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DOE OSTI2020

Smart Adhesive Joint with High-Definition Fiber-Optic Sensing for Automotive Applications

Structural health monitoring of fiber-reinforced composite-based joints for automotive applications during their manufacturing and on-demand assessment for its durability in working environments is critically needed. High-definition fiber-optic sensing is an effective method to measure internal strain/stress development using minimally invasive continuous sensors. The sensing fiber diameters are in the same order of magnitude when compared to reinforcement (glass, basalt, or carbon fibers) used in polymer composites. They also offer a unique ability to monitor the evolution of residual stresses after repeated thermal exposure with varying temperatures for automotive components/joints during painting using an electrophoretic painting process. In this paper, a high-definition fiber-optic sensor utilizing Rayleigh scattering is embedded within an adhesive joint between a carbon fiber-reinforced thermoset composite panel and an aluminum panel to measure spatially resolved strain development, residual strain, and thermal expansion properties during the electrophoretic paint process-simulated conditions. The strain measured by the continuous fiber-optic sensor was compared with an alternate technique using thermal digital image correlation. The fiber-optic sensor was able to identify the spatial variation of residual strains for a discontinuous carbon fiber-reinforced composite with varying local fiber orientations and resin content.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Autonomous Restoration of Networked Microgrids Using Communication-Free Smart Sensing and Protection Units

Here this paper presents autonomous restoration of networked microgrids using distributed energy resources (DERs), inverter-based resources (IBRs) in general, and controllable assets of intelligence without communication to achieve a extremely resilient power system restoration. The main innovation of this work lies in use of real-time smart sensing and decision making that enable autonomous recombination of islanded microgrids, which are individually black-started by local grid-forming (GFM) inverter units. The smart circuit breakers (SCBs) designed and demonstrated here play critical roles in the restoration process to achieve autonomy; the autonomous restoration scheme is not pre-engineered and may be subject to encounter overloading, live or downed segments as the boundary of the microgrids dynamically expands without prior knowledge and only with local measurement. We demonstrate autonomous microgrid restoration both in simulation of a modified IEEE 13-bus system, and in a hardware testbed comprising 24 GFM-IBRs and 6-SCBs, depicting several scenarios to evaluate their functionalities and interoperability. This work validates high potential of extremely resilient microgrid using novel approaches for inverter-based bottom-up system restoration in a communication-free paradigm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Brightest cluster galaxies are statistically special from z = 0.3 to z = 1

ABSTRACT We study brightest cluster galaxies (BCGs) in ∼5000 galaxy clusters from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The sample is selected over an area of 830 deg2 and is uniformly distributed in redshift over the range of z = 0.3−1.0. The clusters have stellar masses in the range of 1011.8−1012.9M⊙. We compare the stellar mass of the BCGs in each cluster to what we would expect if their masses were drawn from the mass distribution of the other member galaxies of the clusters. The BCGs are found to be ‘special’, in the sense that they are not consistent with being a statistical extreme of the mass distribution of other cluster galaxies. This result is robust over the full range of cluster stellar masses and redshifts in the sample, indicating that BCGs are special up to a redshift of z = 1.0. However, BCGs with a large separation from the centre of the cluster are found to be consistent with being statistical extremes of the cluster member mass distribution. We discuss the implications of these findings for BCG formation scenarios.

Dalal, Roohi (ORCID:0000000279989899)↗

DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data

Our study aims to explore the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain-hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain-hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain-hillslope transect at 0.50-m resolution and extracted LiDAR-derived metrics to model the rapid change in microtopography. Furthermore, we investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long-term monitoring to capture community-specific physiological responses to perturbations, offering the possibility of bridging local plot-scale observations with large landscape monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Impact of Random Spatial Fluctuation in Non-Uniform Crystalline Phases on the Device Variation of Ferroelectric FET

In this work, a comprehensive study of random spatial fluctuation of the ferroelectric (FE) phase and dielectric (DE) phase in FeFETs is conducted to understand its impact on device variation. It is found that: i) there exists a certain DE percentage threshold that below which the increase of the DE phase does not significantly impact the device memory window and variation and only above which evident device degradation can be observed; ii) increasing the DE phase increases the variation in the memory window and the coercive field distribution further exacerbates the variation, hence degrading the sensing margin; iii) decreasing the number of grains degrades the device variation, which calls for further grain size engineering for variation suppression.

42 ENGINEERING↗

DOE OSTI2017

Impacts of Instrument Limitations on Estimated Raindrop Size Distribution, Radar Parameters, and Model Microphysics during Mei-Yu Season in East China

Instrumentation limitations on measured raindrop size distributions (DSDs) and their derived relations and physical parameters are studied through a comparison of the DSD measurements during mei-yu season in east China by four collocated instruments, that is, a two-dimensional video disdrometer (2DVD), a vertically pointing Micro Rain Radar (MRR), and two laser-optical OTT Particle Size Velocity (PARSIVEL) disdrometers (first generation: OTT-1; second generation: OTT-2). Among the four instruments, the 2DVD provides the most accurate DSD and drop velocity measurements, so its measured rainfall amount has the best agreement with the reference rain gauge. Other instruments tend to miss more small drops ( D < 1 mm), leading to inaccurate DSDs and a lower rainfall amount. The low rainfall estimation becomes significant during heavy rainfall. The impacts of instrument limitations on the microphysical processes (e.g., evaporation and accretion rates) and convective storm morphology are evaluated. This is important especially for mei-yu precipitation, which is dominated by a high concentration of small drops. Furthermore, the instrument limitations need to be taken into account in both QPE and microphysics parameterization.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Performance of Microreactor Test Article with Embedded Sensors During Testing in The Single Primary Heat Extraction and Removal Emulator

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes that use passive heat flow, increasing the surface area for heat transfer without requiring active coolant flow through the reactor core. Additional experimental testing is required to understand the heat rejection limitations of heat pipes and thermal stresses in the monolithic core block due to significant temperature gradients. This report describes the initial characterization and testing of a stainless-steel test article that was fabricated with embedded sensors to measure heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing to simulate nuclear heating. The electrically heated testing was performed in the Single Primary Heat Extraction and Removal Emulator facility located at Idaho National Laboratory. The ultimate goals of this work are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify the heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. Initial tests focused on the feasibility of using advanced fiber-optic sensors and other sensor technologies to improve the understanding of temperature and strain distributions within the electrically heated experiments. However, these sensing capabilities could benefit the broader microreactor community if the sensors could be used to monitor component and system health during nuclear operations to inform a limited number of microreactor operators, ultimately reducing operation and maintenance costs and moving toward semiautonomous operation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Relative entropy minimization over Hilbert spaces via Robbins-Monro

One way of getting insight into non-Gaussian measures is to first obtain good Gaussian approximations. These best fit Gaussians can then provide a sense of the mean and variance of the distribution of interest. They can also be used to accelerate sampling algorithms. This begs the question of how one should measure optimality, and how to then obtain this optimal approximation. Here, we consider the problem of minimizing the distance between a family of Gaussians and the target measure with respect to relative entropy, or Kullback-Leibler divergence. As we are interested in applications in the infinite dimensional setting, it is desirable to have convergent algorithms that are well posed on abstract Hilbert spaces. We examine this minimization problem by seeking roots of the first variation of relative entropy, taken with respect to the mean of the Gaussian, leaving the covariance fixed. We prove the convergence of Robbins-Monro type root finding algorithms in this context, highlighting the assumptions necessary for convergence to relative entropy minimizers. Numerical examples are included to illustrate the algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Traveling Wave Relays for Distribution Feeder Protection with High Penetrations of Distributed Energy Resources

Increased penetration of power electronics interfaced Distributed Energy Resources (DER) like PV, electric vehicle and battery storage in the distribution system (both in numbers and sizes) may cause bi-directional power flows under normal operation. In addition, they contribute low fault current under short circuit conditions. These two changes can impact the proper operation of legacy protection systems. This can add additional challenges during weak grid and islanded operation. In this research, traveling wave (high frequency) signature based protection scheme is proposed for future distribution systems. Simulations are performed under different transient scenarios to provide insight into the high frequency signatures to show the traveling wave behavior. Finally, this paper discusses the importance of sensing and digital processing requirements for traveling-wave protection in distribution system.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Traveling Wave Relays for Distribution Feeder Protection with High Penetrations of Distributed Energy Resources: Preprint

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Traveling Wave Relays for Distribution Feeder Protection with High Penetrations of Distributed Energy Resources

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2015

Power-law and exponential rank distributions: A panoramic Gibbsian perspective

Rank distributions are collections of positive sizes ordered either increasingly or decreasingly. Many decreasing rank distributions, formed by the collective collaboration of human actions, follow an inverse power-law relation between ranks and sizes. This remarkable empirical fact is termed Zipf’s law, and one of its quintessential manifestations is the demography of human settlements — which exhibits a harmonic relation between ranks and sizes. In this paper we present a comprehensive statistical-physics analysis of rank distributions, establish that power-law and exponential rank distributions stand out as optimal in various entropy-based senses, and unveil the special role of the harmonic relation between ranks and sizes. Our results extend the contemporary entropy-maximization view of Zipf’s law to a broader, panoramic, Gibbsian perspective of increasing and decreasing power-law and exponential rank distributions — of which Zipf’s law is one out of four pillars.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Vinculin is required for neuronal mechanosensing but not for axon outgrowth

Integrin receptors are transmembrane proteins that bind to the extracellular matrix (ECM). In most animal cell types integrins cluster together with adaptor proteins at focal adhesions that sense and respond to external mechanical signals. In the central nervous system (CNS), ECM proteins are sparsely distributed, the tissue is comparatively soft and neurons do not form focal adhesions. Thus, how neurons sense tissue stiffness is currently poorly understood. Here, we found that integrins and the integrin-associated proteins talin and focal adhesion kinase (FAK) are required for the outgrowth of neuronal processes. Vinculin, however, whilst not required for neurite outgrowth was a key regulator of integrin-mediated mechanosensing of neurons. During growth, growth cones of axons of CNS derived cells exerted dynamic stresses of around 10–12 Pa on their environment, and axons grew significantly longer on soft (0.4 kPa) compared to stiff (8 kPa) substrates. Depletion of vinculin blocked this ability of growth cones to distinguish between soft and stiff substrates. These data suggest that vinculin in neurons acts as a key mechanosensor, involved in the regulation of growth cone motility.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Low-power self-organizing reporting devices: Situational awareness using distributed sensor arrays, STL-102-16, Year 1 of 2

Presentation about development of a small, inexpensive sensing and communications device meant for persistent monitoring in a wide range of circumstances. The device provides light, IR, infrasound, sound, ultrasound, magnetometer, accelerometer, temperature, pressure, humidity, and GPS sensing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Enabling Interoperability on PV Inverters Using IEC 61850

Inverter based distributed energy resources are integrated into distribution system at a fast pace. Distribution management systems, microgrid controllers, and other distributed assets need to communicate with each other for sensing and control of the different assets. Interoperability is critical to the ease of data exchange between different assets. This presentation will provide a background on interoperability and a laboratory implementation using IEC 61850. Specifically, this presentation will discuss the use of IEC 61850 7-420 to enable interoperability in PV inverter.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗

DOE OSTI2021

Seabird distribution is better predicted by abundance of prey than oceanography. A case study in the Gulf of Cadiz (SW, Iberian Peninsula)

Abstract Quantifying factors that influence marine predator distributions is essential to understanding the current and future change in marine biodiversity. Here, we test whether marine predator distribution relates to prey, or is better predicted by other specific habitat features. We examine the correlation between spatial distribution of three seabird species and their prey, as well as environmental proxies (oceanographic characteristics) in the Gulf of Cadiz, NE Atlantic. We modeled the at-sea distribution of Cory's shearwater, Balearic shearwater and Northern gannet, based on: (i) pelagic fish abundance according to acoustic surveys, and (ii) a forecast-model of remotely sensed environmental variables (productivity, sea surface temperature, and salinity). In general, seabird distributions were better predicted by abundance of fish than by environmental variables at the habitat scale. We obtained consistent correlations between seabird presence and the abundance of medium-sized (10–20 cm) sardines, anchovies and Mediterranean horse mackerel, providing information on their preferred prey. Additionally, oceanographic productivity variables moderately contributed to seabird distribution models, with better predictive value for the critically endangered Balearic shearwater and Northern gannet in the summer, whilst the model for Cory's shearwater's produced poorer predictions. Predator–prey combined studies may represent essential tools for an efficient ecosystem-based management of marine environments.

de la Cruz, Andrés (ORCID:0000000203632498)↗