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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Development of Testing Methods to Predict Cracking in Photovoltaic Backsheets

In recent years there have been some significant failures of photovoltaic backsheets resulting from inadequacies of design that are not detected in current versions of the qualification standards. In some cases, this has resulted in catastrophic failure in less than 5 y of deployment. In this work, we develop a test sample construction and evaluate accelerated exposure conditions necessary to duplicate the cracking field failure of backsheets using a small and manageable coupon. This sample uses a piece of solder wire to put some controlled topology and stress into the backsheet. Through exposure to different environmental stressors it was found that the application of thermal cycling stresses, after ultraviolet radiation exposure, is of primary importance to duplicate field failures.

14 SOLAR ENERGY↗
DOE OSTI2023

Superiority of compensatory reserve measurement compared with the Shock index for early and accurate detection of reduced central blood volume status

Shock index (SI) equals the ratio of heart rate (HR) to systolic blood pressure (SBP) with clinical evidence that it is more sensitive for trauma patient status assessment and prediction of outcome compared with either HR or SBP alone. We used lower body negative pressure (LBNP) as a human model of central hypovolemia and compensatory reserve measurement (CRM) validated for accurate tracking of reduced central blood volume to test the hypotheses that SI: (1) presents a late signal of central blood volume status; (2) displays poor sensitivity and specificity for predicting the onset of hemodynamic decompensation; and (3) cannot identify individuals at greatest risk for the onset of circulatory shock. We measured HR, SBP, and CRM in 172 human subjects (19–55 years) during progressive LBNP designed to determine tolerance to central hypovolemia as a model of hemorrhage. Subjects were subsequently divided into those with high tolerance (HT) (n = 118) and low tolerance (LT) (n = 54) based on completion of 60 mm Hg LBNP. The time course relationship between SI and CRM was determined and receiver operating characteristic (ROC) area under the curve (AUC) was calculated for sensitivity and specificity of CRM and SI to predict hemodynamic decompensation using clinically defined thresholds of 40% for CRM and 0.9 for SI. The time and level of LBNP required to reach a SI = 0.9 (~60 mm Hg LBNP) was significantly greater (p < 0.001) compared with CRM that reached 40% at ~40 mm Hg LBNP. Shock index did not differ between HT and LT subjects at 45 mm Hg LBNP levels. ROC AUC for CRM was 0.95 (95% CI = 0.94–0.97) compared with 0.91 (0.89–0.94) for SI (p = 0.0002). Despite high sensitivity and specificity, SI delays time to detect reductions in central blood volume with failure to distinguish individuals with varying tolerances to central hypovolemia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Design of BLDC Motor Diagnostic Device Based on Surge Test for Phase to Ground Fault

BLDC motor is essential in the industrial world, especially it been used for an electric vehicle. The benefit of using BLDC motor is that it has high rate efficiency and low mechanical losses. However, to know the isolation condition of winding and increase reliability, it needs to be tested. With the implementation of surge test based fault detection device of BLDC motor, the condition of BLDC motor can be monitored and maintained according to analysis of its condition. The surge test method used because it is sensitive in detecting the initial damage in the isolation failure process on stator winding. A surge voltage is generated from the Cockroft-Walton and switching circuit with capacitors. The result is analyzed using Error Area Ratio (EAR). The test result shows that the higher the value of resistance and inductance, the smaller the frequency of the surge wave. When the BLDC motor in a parallel short circuit condition, the threshold value of the EAR test-reff has a minimum value of 5.28%, an average of 13.52%, and a maximum of 28.55%.

BLDC motor↗
DOE OSTI2025

Monitoring Methods for Early Detection of Inadvertent Fission Product Release at the Advanced Test Reactor

Isotope effluent data obtained during three instances of experiment failures at the Advanced Test Reactor (ATR) are analyzed to provide an overview of the methods used to detect initial signs of unintended fission product release. The data is contextualized with the operational experience, including means of identification and subsequent mitigation strategies, gained during these events. General trends as well as variations in isotopic behavior between the three failures are explored. Background on the Real Time Monitor, a High Purity Germanium detector, and other fission product monitoring systems utilized at the Advanced Test Reactor is also provided. The presented analysis was used to establish administrative action levels which are currently utilized by ATR for early detection of experiment fission product release. Early identification provides time to make programmatic decisions before approaching safety and environmental limits.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2020

Invite the Chaos Monkey: Test Effect Payloads for Tuning ICS Incident Response

In 2010, when Netflix moved their operational systems to the cloud, they developed a pseudo-random mechanism to cause deliberate outages and failures of key system elements to ensure that automated systems for detecting and remediating outages were mature and fully functional. This Chaos Monkey spawned an entire Simian Army designed to ensure system resilience and enable failure response before issues became outages. Through the DARPA RADICS program, Provatech, INL, and other researchers have developed a mechanism for creating controllable failures via Test Effect Payloads (TEPs) for Industrial Control Systems in order to enable an asset owner to tune network and process sensor technology, increase the effectiveness of incident response and remediation, and highlight areas where resilient technology could bring benefit to Operational Technology. This talk will explore what a TEP is, how to create a TEP, and suggest what a systematic system resilience plan using TEP’s might look like. We will also discuss how regulated entities might employ TEP’s in a compliant resilience strategy.

42 ENGINEERING↗
DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Power Loss and Safety Risks - Recoverable vs. Non-Recoverable?

This presentation presents an update to the PV fleet analysis on PV system degradation. A median system degradation of -0.7 %/year was found for almost 5000 inverter traces. No significant difference between silicon and CdTe technologies were found. Systems with trackers showed a slightly higher degradation. Silicon technologies such as PERC and heterojunction showed degradation similar to conventional technologies but also showed different degradation mechanisms. To achieve long lifetimes early failure must be minimized through qualification testing. Rapid detection during normal life is important as is the control of bill of materials in modules.

degradation↗
DOE OSTI2022

Status of Eddy Current Developments at INL

Both standard and advanced Light Water Reactor (LWR) fuel systems are composed of fuel pellets enclosed in a metallic cladding. The fuel cladding represents the first containment of the fission products and the first safety barrier. Upon progression of irradiation, the cladding properties deteriorate, mainly due to the pick-up of hydrogen, which causes embrittlement, and the consequence corrosion that reduced the metallic substrate. Those changes in the properties combined with the harsh environment, e.g., fatigue and fretting occurring during operation, might eventually lead to loss of hermeticity. Therefore, it is important to be able to quantify the amount of corrosion taking place and to detect and size structural discontinuity that can promote cladding failure. Eddy current testing is one of the non-destructive methods employed for this purpose. Inspections are carried out using special probes, which are designed and optimized to inspect a tube with well-defined geometrical and physical characteristics, i.e. external diameter, wall thickness, material electric conductivity and magnetic permeability. The inspection goals are mainly twofold: first, the determination of the amount of corrosion by measurement of the external oxide thickness; second, the detection of flaws in the tube wall, ether on the external or the internal surface of the tube. This report summarizes the available capabilities for eddy current testing of LWR fuel claddings, identifies needs and are of developments for testing of ATF claddings.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Embedded sensors integrated into critical components for in situ health monitoring of steam turbines

Operational flexibility is desired in today’s coal-fired power plants to balance power grids by compensating for the variable electricity supply from renewable energy sources and distributed gensets. This demanding requirement accelerates materials degradation and makes in-situ health monitoring essential. Life monitoring of components and subsystems is thus seen as essential in assessing material and mechanical behavior to estimate system reliability, move to a conditionbased maintenance strategy and determine time to failure of the units in their actual operating conditions. Vibration monitoring can be exploited for blade tip timing to measure blade vibration amplitude and tip clearance to detect any deterioration taking place in the condition of steam turbine blades. A failure of a rotating blade can lead to severe turbine damage followed by extensive repairs and loss of power production. A blade vibration monitoring system can help early detection of abnormal blade vibration behavior. In conjunction with a health monitoring system, the vibration characteristics can be analyzed to support a pro-active maintenance and inspection schedule. While the feasibility of this inspection technique has been amply demonstrated, there is a need to install induction probes to magnetize the blade for signal output. Siemens, in partnership with Raytheon Technologies Research Corporation (RTRC), proposes a holistic approach to develop embedded sensors to utilize radio frequency for not only coupling to sensors, but as the sensing modality. The goal of this project is to “embed” the novel sensing approach by using either additively manufactured or extruded waveguides on rotating blades for recording, evaluation and monitoring of blade vibrations in Low Pressure turbines, with applications extending to aero engines

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Review of Existing and Emerging Methods for Lithium Detection and Characterization in Li-Ion and Li-Metal Batteries

Whether attempting to eliminate parasitic Li metal plating on graphite (and other Li-ion anodes) or enabling stable, uniform Li metal formation in ‘anode-free’ Li battery configurations, the detection and characterization (morphology, microstructure, chemistry) of Li that cannot be reversibly cycled is essential to understand the behavior and degradation of rechargeable batteries. In this review, various approaches used to detect and characterize the formation of Li in batteries are discussed. Each technique has its unique set of advantages and limitations, and works towards solving only part of the full puzzle of battery degradation. Going forward, multimodal characterization holds the most promise towards addressing two pressing concerns in the implementation of the next generation of batteries in the transportation sector (viz. reducing recharging times and increasing the available capacity per recharge without sacrificing cycle life). Such characterizations involve combining several techniques (experimental- and/or modeling-based) in order to exploit their respective advantages and allow a more comprehensive view of cell degradation and the role of Li metal formation in it. Additionally, it is also discussed which individual techniques, or combinations thereof, can be implemented in real-world battery management systems on-board electric vehicles for early detection of potential battery degradation that would lead to failure.

25 ENERGY STORAGE↗
DOE OSTI2022

Node Monitoring as a Fault Detection Countermeasure against Information Leakage within a RISC-V Microprocessor

Advanced, superscalar microprocessors (μP) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the μP. Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The μP designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the μPs.

42 ENGINEERING↗
DOE OSTI2023

Enhancement of operational Flexibility of Power Plants Using IN740

Headers are crucial components within diverse industries, especially in the energy sector, as they enable the efficient transfer of fluids. The selection of materials for headers is determined by their specific applications; for example, Grade 91 and Grade 92 steels are commonly utilized. In our research, we recommend the adoption of INCONEL alloy 740 due to its exceptional robustness and heat-resistant characteristics. One critical parameter in header systems is the heat transfer coefficient, which directly influences the efficiency of heat exchange processes and can thereby impact the structural integrity of the headers. This coefficient is closely linked with factors such as the Nusselt number, which is influenced by fluid flow characteristics and thermal properties. The flow direction within headers, whether unidirectional or multidirectional—significantly affects the overall dynamics of heat transfer. This research focuses on exploring the behavior of headers, a specific pipeline system component, using ANSYS simulation software. The study aims to predict heat transfer and mechanical behavior within headers under various conditions through steady-state and transient simulations (parts 1 and 2 of the report). Key parameters such as heat transfer coefficient, velocity, and temperature are examined with the goal of optimizing header design. Part 3 of the report addresses the critical yet underexplored relationship between pressure drop and heat transfer coefficients in the transient flow regime within headers, specifically focusing on smooth horizontal circular tubes. Limited experimental work has been conducted in this area, prompting the need for comprehensive analysis. By leveraging machine learning techniques, this research aims to establish a correlation between pressure drop and heat transfer across various flow conditions, including laminar, transient, quasi-turbulent, and turbulent regimes. The data utilized for this analysis were meticulously gathered from existing literature, capturing simultaneous measurements of pressure drops and heat transfer. Part 4: In the context of power plants, header pipes are essential components that significantly influence system performance by facilitating the collection and distribution of steam. This report highlights the critical role of header pipes in enhancing reliability, efficiency, and overall power plant performance. A key aspect of this investigation is shape optimization, which aims to maximize performance while minimizing material usage. By focusing on shape optimization, this research contributes to improved efficiency and a reduced environmental footprint for power plant installations. The methodology developed in this study emphasizes optimizing header shapes to decrease reliance on expensive alloy materials and lower maintenance costs. Furthermore, a case study was conducted using a header from an operational power plant to validate the proposed optimization techniques. Finally, part 5 addresses the challenges associated with flexible operations in boiler systems, the study outlines several strategies for enhancing the durability and reliability of steam headers. Key approaches include material selection, which involves utilizing advanced materials with superior high-temperature properties and enhanced fatigue resistance to extend the lifespan of steam headers. Design modifications are also recommended, focusing on implementing changes that mitigate thermal stress and cyclic loading to reduce the likelihood of failures under varying operating conditions. Additionally, the study emphasizes the importance of regular inspection and monitoring by establishing rigorous protocols to detect early signs of damage, allowing for timely maintenance and minimizing the risks of catastrophic failures. Furthermore, operational guidelines are developed to minimize the frequency and severity of thermal transients, ensuring stable and efficient operations. Collectively, these strategies provide a robust framework for overcoming the unique challenges posed by flexible operation in boiler systems, ultimately contributing to improved reliability and performance in power plant operations. Through this comprehensive approach, the study not only enhances the understanding of pressure drop and heat transfer relationships but also promotes advancements in design and operation that will benefit the entire power generation industry.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98% (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63%—the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.

Marfo, William↗
DOE OSTI2022

Optimizing performance and yield of vertical GaN diodes using wafer scale optical techniques

Abstract To improve the manufacturing of vertical GaN devices for power electronics applications, the effects of defects in GaN substrates need to be better understood. Many non-destructive techniques including photoluminescence, Raman spectroscopy and optical profilometry, can be used to detect defects in the substrate and epitaxial layers. Raman spectroscopy was used to identify points of high crystal stress and non-uniform conductivity in a substrate, while optical profilometry was used to identify bumps and pits in a substrate which could cause catastrophic device failures. The effect of the defects was studied using vertical P-i-N diodes with a single zone junction termination extention (JTE) edge termination and isolation, which were formed via nitrogen implantation. Diodes were fabricated on and off of sample abnormalities to study their effects. From electrical measurements, it was discovered that the devices could consistently block voltages over 1000 V (near the theoretical value of the epitaxial layer design), and the forward bias behavior could consistently produce on-resistance below 2 mΩ cm 2 , which is an excellent value considering DC biasing was used and no substrate thinning was performed. It was found that high crystal stress increased the probability of device failure from 6 to 20%, while an inhomogeneous carrier concentration had little effect on reverse bias behavior, and slightly (~ 3%) increased the on-resistance (R on ). Optical profilometry was able to detect regions of high surface roughness, bumps, and pits; in which, the majority of the defects detected were benign. However a large bump in the termination region of the JTE or a deep pit can induce a low voltage catastrophic failure, and increased crystal stress detected by the Raman correlated to the optical profilometry with associated surface topography.

42 ENGINEERING↗