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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Online Voltage Event Detection Using Synchrophasor Data with Structured Sparsity-Inducing Norms

This paper develops an accurate and computationally efficient data-driven framework to detect voltage events from PMU data streams. It develops an innovative Proximal Bilateral Random Projection (PBRP) algorithm to quickly decompose the PMU data matrix into a low-rank matrix, a row-sparse event-pattern matrix and a noise matrix. Here, the row-sparse pattern matrix significantly distinguishes events from normal behavior. These matrices are then fed into a clustering algorithm to separate voltage events from normal operating conditions. Large-scale numerical study results on real-world PMU data show that the proposed algorithm is computationally more efficient and achieves higher F scores than state-of-the-art benchmarks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Online Detection of Inter-Turn Winding Faults in Single-Phase Distribution Transformers Using Smart Meter Data

Turn-to-turn faults between primary windings due to insulation degradation are a major cause of distribution transformer failure, and occur due to high levels of stress such as overloading and overheating. An additional consequence of these faults is an increased voltage on the transformer secondary due to effective change in turns ratio. This paper develops a novel method for early detection of insulation degradation and subsequent inter-turn winding failure by monitoring the transformer secondary voltage. The algorithm is based on a cumulative sum (CUSUM) statistic and compares voltages on neighbouring transformers to flag degrading assets. Results obtained from simulation as well as experimental data show that smart meter measurements can be utilized to achieve very high detection accuracy while keeping costs low. Here, the paper also demonstrates the validity of the algorithm in the presence of measurement noise, residential solar power injection etc.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Using online tools at the Bovine Genome Database to manually annotate genes in the new reference genome

With the availability of a new highly contiguous Bos taurus reference genome assembly (ARS-UCD1.2), it is the opportune time to upgrade the bovine gene set by seeking input from researchers. Furthermore, advances in graphical genome annotation tools now make it possible for researchers to leverage sequence data generated with the latest technologies to collaboratively curate genes. For many years the Bovine Genome Database (BGD) has provided tools such as the APOLLO genome annotation editor to support manual bovine gene curation. The goal of this paper is to explain the reasoning behind the decisions made in the manual gene curation process while providing examples using the existing BGD tools. We will describe the sources of gene annotation evidence provided at the BGD, including RNAseq and Iso-Seq data. We will also explain how to interpret various data visualizations when curating gene models, and will demonstrate the value of manual gene annotation. The process described here can be applied to manual gene curation for other species with similar tools. With a better understanding of manual gene annotation, researchers will be encouraged to edit gene models and contribute to the enhancement of livestock gene sets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2021

Feasibility of Lead Fast Reactor Heat Exchanger Tube Online Monitoring

Within virtually all reactor systems with heat exchangers, the thinnest barriers between primary and secondary fluids are the heat exchanger tubes. Consistent with light water reactor experience, the advanced reactor heat exchangers are the most susceptible regions for corrosion and leakage (NRC, 2019). Moreover, traditional inspections and repairs using robotics will be expensive or impractical. On-line structural health corrosion monitoring (OLSHM) is therefore desirable to detect tube degradation prior to leaks that may allow mixing of heat exchanger fluids. Rhetorically many molten salt reactor (MSR) designers plan for on-line monitoring of critical reactor components including heat exchangers but there are no proven OLSHM sensors or systems for MSR heat exchangers. Guided wave ultrasound from the tube-end or ID using conventional low-temperature piezoelectric sensors has been shown to be sensitive to pits and cracks less than 50% through-wall in long pipes and tubes (Narayanan M.M., 2019) . Mode and frequency selection are important for practical sensor design decisions and ultimate performance. The L-0 wave-mode is easily excited by L-mode transducers but they have surface normal wave motion that will be sensitive to and more attenuative because of fluid interaction with the tube surface (Rose J.L., 1994). Shear Horizontal (SH) wave modes have material motion parallel to the tube surface therefore are virtually unaffected by fluid interaction. For SH-0 to be nondispersive in steel, the frequency-thickness product (MHz-mm) must be less than approximately 1.5. SH mode transducers must be coupled by a material that can support shear stress; a thin layer of honey works well for temporary sensors and an adhesive or a brazed metallurgical bond for permanent sensors. If sensors are incorporated into the fabrication process, the same kinds of SH-0 waves can be generated from the stagnant flow area of the tube OD without compromising the heat-exchange tube function. Piezoelectric materials are also available that can withstand harsh temperatures of molten salt heat exchangers (Stevenson T., 2015) (Machura et al., 2008). Though they may not have as high-performance characteristics, their performance only needs to be good enough to detect flaws of interest. This project attempts to demonstrate feasibility to develop a sensor that can be sensitive to flaws of interest and perform long-term monitoring of a molten salt reactor heat exchanger. Benefits to the advanced reactor system include: • Minimizing the expense of a reactor shut down for periodic inspection—only shutdown for cause. • Reduce risk of unexpected tube rupture by detecting flaws before they grow to through-wall leaks. • Cost reduction by minimizing need to design for manual/robotic inspectability. Transducer design, characterization of the beam profile, wedges vs. direct application of piezoelectric sensors, and ultrasonic pulse-echo responses from a representative heat exchange tube with fabricated flaw types are discussed.

On-Line Tube Monitor, Guided Wave Ultrasound, SH-0↗
DOE OSTI2022

Large Scale Caching and Streaming of Training Data for Online Deep Learning

The training of deep neural network models on large data remains a difficult problem, despite progress towards scalable techniques. In particular, there is a mismatch between the random but predetermined order in which AI flows select training samples and the streaming I/O patterns for which traditional HPC data storage (e.g., parallel file systems) are designed. In addition, as more data are obtained, it is feasible neither simply to train learning models incrementally, due to catastrophic forgetting (i.e., bias towards new samples), nor to train frequently from scratch, due to prohibitive time and/or resource constraints. In this paper, we study data management techniques that combine caching and streaming with rehearsal support in order to enable efficient access to training samples in both offline training and continual learning. We revisit state-of-art streaming approaches based on data pipelines that transparently handle prefetching, caching, shuffling, and data augmentation, and discuss the challenges and opportunities that arise when combining these methods with data-parallel training techniques. We also report on preliminary experiments that evaluate the I/O overheads involved in accessing the training samples from a parallel file system (PFS) under several concurrency scenarios, highlighting the impact of the PFS on the design of the data pipelines.

data pipelines↗
DOE OSTI2024

Analytical methods for online data quality assessment

This chapter provides a comprehensive overview of the main steps for algorithmic sensor signal quality assessment, which can enhance the decision-making process for water resource recovery facility (WRRF) operation and optimization. It introduces the concept of redundancy as the basis for data quality assessment. It also explains the typical data processing pipeline, which consists of preliminary analysis, data pre-processing, and specific algorithmic approaches. Each of these processes is presented and discussed in three separate sections. Importantly, this chapter introduces the main approaches for data quality assessment, provides guidelines for selecting the most suitable one and the key performance indicators to evaluate them and explains how to collect metadata through such an algorithmic approach.

Aguado, Daniel↗