Showing papers by "General Electric published in 2019"

The 2019 materials by design roadmap

Abstract: Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design.

A Comprehensive Review on Second-Life Batteries: Current State, Manufacturing Considerations, Applications, Impacts, Barriers & Potential Solutions, Business Strategies, and Policies

Abstract: The number of used batteries is increasing in quantity as time passes by, and this amount is to expand drastically, as electric vehicles are getting increasingly popular. Proper disposal of the spent batteries has always been a concern, but it has also been discovered that these batteries often retain enough energy perfectly suited for other uses, which can extend the batteries’ operational lifetime into a second one. Such use of batteries has been termed as the “second-life,” and it is high time to adopt such usage in large scale to properly exploit the energy and economics that went into battery production and reduce the environmental impacts of battery waste ending up in landfills. This paper aids in that quest by providing a complete picture of the current state of the second-life battery (SLB) technology by reviewing all the prominent work done in this field previously. The second-life background, manufacturing process of energy storage systems using the SLBs, applications, and impacts of this technology, required business strategies and policies, and current barriers of this technology along with potential solutions are discussed in detail in this paper to act as a major stepping stone for future research in this ever-expanding field.

A System-Level Approach to Controller Synthesis

Abstract: Biological and advanced cyber-physical control systems often have limited, sparse, uncertain, and distributed communication and computing in addition to sensing and actuation. Fortunately, the corresponding plants and performance requirements are also sparse and structured, and this must be exploited to make constrained controller design feasible and tractable. We introduce a new “system level” (SL) approach involving three complementary SL elements. SL parameterizations (SLPs) provide an alternative to the Youla parameterization of all stabilizing controllers and the responses they achieve, and combine with SL constraints (SLCs) to parameterize the largest known class of constrained stabilizing controllers that admit a convex characterization, generalizing quadratic invariance. SLPs also lead to a generalization of detectability and stabilizability, suggesting the existence of a rich separation structure, that when combined with SLCs is naturally applicable to structurally constrained controllers and systems. We further provide a catalog of useful SLCs, most importantly including sparsity, delay, and locality constraints on both communication and computing internal to the controller, and external system performance. Finally, we formulate SL synthesis problems, which define the broadest known class of constrained optimal control problems that can be solved using convex programming.

Generating Responses with a Specific Emotion in Dialog

Abstract: It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction. After a careful investigation of real-life conversation data, we found that there are at least two ways to express emotions with language. One is to describe emotional states by explicitly using strong emotional words; another is to increase the intensity of the emotional experiences by implicitly combining neutral words in distinct ways. We propose an emotional dialogue system (EmoDS) that can generate the meaningful responses with a coherent structure for a post, and meanwhile express the desired emotion explicitly or implicitly within a unified framework. Experimental results showed EmoDS performed better than the baselines in BLEU, diversity and the quality of emotional expression.

Practical guides for x-ray photoelectron spectroscopy: First steps in planning, conducting, and reporting XPS measurements

Abstract: Over the past three decades, the widespread utility and applicability of X-ray photoelectron spectroscopy (XPS) in research and applications has made it the most popular and widely used method of surface analysis. Associated with this increased use has been an increase in the number of new or inexperienced users which has led to erroneous uses and misapplications of the method. This article is the first in a series of guides assembled by a committee of experienced XPS practitioners that are intended to assist inexperienced users by providing information about good practices in the use of XPS. This first guide outlines steps appropriate for determining whether XPS is capable of obtaining the desired information, identifies issues relevant to planning, conducting and reporting an XPS measurement, and identifies sources of practical information for conducting XPS measurements. Many of the topics and questions addressed in this article also apply to other surface-analysis techniques.

On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach

Abstract: Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustor behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustor anomaly detection performance.

Noninvasive sub-organ ultrasound stimulation for targeted neuromodulation

Abstract: Tools for noninvasively modulating neural signaling in peripheral organs will advance the study of nerves and their effect on homeostasis and disease. Herein, we demonstrate a noninvasive method to modulate specific signaling pathways within organs using ultrasound (U/S). U/S is first applied to spleen to modulate the cholinergic anti-inflammatory pathway (CAP), and US stimulation is shown to reduce cytokine response to endotoxin to the same levels as implant-based vagus nerve stimulation (VNS). Next, hepatic U/S stimulation is shown to modulate pathways that regulate blood glucose and is as effective as VNS in suppressing the hyperglycemic effect of endotoxin exposure. This response to hepatic U/S is only found when targeting specific sub-organ locations known to contain glucose sensory neurons, and both molecular (i.e. neurotransmitter concentration and cFOS expression) and neuroimaging results indicate US induced signaling to metabolism-related hypothalamic sub-nuclei. These data demonstrate that U/S stimulation within organs provides a new method for site-selective neuromodulation to regulate specific physiological functions.

A Megawatt-Scale Medium-Voltage High-Efficiency High Power Density “SiC+Si” Hybrid Three-Level ANPC Inverter for Aircraft Hybrid-Electric Propulsion Systems

Abstract: A hybrid-electric propulsion system is an enabling technology to make the aircraft more fuel saving, quieter, and lower carbide emission. In this article, a megawatt (MW) scale power inverter based on a three-level active neutral-point-clamped (3L-ANPC) topology will be developed. To achieve high efficiency, the switching devices operating at carrier frequency in the power converter are configured by the emerging silicon carbide (SiC) metal–oxide–semiconductor field-effect transistors, while the conventional silicon (Si) insulated-gate bipolar transistors are selected for switches operating at the fundamental output frequency. To obtain high power density, dc bus voltage is increased from the conventional 270 V to medium voltage of 2.4 kV to reduce cable weight. Also, unlike the traditional 400 Hz dominated aircraft ac systems, the rated fundamental output frequency here is boosted to 1.4 kHz to drive the high-speed motor, which helps further to reduce the motor weight. Main hardware development and control modulation strategies are presented. Experimental results are presented to verify the performance of this MW-scale medium-voltage “SiC+Si” hybrid 3L-ANPC inverter. It is shown that the 1-MW 3L-ANPC inverter can achieve a high efficiency of 99% and a high power density of 12 kVA/kg.

A Modular SiC High-Frequency Solid-State Transformer for Medium-Voltage Applications: Design, Implementation, and Testing

Abstract: The advancements in wide-bandgap devices are enabling applications of power electronic converters coupled directly with medium-voltage (MV) grids that incorporate galvanic isolation within the converter using high-frequency solid-state transformers (SSTs). This paper presents analysis, design, and the characterization results of a 50-kVA modular soft-switched ac–ac SST using silicon carbide (SiC) MOSFET for MV (>6 kV ac) applications. The SST is comprised of two hard-switched ac-line interface bridges and a resonant dc–dc stage switched at approximately 180 kHz. To minimize the cost of switching elements per ampere and maximize the design flexibility, the design uses multiple discrete SiC devices of, readily available, 1700-V ratings. This paper covers the analysis of soft-switching operation, control architecture, converter design, and system-level integration.

Adaptable microfluidic system for single-cell pathogen classification and antimicrobial susceptibility testing.

Abstract: Infectious diseases caused by bacterial pathogens remain one of the most common causes of morbidity and mortality worldwide. Rapid microbiological analysis is required for prompt treatment of bacterial infections and to facilitate antibiotic stewardship. This study reports an adaptable microfluidic system for rapid pathogen classification and antimicrobial susceptibility testing (AST) at the single-cell level. By incorporating tunable microfluidic valves along with real-time optical detection, bacteria can be trapped and classified according to their physical shape and size for pathogen classification. By monitoring their growth in the presence of antibiotics at the single-cell level, antimicrobial susceptibility of the bacteria can be determined in as little as 30 minutes compared with days required for standard procedures. The microfluidic system is able to detect bacterial pathogens in urine, blood cultures, and whole blood and can analyze polymicrobial samples. We pilot a study of 25 clinical urine samples to demonstrate the clinical applicability of the microfluidic system. The platform demonstrated a sensitivity of 100% and specificity of 83.33% for pathogen classification and achieved 100% concordance for AST.

Efficacy and tolerability of anti-programmed death-ligand 1 (PD-L1) antibody (Avelumab) treatment in advanced thymoma.

Abstract: Thymic epithelial tumors are PD-L1–expressing tumors of thymic epithelial origin characterized by varying degrees of lymphocytic infiltration and a predisposition towards development of paraneoplastic autoimmunity. PD-1–targeting antibodies have been evaluated, largely in patients with thymic carcinoma. We sought to evaluate the efficacy and safety of the anti-PD-L1 antibody, avelumab (MSB0010718C), in patients with relapsed, advanced thymic epithelial tumors and conduct correlative immunological studies. Seven patients with thymoma and one patient with thymic carcinoma were enrolled in a phase I, dose-escalation trial of avelumab (MSB0010718C), and treated with avelumab at doses of 10 mg/kg to 20 mg/kg every 2 weeks until disease progression or development of intolerable side effects. Tissue and blood immunological analyses were conducted. Two of seven (29%) patients with thymoma had a confirmed Response Evaluation Criteria in Solid Tumors–defined partial response, two (29%) had an unconfirmed partial response and three patients (two thymoma; one thymic carcinoma) had stable disease (43%). Three of four responses were observed after a single dose of avelumab. All responders developed immune-related adverse events that resolved with immunosuppressive therapy. Only one of four patients without a clinical response developed immune-related adverse events. Responders had a higher absolute lymphocyte count, lower frequencies of B cells, regulatory T cells, conventional dendritic cells, and natural killer cells prior to therapy. These results demonstrate anti-tumor activity of PD-L1 inhibition in patients with relapsed thymoma accompanied by a high frequency of immune-related adverse events. Pre-treatment immune cell subset populations differ between responders and non-responders. ClinicalTrials.gov - NCT01772004 . Date of registration – January 21, 2013.

Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

Abstract: Bearing is the major contributor to wind turbine gearbox failures. Accurate remaining useful life prediction for drivetrain gearboxes of wind turbines is of great importance to achieve condition-based maintenance to improve the wind turbine reliability and reduce the cost of wind power. However, remaining useful life prediction is a challenging work due to the limited monitoring data and the lack of an accurate physical fault degradation model. The particle filtering method has been used for the remaining useful life prediction of wind turbine drivetrain gearboxes, but suffers from the particle impoverishment problem due to a low particle diversity, which may lead to unsatisfactory prediction results. To solve this problem, this paper proposes an enhanced particle filtering algorithm in which an adaptive neuro-fuzzy inference system is designed to learn the state transition function in the fault degradation model using the fault indicator extracted from the monitoring data; a particle modification method and an improved multinomial resampling method are proposed to improve the particle diversity in the resampling process to solve the particle impoverishment problem. The enhanced particle filtering algorithm is applied successfully to predict the remaining useful life of a bearing in the drivetrain gearbox of a 2.5 MW wind turbine equipped with a doubly-fed induction generator.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

Abstract: This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Deep learning medical systems and methods for image reconstruction and quality evaluation

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Fault Diagnosis of Wind Turbine Gearboxes Based on DFIG Stator Current Envelope Analysis

Abstract: The drivetrains of many utility-scale wind turbines have a gearbox connected with a doubly fed induction generator (DFIG). Since gearbox failure is a major contributor to the high maintenance cost of wind turbines faced by the wind industry, it is important to perform fault diagnosis for gearboxes. Among different gearbox fault diagnosis methods, those using current signals collected from generator terminals have shown their merits in terms of complexity, cost, and reliability. In this paper, a new method that uses a DFIG stator current signal for the fault diagnosis of wind turbine drivetrain gearbox in nonstationary conditions is proposed. In the proposed method, the dc offset and high frequency noise of the current signal are first eliminated. Then, the envelope of the current signal is obtained by using the Hilbert transform. The current envelope signal only contains nonstationary frequencies that are proportional to the DFIG shaft rotating frequency. Next, a synchronous resampling algorithm is designed to convert the current envelope signal with a constant time interval to a resampled current envelope signal with a constant phase angle interval. Finally, the power spectral density analysis is used to obtain the frequency spectrum of the resampled current envelope signal from which the constant characteristic frequencies can be easily identified for the purpose of fault diagnosis. Laboratory test data collected from a 1/3 hp DFIG wind turbine drivetrain test rig and field data obtained from three 1.6-MW DFIG wind turbines are used to show the superiority of the proposed method.

The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal

Abstract: The Sandia Fracture Challenges provide a forum for the mechanics community to assess its ability to predict ductile fracture through a blind, round-robin format where mechanicians are challenged to predict the deformation and failure of an arbitrary geometry given experimental calibration data. The Third Challenge (SFC3) required participants to predict fracture in an additively manufactured (AM) 316L stainless steel bar containing through holes and internal cavities that could not have been conventionally machined. The volunteer participants were provided extensive data including tension and notched tensions tests of 316L specimens built on the same build-plate as the Challenge geometry, micro-CT scans of the Challenge specimens and geometric measurements of the feature based on the scans, electron backscatter diffraction (EBSD) information on grain texture, and post-test fractography of the calibration specimens. Surprisingly, the global behavior of the SFC3 geometry specimens had modest variability despite being made of AM metal, with all of the SFC3 geometry specimens failing under the same failure mode. This is attributed to the large stress concentrations from the holes overwhelming the stochastic local influence of the AM voids and surface roughness. The teams were asked to predict a number of quantities of interest in the response based on global and local measures that were compared to experimental data, based partly on Digital Image Correlation (DIC) measurements of surface displacements and strains, including predictions of variability in the resulting fracture response, as the basis for assessment of the predictive capabilities of the modeling and simulation strategies. Twenty-one teams submitted predictions obtained from a variety of methods: the finite element method (FEM) or the mesh-free, peridynamic method; solvers with explicit time integration, implicit time integration, or quasi-statics; fracture methods including element deletion, peridynamics with bond damage, XFEM, damage (stiffness degradation), and adaptive remeshing. These predictions utilized many different material models: plasticity models including J2 plasticity or Hill yield with isotropic hardening, mixed Swift-Voce hardening, kinematic hardening, or custom hardening curves; fracture criteria including GTN model, Hosford-Coulomb, triaxiality-dependent strain, critical fracture energy, damage-based model, critical void volume fraction, and Johnson-Cook model; and damage evolution models including damage accumulation and evolution, crack band model, fracture energy, displacement value threshold, incremental stress triaxiality, Cocks-Ashby void growth, and void nucleation, growth, and coalescence. Teams used various combinations of calibration data from tensile specimens, the notched tensile specimens, and literature data. A detailed comparison of results based of these different methods is presented in this paper to suggest a set of best practices for modeling ductile fracture in situations like the SFC3 AM-material problem. All blind predictions identified the nominal crack path and initiation location correctly. The SFC3 participants generally fared better in their global predictions of deformation and failure than the participants in the previous Challenges, suggesting the relative maturity of the models used and adoption of best practices from previous Challenges. This paper provides detailed analyses of the results, including discussion of the utility of the provided data, challenges of the experimental-numerical comparison, defects in the AM material, and human factors.

Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.

Abstract: Level of Evidence: 5

Online Junction Temperature Monitoring Using Intelligent Gate Drive for SiC Power Devices

Abstract: Junction temperature is an important design/operation parameter, as well as, a significant indicator of device's health condition for power electronics converters. Compared to its silicon (Si) counterparts, it is more critical for silicon carbide (SiC) devices due to the reliability concern introduced by the immaturity of new material and packaging. This paper proposes a practical implementation using an intelligent gate drive for online junction temperature monitoring of SiC devices based on turn- off delay time as the thermo-sensitive electrical parameter. First, the sensitivity of turn- off delay time on the junction temperature for fast switching SiC devices is analyzed. A gate impedance regulation assist circuit is proposed to enhance the sensitivity by a factor of 60 and approach 736 ps/°C tested in the case study with little penalty on the power conversion performance. Next, an online monitoring unit based on gate assist circuits is developed to monitor the turn- off delay time in real time with the resolution less than 104 ps. As a result, the micro-controller is capable of “reading” junction temperature during the converter operation. Finally, a SiC-based half-bridge inverter is constructed with an intelligent gate drive consisting of the gate impedance regulation circuit and online turn- off delay time monitoring unit. Experimental results demonstrate the feasibility and accuracy of the proposed approach.

Attack Detection for Securing Cyber Physical Systems

Abstract: Cyber-physical systems (CPSs) security has become a critical research topic as more and more CPS applications are making increasing impacts in diverse industrial sectors. Due to the tight interaction between cyber and physical components, CPS security requires a different strategy from the traditional information technology (IT) security. In this paper, we propose a machine learning-based attack detection (AD) scheme, as part of our overall CPS security strategies. The proposed scheme performs AD at the physical layer by modeling and monitoring physics or physical behavior of the physical asset or process. In developing the proposed AD scheme, we devote our efforts on intelligently deriving salient signatures or features out of the large number of noisy physical measurements by leveraging physical knowledge and using advanced machine learning techniques. Such derived features not only capture the physical relationships among the measurements but also have more discriminant power in distinguishing normal and attack activities. In our experimental study for demonstrating the effectiveness of the proposed AD scheme, we consider heavy-duty gas turbines of combined cycle power plants as the CPS application. Using the data from both the high-fidelity simulation and several real plants, we demonstrate that our proposed AD scheme is effective in early detection of attacks or malicious activities.

VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results

Abstract: Recently, automatic visual data understanding from drone platforms becomes highly demanding. To facilitate the study, the Vision Meets Drone Object Detection in Image Challenge is held the second time in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones. Results of 33 object detection algorithms are presented. For each participating detector, a short description is provided in the appendix. Our goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them. The evaluation protocol of the VisDrone-DET2019 Challenge and the comparison results of all the submitted detectors on the released dataset are publicly available at the website: http: //www.aiskyeye.com/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.

VisDrone-VID2019: The Vision Meets Drone Object Detection in Video Challenge Results

Abstract: Video object detection has drawn great attention recently. The Vision Meets Drone Object Detection in Video Challenge 2019 (VisDrone-VID2019) is held to advance the state-of-the-art in video object detection for videos captured by drones. Specifically, there are 13 teams participating the challenge. We also report the results of 6 state-of-the-art detectors on the collected dataset. A short description is provided in the appendix for each participating detector. We present the analysis and discussion of the challenge results. Both the dataset and the challenge results are publicly available at the challenge website: http://www.aiskyeye.com/.

Significantly conquering moisture-induced luminescence quenching of red line-emitting phosphor Rb2SnF6:Mn4+ through H2C2O4 triggered particle surface reduction for blue converted warm white light-emitting diodes

Abstract: Mn4+ doped fluoride phosphors have attracted tremendous attention in the solid-state lighting field due to the outstanding feature of efficient narrow band red emission. However, poor resistance against moisture-induced luminescence quenching is a recognized obstacle for realizing their wider commercial use. Herein, we design and fabricate a highly stable red phosphor based on previously unnoticed Rb2SnF6:Mn4+, which, estimated by the single crystal X-ray diffraction data, crystallizes in the trigonal space group Pm1 with the lattice parameters a = b = 6.0323(12) A, c = 4.7880(8) A, and V = 150.89(7) A3. The Rb2SnF6:Mn4+ as expected exhibits highly efficient red emission upon excitation by UV and blue light. Significantly, the poor water resistance is conquered by constructing a protective deactivated layer with surface reduction of Mn4+. A treatment solution with appropriate reducing ability is emphasized to obtain Rb2SnF6:Mn4+ with simultaneous high brightness and water resistance. The results show that the low concentration of H2C2O4 solution treated Rb2SnF6:Mn4+ preserves a bright red-emitting color analogous to its initial color and >95% of its initial emission intensity when immersing in water at room temperature (RT) and in boiling water for 3 h. Lastly, by employing H2C2O4 treated Rb2SnF6:Mn4+ as a red phosphor, a high quality WLED with a CRI of 90, CCT of 3936 K and luminous efficacy of 106.24 lm W−1 is fabricated. This work not only experimentally fabricates a new efficient and stable Rb2SnF6:Mn4+ phosphor that can be used for high performance warm WLEDs but also provides a deep insight into how to well address moisture-induced Mn4+ luminescence quenching through appropriate reduction of Mn4+, opening up new opportunities for future solid-state lighting purposes.

Evaluation of Switching Loss Contributed by Parasitic Ringing for Fast Switching Wide Band-Gap Devices

Abstract: Parasitic ringing is commonly observed during the high-speed switching of wide band-gap (WBG) devices. Additional loss contributed by parasitic ringing becomes a concern especially for high switching frequency applications. This paper investigates the effects of parasitic ringing on the switching loss of WBG devices in a phase-leg configuration. An analytical switching loss model considering parasitics in power devices and application circuit is derived. Two switching commutation modes, gate drive dominated mode and power loop dominated mode, are investigated, respectively, and the switching loss induced by damping ringing is identified. It is found that this portion of the loss is at most the energy stored in parasitics, which always exists regardless of the switching speed and parasitic ringing. Therefore, with the given WBG device in the specific application circuit, damping more severe parasitic ringing during faster switching transient would not introduce higher switching loss. Additionally, the extra switching loss induced by resonance among parasitics and crosstalk is investigated. It is observed that severe resonance and its resultant over-voltage during the turn- on transient worsen the crosstalk, causing large shoot-through current and excessive switching loss. The theoretical analysis has been verified by the double pulse test with a 1200-V/50-A SiC-based phase-leg power module.

Qualification and certification of metal additive manufactured hardware for aerospace applications

Abstract: Additive manufacturing is being increasingly used to develop new metal products in the aerospace sector. However, like in other commercial materials and processes, variation in part quality and mechanical properties due to inadequate control of dimensions, microstructure, potential defects, surface roughness, and residual stress can result in designs that limit a part’s use in high-value or mission-critical applications. To ensure quality and consistency and to enable more widespread use, robust quality control and qualification and certification (Q&C) procedures are needed for additive manufactured (AM) hardware. Unfortunately, few quality documents are publicly available, forcing aerospace companies and organizations to establish their own guidelines. Furthermore, where parts and systems require regulator certification, requirement interpretations are still evolving. This chapter reviews current Q&C best practice for metal AM hardware used in aerospace applications from both industry and government perspectives. Current Q&C standardization needs are summarized and recommendations made to accelerate the use of metal AM hardware in the aerospace sector.

A Distributed Power System Control Architecture for Improved Distribution System Resiliency

Abstract: Electric distribution systems around the world are seeing an increasing number of utility-owned and non-utility-owned (customer-owned) intelligent devices and systems being deployed. New deployments of utility-owned assets include self-healing systems, microgrids, and distribution automation. Non-utility-owned assets include solar photovoltaic generation, behind-the-meter energy storage systems, and electric vehicles. While these deployments provide potential data and control points, the existing centralized control architectures do not have the flexibility or the scalability to integrate the increasing number or variety of devices. The communication bandwidth, latency, and the scalability of a centralized control architecture limit the ability of these new devices and systems from being engaged as active resources. This paper presents a standards-based architecture for the distributed power system controls, which increases operational flexibility by coordinating centralized and distributed control systems. The system actively engages utility and non-utility assets using a distributed architecture to increase reliability during normal operations and resiliency during extreme events. Results from laboratory testing and preliminary field implementations, as well as the details of an ongoing full-scale implementation at Duke Energy, are presented.