts is to develop, demonstrate, and support deployment of new digital II&C technologies for nuclear process control, enhance worker performance, and provide enhanced monitoring capabilities to ensure the continued safe, reliable, and economic operation of the nation’s NPPs. Digital II&C technologies are deployed in a number of power generation settings worldwide. Current instrumentation and human-machine interfaces in the nuclear power sector employ analog technologies. In other power generation sectors, analog technologies have largely been replaced with digital technologies. This is in part due to the manufacturing and product support base transitioning to these newer technologies. It also accompanies the transition of education curricula for I&C engineers to digital technologies. Consequently, product manufacturers refer to analog I&C as having reached the end of its useful service life. Although considered obsolete by other industries, analog I&C continues to function reliably, though spare and replacement parts are becoming increasingly scarce as is the workforce that is familiar with and able to maintain it. Analog I&C was also the predominant means used for process control in an era when human labor was more affordable, and maintaining an I&C technology base through a larger workforce conducting frequent rounds for surveillances, inspections, and tests was accepted in the nuclear power generation business model. Today’s power generation business climate is much different and II&C technologies are needed in the long run that are more highly automated and require less cost to operate and maintain, are as highly reliable as those used today, and will be familiar to a future work force. They should also enable performance gains for the utility so that they are not merely a sunken cost, as this would weigh heavily on the balance sheet at a time of particularly high cost competitiveness in electricity markets.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Review of transportation hydrogen infrastructure performance and reliability

Hydrogen infrastructure for fueling vehicles has progressed in the last decade from stations with restricted access and limited operating hours to customer-friendly retail stations open to the public. There are now 121 retail hydrogen stations around the world. In California, the number of public retail hydrogen stations has increased from zero to more than 30 in less than two years, and the annual amount of hydrogen dispensed by retail stations has grown from 27,400 kg in 2015 to nearly 105,000 kg in 2016 and more than 440,000 kg in 2017 - an increase of about four times year over year. For more than a decade, government, industry, and academia have studied many aspects of hydrogen infrastructure, from renewable hydrogen production to retail hydrogen station performance. This paper reviews the engineering and deployment of modern hydrogen infrastructure, including the costs, benefits, and operational considerations (including safety, reliability, availability), as well as challenges to the scale-up of hydrogen infrastructure. The results identify hydrogen station reliability as a key factor in the expense of operating hydrogen systems, placing it in the context of the larger reliability engineering field.

08 HYDROGEN↗

DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Software-Defined Virtual Synchronous Condenser

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Furthermore, extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

17 WIND ENERGY↗

NASA NTRS1974

Multi-kw dc power distribution system study program

The first phase of the Multi-kw dc Power Distribution Technology Program is reported and involves the test and evaluation of a technology breadboard in a specifically designed test facility according to design concepts developed in a previous study on space vehicle electrical power processing, distribution, and control. The static and dynamic performance, fault isolation, reliability, electromagnetic interference characterisitics, and operability factors of high distribution systems were studied in order to gain a technology base for the use of high voltage dc systems in future aerospace vehicles. Detailed technical descriptions are presented and include data for the following: (1) dynamic interactions due to operation of solid state and electromechanical switchgear; (2) multiplexed and computer controlled supervision and checkout methods; (3) pulse width modulator design; and (4) cable design factors.

Berkery, E. A.↗

NASA NTRS1980

Study of thermal management for space platform applications

Techniques for the management of the thermal energy of large space platforms using many hundreds of kilowatts over a 10 year life span were evaluated. Concepts for heat rejection, heat transport within the vehicle, and interfacing were analyzed and compared. The heat rejection systems were parametrically weight optimized over conditions for heat pipe and pumped fluid approaches. Two approaches to achieve reliability were compared for: performance, weight, volume, projected area, reliability, cost, and operational characteristics. Technology needs are assessed and technology advancement recommendations are made.

Oren, J. A.↗

NASA NTRS1991

Formulation of a strategy for monitoring control integrity in critical digital control systems

Advanced aircraft will require flight critical computer systems for stability augmentation as well as guidance and control that must perform reliably in adverse, as well as nominal, operating environments. Digital system upset is a functional error mode that can occur in electromagnetically harsh environments, involves no component damage, can occur simultaneously in all channels of a redundant control computer, and is software dependent. A strategy is presented for dynamic upset detection to be used in the evaluation of critical digital controllers during the design and/or validation phases of development. Critical controllers must be able to be used in adverse environments that result from disturbances caused by an electromagnetic source such as lightning, high intensity radiated field (HIRF), and nuclear electromagnetic pulses (NEMP). The upset detection strategy presented provides dynamic monitoring of a given control computer for degraded functional integrity that can result from redundancy management errors and control command calculation error that could occur in an electromagnetically harsh operating environment. The use is discussed of Kalman filtering, data fusion, and decision theory in monitoring a given digital controller for control calculation errors, redundancy management errors, and control effectiveness.

Belcastro, Celeste M.↗

NASA NTRS1995

Controls concepts for next generation reuseable rocket engines

Three primary issues will drive the design and control used in next generation reuseable rocket engines. In addition to steady-state and dynamic performance, the requirements for increased durability, reliability and operability (with faults) will dictate which new controls and design technologies and features will be brought to bear. An array of concepts which have been brought forward will be tested against the measures of cost and benefit as reflected in the above 'ilities'. This paper examines some of the new concepts and looks for metrics to judge their value.

Lorenzo, Carl F.↗

NASA NTRS2012

Integration Science and Technology of Advanced Ceramics for Energy and Environmental Applications

The discovery of new and innovative materials has been known to culminate in major turning points in human history. The transformative impact and functional manifestation of new materials have been demonstrated in every historical era by their integration into new products, systems, assemblies, and devices. In modern times, the integration of new materials into usable products has a special relevance for the technological development and economic competitiveness of industrial societies. Advanced ceramic technologies dramatically impact the energy and environmental landscape due to potential wide scale applications in all aspects of energy production, storage, distribution, conservation, and efficiency. Examples include gas turbine propulsion systems, fuel cells, thermoelectrics, photovoltaics, distribution and transmission systems based on superconductors, nuclear power generation, and waste disposal. Robust ceramic integration technologies enable hierarchical design and manufacturing of intricate ceramic components starting with geometrically simpler units that are subsequently joined to themselves and/or to metals to create components with progressively higher levels of complexity and functionality. However, for the development of robust and reliable integrated systems with optimum performance under different operating conditions, the detailed understanding of various thermochemical and thermomechanical factors is critical. Different approaches are required for the integration of ceramic-metal and ceramic-ceramic systems across length scales (macro to nano). In this presentation, a few examples of integration of ceramic to metals and ceramic to ceramic systems will be presented. Various challenges and opportunities in design, fabrication, and testing of integrated similar (ceramic-ceramic) and dissimilar (ceramic-metal) material systems will be discussed. Potential opportunities and need for the development of innovative design philosophies, approaches, and integrated system testing under simulated application conditions will also be presented.

Singh, M.↗

DOE OSTI2013

National Wind Technology Center Dynamic 5-Megawatt Dynamometer

The National Wind Technology Center (NWTC) offers wind industry engineers a unique opportunity to conduct a wide range of tests. Its custom-designed dynamometers can test wind turbine systems from 1 kilowatt (kW) to 5 megawatts (MW). The NWTC's new dynamometer facility simulates operating field conditions to assess the reliability and performance of wind turbine prototypes and commercial machines, thereby reducing deployment time, failures, and maintenance or replacement costs. Funded by the U.S. Department of Energy with American Recovery and Reinvestment Act (ARRA) funds, the 5-MW dynamometer will provide the ability to test wind turbine drivetrains and connect those drivetrains directly to the electricity grid or through a controllable grid interface (CGI). The CGI tests the low-voltage ride-through capability of a drivetrain as well as its response to faults and other abnormal grid conditions.

17 WIND ENERGY↗