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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Muon neutrino charged current single pion production on various targets in the MINERvA detector

Measurements of neutrino oscillation parameters at accelerator-based experiments require precise knowledge of neutrino interaction cross sections. A significant fraction of neutrino interactions observed in these experiments produce single charged pions. While single charged pion production has been measured on hydrocarbon and water, few precise measurements exist for heavier targets, and there are no high statistics measurements that directly compare pion production on different targets. This thesis measures differential cross sections of muon neutrino charged current single charged pion production with no other mesons in the final state in a neutrinobeam with a mean energy of ≈ 6 GeV. The pion production is studied on targets of scintillator, carbon, water, iron, and lead at MINERvA, a neutrino cross section experiment located in the NuMI beamline at Fermilab. These measured cross sections are differential in μ - and π + kinematics. A strong suppression of low Q 2 and an enhancement of low π are observed relative to commonly used neutrino interaction models in both the light and heavy nuclear targets. Incorporation of these observed effects in future neutrino oscillation experiments will improve their ability to measure energy of neutrino interactions, and therefore measure the parameters governing neutrino oscillations.

Bercellie, Aaron↗
DOE OSTI2020

Technology Assessment for Radioiodine at the Hanford Site - 20240

An evaluation was conducted to assess the practicability of technologies of sufficient promise and maturity to warrant treatability tests for remediating radioiodine (I-129) in groundwater at the 200-UP-1 operable unit at the U.S. Department of Energy Hanford Site. Technologies were evaluated based on recent updated knowledge of subsurface iodine transport behavior at Hanford, and the effectiveness, implementability, and cost of potential treatment technologies for I-129. Because more than two-thirds of the iodine at Hanford is estimated to be present in its oxidized form as iodate, treatment technologies were evaluated with respect to addressing iodate. In situ treatments were identified as having limited implementability because the plume core is located directly beneath the Environmental Restoration Disposal Facility disposal cells, a landfill used for the disposal of low-level radioactive, hazardous, and mixed wastes generated from Hanford cleanup activities. Further limitations in treatment technologies for radioiodine were due to the isotopic distribution of subsurface iodine. Data indicate that stable iodine (I- 127) concentrations are up to three orders of magnitude greater than I-129, limiting the effectiveness of potential technologies because they are not isotope-specific. Ex situ approaches also lacked the effectiveness and maturity required to treat I-129. Hence, the technology evaluation did not identify any technologies that needed treatability testing, driven by site and contaminant properties that hinder effectiveness and/or implementability of the technologies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Retiring environmental risks of marine renewable energy devices: The “habitat change” case

The installation, presence, operation, and decommissioning of marine renewable energy (MRE) devices inevitably alters the surrounding ocean habitats. These changes may include direct effects on the benthos from the installation or removal of foundations and anchors, changes in community composition on and near devices, artificial reef effects, and indirect effects such as alteration of the food web or facilitation of non-native, invasive species dispersal. Although there is no expectation that MRE devices affect marine environments differently than other anthropogenic ocean uses, regulators and stakeholders continue to have questions about potential negative impacts to species and habitats from development. Research studies and survey reports that inform our understanding of habitat changes related to MRE devices and associated equipment were compiled into an evidence base, sorted into categories of effects, and evaluated by a group of international experts to assess potential risk to habitats and biota from small numbers of MRE devices, as well as to identify knowledge gaps. These gaps were organized by category and divided up by relevance to consenting, research, or project development and monitoring responsibilities. Identifying these “known unknowns” allows for study design and collaboration from various perspectives to fill the knowledge gaps. Distribution of the evidence base and remaining uncertainties and knowledge gaps to the MRE community, coupled with new research, will help advance the MRE industry while resolving concerns about the potential risks of habitat change for small numbers of devices.

Hemery, Lenaig G.↗
DOE OSTI2023

Prediction on X-ray output of free electron laser based on artificial neural networks

Abstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, which leads to measurement uncertainties for experiments relying on multiple XFEL pulses. Accurate in-situ measurements of x-ray wavefront and energy spectrum incident upon a sample poses challenges. Here we address this by developing a virtual diagnostics framework using an artificial neural network (ANN) to predict x-ray photon beam properties from electron beam properties. We recorded XFEL electron parameters while adjusting the accelerator’s configurations and measured the resulting x-ray wavefront and energy spectrum shot-to-shot. Training the ANN with this data enables effective prediction of single-shot or average x-ray beam output based on XFEL undulator and electron parameters. This demonstrates the potential of utilizing ANNs for virtual diagnostics linking XFEL electron and photon beam properties.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Time-Based CAN IDS Paper Results Code

Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs). This inherited complexity has expanded the CAN attack surface which is vulnerable to message injection attacks. These injections change the overall timing characteristics of messages on the bus, and thus, to detect these malicious messages, time-based intrusion detection systems (IDSs) have been proposed. However, time-based IDSs are usually trained and tested on low-fidelity datasets with unrealistic, labeled attacks. This makes difficult the task of evaluating, comparing, and validating IDSs. Here we detail and benchmark four time-based IDSs against the newly published ROAD dataset, the first open CAN IDS dataset with real (non-simulated) stealthy attacks with physically verified effects. We found that methods that perform hypothesis testing by explicitly estimating message timing distributions have lower performance than methods that seek anomalies in a distribution related statistic. In particular, these “distribution-agnostic” based methods outperform “distribution-based” methods by at least 55% in area under the precision-recall curve (AUC-PR). Our results expand the body of knowledge of CAN time-based IDSs by providing details of these methods and reporting their results when tested on datasets with real advanced attacks. Finally, we develop an after-market plug-in detector using lightweight hardware, which can be used to deploy the best performing IDS method on nearly any vehicle.

Moriano, Pablo [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2021

Time-Based CAN Intrusion Detection Benchmark

Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs). This inherited complexity has expanded the CAN attack surface by the injection of malicious messages that vary their time-based characteristics. To detect these malicious messages, time-based intrusion detection systems (IDS) have been proposed. However, time-based IDS are usually trained and tested on low-fidelity datasets with unrealistic labeled attacks. This makes difficult the task of evaluating, comparing, and validating IDS. Here we detail and benchmark four time-based IDS in a dataset with real and advanced attacks. We found that methods with strong assumptions regarding the distribution of inter-arrival times have lower performance than distribution agnostic based methods. In particular, distribution agnostic based methods outperform distribution based methods at least on $55\%$ in area under the precision-recall (AUC-PR) curve. Our results expand the body of knowledge of CAN time-based IDS by providing details of these methods and reporting their results when tested on datasets with real and advanced attacks. We describe limitations, open challenges, and how lessons learnt from this research can inform the design of deployable time-based IDS in modern vehicles.

Blevins, Deborah↗
DOE OSTI2023

Space-Time Quantum Information from the Entangled States of Magnetic Molecule (STI Product)

This collaborative project combines synthesis, measurement, and theory by three faculty members at the Eddleman Quantum Institute of UC Irvine to effectively investigate the quantum properties of molecules in the space, time, and frequency domains. Through synthetic chemistry, molecules are tailored for their magnetic and coherent properties. By combining femtosecond (fs) terahertz (THz) light and a continuous wave (cw) THz laser with a low temperature scanning tunneling microscope (STM), quantum phenomena are probed with simultaneous femtosecond temporal and atomic-scale spatial resolution. In particular, the invention of the quantum superposition microscope (QSM) advances quantum sensing for enhanced spectroscopy and imaging capabilities. Coupling theory to the experimental efforts offers a deeper understanding and predictive power for the molecular systems. The phenomena of superposition, entanglement, and coherence is central to quantum information science and can be realized in qubit states. Many systems can be modeled by a double-well potential in which two levels are formed in the two lowest energy states interacting with the environment and external radiation. In focusing on molecules as two-level systems, the underlying expectation is that their tunable composition and structure allows an effective parameter space to optimize their use as qubits for quantum sensing and computing. The THz radiation induces the superposition between the two states, appearing as temporal oscillations that damp in amplitude. Enhanced spectroscopy and imaging in the time and frequency domains is achieved through the extreme sensitivity of the frequency and damping of coherence of two-level systems to its environment. A single hydrogen molecule trapped in the STM tunneling gap experiences a double-well potential and absorption of THz femtosecond pulses of light creates the superposition of its two levels, appearing as damped oscillations in the light induced direct current (DC). The oscillation frequency depends sensitively on the electric field distribution of the copper nitride (Cu 2 N) surface, through the Stark effect, and associated with the different charge distributions at the copper and nitrogen sites and in between. This QSM can resolve variation in the surface electric field with 0.02 nanometer resolution. In addition, the single hydrogen molecule entaes with nearby hydrogen molecules as seen in the avoided level crossings of energy (oscillation frequency) versus the voltage across the tunneling gap. Thus, the first application of the QSM senses and images the surface electric field at the atomic scale. Results from this project advance fundamental understanding of quantum phenomena, develop novel synthesis, measurement, and theory, provide the knowledge foundation for molecule-based qubits and sensing that enable the development of the QSM and emergent technologies. This project trained researchers in quantum information science, extended knowledge in classrooms, and outreached to the community.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Technical Evaluation of the Department of Energy Research and Development Activities in Underground Research Laboratories - 20287

Congress created the U.S. Nuclear Waste Technical Review Board (NWTRB) in the 1987 Nuclear Waste Policy Amendments Act (Public Law 100-203) to evaluate the technical and scientific validity of activities undertaken by the Secretary of Energy to implement the Nuclear Waste Policy Act. Since 2012, DOE has collaborated in research conducted in several underground research laboratories (URLs) located in Europe and Asia. According to DOE, these international collaborations have been beneficial to its spent nuclear fuel (SNF) and high-level waste radioactive (HLW) disposal research program, particularly after the termination of the Yucca Mountain repository program when DOE began generic research on alternative host rocks (crystalline, clay, and salt) and repository environments very different from those at Yucca Mountain. In accordance with its mandate, the NWTRB is reviewing the DOE research and development (R and D) activities related to URLs. The NWTRB's review is documented in a report to Congress and the Secretary of Energy that will be released in January 2020, and this paper summarizes the NWTRB review and findings. The NWTRB held a fact-finding meeting with DOE and subsequently held a workshop on international URL collaborations in April of 2019. Based on the presentations and discussions at the workshop and at the fact-finding meeting, as well as information from reports published by DOE and others, there are four principal findings related to DoE's URL-related R and D activities. First, DOE participation in URL-related international research greatly benefits the U.S. geologic disposal R and D program by furthering its understanding of generic and site-specific disposal issues relevant to alternative repository host rocks and environments. DOE-funded R and D activities also are benefiting the URL-related research of other countries, especially in the area of complex analytical and numerical model/software development. Second, the more developed repository programs in other countries have focused on creating and strengthening their safety cases and making them transparent to the public. Repository programs in other countries use URLs to explain the technical bases underlying their safety cases, periodically reassess knowledge gaps and define new activities to strengthen the technical bases, and demonstrate the technology that will allow implementation of the proposed safety concept. Third, countries with more developed geologic disposal programs have found domestic URLs essential to their repository programs. DOE needs domestic URLs to advance geologic disposal efforts over the next decades and further its ability to train the next generation of scientists, engineers, and skilled technical workers. Fourth, DoE's international URL collaborations have advanced its generic disposal R and D program, including development of modeling capabilities recognized internationally as state-of-the-art, but further work on its coupled thermal-hydrological-mechanical-chemical models and URL- and laboratory-based research can strengthen its program. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Toward first principles-based simulations of dense hydrogen

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A Strategic Condition Framework for Energy Innovation

Building upon previous work in innovation, technology, and economic fields, we propose a conceptual framework to address the interplay dynamics of public policy and the value chains of clean energy technology innovation systems, by focusing on market structural and strategic conditions in relation to the rate and direction of knowledge diffusion within activity-based value chains. Our framework extends beyond socio-technical transition models by considering such nuances as: 1) the implications of geography, 2) the locus of knowledge types, 3) the dynamics of financial and knowledge flow, and 4) emphasizing the importance of both market structure and institutional conditions. At the firm level, we consider dynamics of interactions between suppliers and customers, innovation activities, the competitive environment, and the firm’s broader strategic relationship to governance activities and structures; we allow for the fact that each of these aspects of a firm’s identity may have a strategically relevant geographic dimension (e.g., service territories, locational concentration of input resources, etc.). We provide a proof-of-concept application of the U.S. utility-scale solar photovoltaic (PV) and onshore wind sectors, including their interactions with the U.S. power market, which we in present in a web-based information platform (Energy I-SPARK, ei-spark.lbl.gov).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

High power picosecond green and deep ultraviolet generations with an all-fiberized MOPA

We demonstrate high power picosecond green and deep ultraviolet (DUV) lasers based on an all-fiberized master oscillator power amplifier (MOPA). The main power amplifier is fabricated with a highly Yb-doped large mode area (LMA) silicate glass fiber. It delivers 75.2-W laser output at 1029 nm with a pulse repetition rate of 10 MHz and a pulse duration of 70 ps. With a lithium triborate (LBO) crystal, a 43.0-W green output at 514.5 nm has been achieved with a pulse duration of 55 ps. With a caesium lithium borate (CLBO) crystal, a 14.5-W picosecond DUV output at 257 nm has been generated, which is the highest power for the all-fiber based DUV laser, to the best of our knowledge.

Pan, Lei (ORCID:0000000288896117)↗
DOE OSTI2022

Characterization of the structure and chemistry of the solid–electrolyte interface by cryo-EM leads to high-performance solid-state Li-metal batteries

Solid-state lithium-metal (Li 0 ) batteries are gaining traction for electric vehicle applications because they replace flammable liquid electrolytes with a safer, solid-form electrolyte that also offers higher energy density and better resistance against Li dendrite formation. Solid polymer electrolytes (SPEs) are highly promising candidates because of their tunable mechanical properties and easy manufacturability; however, their electrochemical instability against lithium metal (Li 0 ), mediocre conductivity, and poorly understood Li 0 /SPE interphases have prevented extensive application in real batteries. In particular, the origin of the low Coulombic efficiency (CE) associated with SPEs remains elusive, as the debate continues as to whether it originates from unfavored interfacial reactions or lithium dendritic growth and dead lithium formation. In this work, we use state-of-the-art cryo-electromicroscopy (cryoEM) imaging and spectroscopic techniques to characterize the structure and chemistry of the interface between Li 0 and a polyacrylate-based SPE. Contradicting the conventional knowledge, we find that no protective interphase forms, owing to the sustained reactions between deposited Li dendrites and polyacrylic backbones and succinonitrile plasticizer. Due to the reaction induced volume change, large amounts of cracks form inside the Li dendrites with a stress corrosion-cracking behavior, indicating that Li0cannot be passivated in this SPE system. Based on this observation, we then introduce additive engineering leveraging on the knowledge of liquid electrolytes, and demonstrate that the Li 0 surface can be effectively protected against corrosion using fluoroethylene carbonate (FEC), leading to densely packed Li 0 domes with conformal and stable solid-electrolyte interphases (SEIs) films. Owing to the high room temperature ionic conductivity of 1.01 mS/cm -1 , the high transference number of 0.57 and the stabilized lithium electrolyte interface, this improved new SPE delivers an excellent lithium plating/stripping CE of 99% and 1800 hours of stable cycling in Li||Li symmetric cells (0.2 mA/cm -2 , 1mAh/cm -2 ). Furthermore, this improved cathodic stability along with the high anodic stability enables record high cycle life of >2000 cycles for Li||LiFePO 4 and >400 cycles for Li||LiCoO 2 full cells.

25 ENERGY STORAGE↗
DOE OSTI2020

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO 2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO 2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO 2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NN C-part ). NN C-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R 2 > .94) and RECO (R 2 > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO 2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO 2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN C-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2020

Who Is Metabolizing What? Discovering Novel Biomolecules in the Microbiome and the Organisms Who Make Them

Even as the field of microbiome research has made huge strides in mapping microbial community composition in a variety of environments and organisms, explaining the phenotypic influences on the host by microbial taxa – both known and unknown – and their specific functions still remain major challenges. A pressing need is the ability to assign specific functions in terms of enzymes and small molecules to specific taxa or groups of taxa in the community. This knowledge will be crucial for advancing personalized therapies based on the targeted modulation of microbes or metabolites that have predictable outcomes to benefit the human host. This perspective article advocates for the combined use of standards-free metabolomics and activity-based protein profiling strategies to address this gap in functional knowledge in microbiome research via the identification of novel biomolecules and the attribution of their production to specific microbial taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗