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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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NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗
DOE OSTI2021

MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling

Abstract. Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.

Spuler, Scott M.↗
NASA NTRS2007

Electrification in Hurricanes over the Tropical Americas: Implication for Stratospheric Water Vapor

This study explores the relation between lightning activity and water vapor in the Tropical Tropopause Layer (TTL) over hurricane systems in the Tropical Americas. The hypothesis herein is that hurricanes that exhibit enhanced lightning activity are associated with stronger updrafts that can transport more moisture directly into the TTL (and subsequently into the tropical stratosphere) or even directly into the tropical stratosphere over this region. The TTL over the Tropical Americas, which includes the Caribbean and Gulf of Mexico, is of particular interest, because summertime cold point tropopause is the lowest in height and thus the warmest in temperature over the tropics. The latter condition implies higher saturation values and thus potential for more water vapor to enter the stratosphere. Climate forecast is very sensitive to stratospheric water vapor abundance, because of the key role that water vapor plays in regulating the chemical and radiative properties of the stratosphere. Given the potential for increases in hurricane intensity and frequency under predicted warmer conditions, it becomes essential to understand the effect of hurricanes on stratospheric water vapor. In this study, we use a combination of ground and space-borne observations as well as trajectory calculations. The observations include: cloud-to-ground (CG) lightning data from the U.S. National Lightning Detection Network (NLDN), geostationary infrared observations from the National Climatic Data Center Hurricane Satellite (HURSAT) data set, cloud properties from Aqua-MODIS, and water vapor from Aura-MLS. We analyze hurricanes from the 2005 season when Aura-MLS data are available, namely: Dennis, Emily, Katrina, Rita, and Wilma. Our analysis consists of examining CG lightning, cloud-top properties, and TTL water vapor (i.e., 100 and 147 mb) over the hurricane while it remains over water in the Tropical Americas region. We investigate daily as well as diurnal statistical properties. The hurricanes analyzed in this study showed that lightning activity is negatively correlated with minimum infrared brightness temperature and positively correlated with 100-mb water vapor. An examination of the maxima in water vapor observed over the hurricane not only shows larger magnitudes, but also larger differences between water vapor averages and water vapor maxima over the hurricane as lightning activity increases. Trajectory calculations are performed using the Flextra model in order to investigate the fate of the moister air masses found in the TTL.

Pittman, Jasna V.↗
NASA NTRS2019

Complex Dynamics of Air Traffic Flow

Air traffic in the United States has continued to grow at a steady pace since 1980, except for a dip immediately after the tragic events of September 11, 2001. There are different growth scenarios associated both with the magnitude and the composition of the future air traffic. The Terminal Area Forecast (TAF), prepared every year by the FAA, projects the growth of traffic in the United States. Both Boeing and Airbus publish market outlooks for air travel annually. Although predicting the future growth of traffic is difficult, there are two significant trends: heavily congested major airports continue to see an increase in traffic, and the emergence of regional jets and other smaller aircraft with fewer passengers operating directly between non-major airports. The interaction between air traffic demand and the ability of the system to provide the necessary airport and airspace resources can be modeled as a network. The size of the resulting network varies depending on the choice of its nodes. It would be useful to understand the properties of this network to guide future design and development. Many questions, such as the growth of delay with increasing traffic demand and impact of the en route weather on future air traffic, require a systematic understanding of the properties of the air traffic network. There has been a major advance in the understanding of the behavior of networks with a large number of components. Several theories have been advanced about the evolution of large biological and engineering networks by authors in diversified disciplines like physics, mathematics, biology and computer science. Several networks exhibit a scale-free property in the sense that the probabilistic distribution of their nodes as a function of connections decreases slower than an exponential. These networks are characterized by the fact that a small number of components have a disproportionate influence on the performance of the network. Scale-free networks are tolerant to random failure of components, but are vulnerable to selective attack on components. This paper examines two network representations for the baseline air traffic system. A network defined with the 40 major airports as nodes and with standard flight routes as links has a characteristic scale: all nodes have 60 or more links and no node has more than 460 links. Another network is defined with baseline aircraft routing structure exhibits an exponentially truncated scale-free behavior. Its degree ranges from 2 connections to 2900 connections, and 225 nodes have more than 250 connections. Furthermore, those high-degree nodes are homogeneously distributed in the airspace. A consequence of this scale-free behavior is that the random loss of a single node has little impact, but the loss of multiple high-degree nodes (such as occurs during major storms in busy airspace) can adversely impact the system. Two future scenarios of air traffic growth are used to predict the growth of air traffic in the United States. It is shown that a three-times growth in the overall traffic may result in a ten-times impact on the density of traffic in certain parts of the United States.

Scale-free Networks↗
DOE OSTI2015

Monticello Mill Tailings Site Operable Unit III Annual Groundwater Report May 2014 Through April 2015, October 2015

This report provides the annual analysis of water quality restoration progress, cumulative through April 2015, for Operable Unit (OU) III, surface water and groundwater, of the U.S. Department of Energy (DOE) Office of Legacy Management Monticello Mill Tailings Site (MMTS). The MMTS is a Comprehensive Environmental Response, Compensation, and Liability Act National Priorities List site located in and near the city of Monticello, San Juan County, Utah. MMTS comprises the 110-acre site of a former uranium- and vanadium-ore-processing mill (mill site) and 1,700 acres of surrounding private and municipal property. Milling operations generated 2.5 million cubic yards of waste (tailings) from 1942 to 1960. The tailings were impounded at four locations on the mill site. Inorganic constituents in the tailings drained from the impoundments to contaminate local surface water (Montezuma Creek) and groundwater in the underlying alluvial aquifer. Mill tailings dispersed by wind and water also contaminated properties surrounding and downstream of the mill site. Remedial actions to remove and isolate radiologically contaminated soil, sediment, and debris from the former mill site (OU I) and surrounding properties (OU II) were completed in 1999 with the encapsulation of the wastes in an engineered repository located on DOE property 1 mile south of the former mill site. Contamination of groundwater and surface water remains within OU III at levels that exceed water quality protection standards. Uranium is the primary contaminant of concern. LM implemented monitored natural attenuation with institutional controls as the OU III remedy in 2004. Because groundwater restoration proceeded more slowly than expected and did not meet performance criteria established in the OU III Record of Decision (June 2004), LM implemented a contingency action in 2009 by an Explanation of Significant Difference to include a pump-and-treat system using a single extraction well and treatment by zero-valent iron (ex-situ treatment system). The contingency action was optimized in 2015 with the installation of 8 extraction wells and 16 monitoring wells in a focused area of the aquifer (area of attainment). Contaminated water is treated by solar evaporation at an existing onsite LM facility. Environmental monitoring at OU III consists of twice-yearly (April and October) collection and analysis of hydrologic and water-quality data from an established network of observation wells, seeps, and surface water locations. The scope of monitoring was expanded in 2009 for the ex situ treatment system and in 2015 for the remedy optimization system. Operation and monitoring of the ex situ treatment system was discontinued in 2014 with the start-up of the remedy optimization system. No data anomalies for OU III water quality trending or restoration progress are identified for the May 2014 through April 2015 reporting period. Although some regions of the aquifer demonstrate decreasing concentration trends, such trending is not evident for the bulk of the aquifer and a prolonged restoration period is indicated. The groundwater contingency remedy optimization system captures significant contaminant mass (primarily uranium) from the area of attainment; however, because that system only became operational in 2015, a long-term forecast of restoration progress is premature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Dispersion of Radionuclides and Exposure Assessment in Urban Environments: A Joint CEA and LLNL Report

In the interest of promoting the international exchange of technical expertise, the US Department of Energy’s Office of Emergency Operations (NA-40) and the French Commissariat à l'Energie Atomique et aux énergies alternatives (CEA) requested that the National Atmospheric Release Advisory Center (NARAC) of Lawrence Livermore National Laboratory (LLNL) in Livermore, California host a joint table top exercise with experts in emergency management and atmospheric transport modeling. In this table top exercise, LLNL and CEA compared each other’s flow and dispersion models. The goal of the comparison is to facilitate the exchange of knowledge, capabilities, and practices, and to demonstrate the utility of modeling dispersal at different levels of computational fidelity. Two modeling approaches were examined, a regional scale modeling approach, appropriate for simple terrain and/or very large releases, and an urban scale modeling approach, appropriate for small releases in a city environment. This report is a summary of LLNL and CEA modeling efforts from this exercise. Two different types of LLNL and CEA models were employed in the analysis: urban-scale models (Aeolus CFD at LLNL/NARAC and Parallel- Micro-SWIFT-SPRAY, PMSS, at CEA) for analysis of a 5,000 Ci radiological release and Lagrangian Particle Dispersion Models (LODI at LLNL/NARAC and PSPRAY at CEA) for analysis of a much larger (500,000 Ci) regional radiological release. Two densely-populated urban locations were chosen: Chicago with its high-rise skyline and gridded street network and Paris with its more consistent, lower building height and complex unaligned street network. Each location was considered under early summer daytime and nighttime conditions. Different levels of fidelity were chosen for each scale: (1) lower fidelity mass-consistent diagnostic, intermediate fidelity Navier-Stokes RANS models, and higher fidelity Navier-Stokes LES for urban-scale analysis, and (2) lower-fidelity single-profile meteorology versus higher-fidelity three-dimensional gridded weather forecast for regional-scale analysis. Tradeoffs between computation time and the fidelity of the results are discussed for both scales. LES, for example, requires nearly 100 times more processor time than the mass-consistent diagnostic model or the RANS model, and seems better able to capture flow entrainment behind tall buildings. As anticipated, results obtained by LLNL and CEA at regional scale around Chicago and Paris look very similar in terms of both atmospheric dispersion of the radiological release and total effective dose. Both LLNL and CEA used the same meteorological data, Lagrangian particle dispersion models, and the same dose coefficients. LLNL and CEA urban-scale modeling results show consistent phenomenological behavior and predict similar impacted areas even though the detailed 3D flow patterns differ, particularly for the Chicago cases where differences in vertical entrainment behind tall buildings are particularly notable. Although RANS and LES (LLNL) models incorporate more detailed physics than do mass-consistent diagnostic flow models (CEA), it is not possible to reach definite conclusions about the prediction fidelity of the various models as experimental measurements were not available for comparison. Stronger conclusions about the relative performances of the models involved and evaluation of the tradeoffs involved in model simplification could be made with a systematic benchmarking of urban-scale modeling. This could be the purpose of a future US / French collaborative exercise.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Confronting Models with Regional CO 2 Observations

This research addressed an important DOE science area: the role of natural disturbances in carbon cycling. Our project included both (a) modeling and field measurements and (b) strengthened ties between DOE national laboratories and universities which continue after the project was completed. Our focus was on understanding the consequences of drought and emissions (fire and urban) to atmospheric trace gas composition (carbon dioxide, carbon monoxide, and methane), including both concentration and stable isotope composition. Measurements of these atmospheric gases were used to infer the spatial and temporal patterns of both sources and sinks in the carbon cycle. As part of this effort, we maintained two long-term monitoring networks along a geographic gradient in Utah and Colorado that spans montane forests, urban regions, and oil/gas fields. In this research, we expanded on a well-accepted atmospheric model (STILT-WRF) as a tool to evaluate fluxes in CLM/CESM-related models. We used the STILT model to provide a strong linkage between point atmospheric measurements and the surface parameterizations/emissions that are part of the CLM/CESM models. Overall, this project benefited and supported the DOE Long Term Mission and Goals in four distinct ways: (a) testing of carbon cycle models, (b) testing and evaluating model mechanisms whereby factors in CLM influence trace gas composition in CESM, (c) acquiring high-quality, long-term data on concentrations of carbon dioxide in western USA ecosystems, and (d) by reducing uncertainties associated with the representation of climate-carbon feedbacks in Earth System models through the development of new methods for evaluating model performance. One postdoctoral associate (Henrique Duarte) and one Ph.D. student (Darek Malia) were trained with this support. Nine peer-reviewed publications and 14 poster presentations were produced with support from this project. This proposal addresses a specific science area in the RFP: the role of natural disturbances in carbon cycling. In particular, we will focus on the consequences of drought and fire at landscape to regional scales for atmospheric trace gas concentration and stable isotope composition (CO 2 , CO, and CH 4 ). We seek to expand on the well accepted atmospheric Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled to the Weather Research and Forecasting (WRF) model as a tool to evaluate stable isotope fluxes in CLM/CESM-related models through existing, long-term data sets and field campaigns where high-precision trace gas concentration and isotope measurements can be made along gradients and transects. Given the confounding impacts of anthropogenic trace gas emissions on landscape-to-regional atmospheric observations, we will also measure and parameterize isotopic characteristics of CO 2 , CO, and CH 4 from anthropogenic or forest sources in order to allow us the opportunity to partition atmospheric trace gases into natural and anthropogenic sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Natural time and nowcasting induced seismicity at the Groningen gas field in the Netherlands

The Groningen gas field in the Netherlands has a significant level of induced seismicity; this seismicity is attributed to fluid withdrawal not fluid injection. This is one of the most productive gas fields in Europe. Production began in 1963 and the first induced earthquake occurred in 1991. Increasing levels of seismicity led to the installation of a sensitive seismic network in 1995 with a minimum magnitude completeness of $M$ ≥ 1.5. The largest induced earthquake was a magnitude 3.6 in 2012, which caused thousands of damage claims and led to a reduction in gas production in 2014 January. In our work, we show that the induced earthquakes satisfy Gutenberg–Richter (GR) frequency–magnitude statistics over the range 1.5 ≤ $M$ ≤ 3.0. We utilize truncated GR scaling to introduce a possible maximum earthquake with $M$ = 3.85. We also demonstrate an almost complete absence of aftershocks following the largest induced earthquakes. To quantify the temporal evolution of the induced seismicity, we perform our nowcasting method. Nowcasting uses the catalogue of seismicity in the region. Two earthquake magnitudes are selected, one large say $M_λ$ ≥ 2.5, and one small say $M_σ$ ≥ 1.5. The method utilizes the number of small earthquakes that occur between pairs of large earthquakes. A major advantage of nowcasting is that it relies on ‘natural time’, earthquake counts, between events rather than clock time. Thus, the results are applicable to induced seismicity that varies in time. We count the rate of occurrence of small induced earthquakes to nowcast the probability of occurrence of larger earthquakes.

58 GEOSCIENCES↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗
DOE OSTI2013

On the stability analysis of a pair of van der Pol oscillators with delayed self-connection, position and velocity couplings

In this paper, we perform a stability analysis of a pair of van der Pol oscillators with delayed self-connection, position and velocity couplings. Bifurcation diagram of the damping, position and velocity coupling strengths is constructed, which gives insight into how stability boundary curves come into existence and how these curves evolve from small closed loops into open-ended curves. The van der Pol oscillator has been considered by many researchers as the nodes for various networks. It is inherently unstable at the zero equilibrium. Stability control of a network is always an important problem. Currently, the stabilization of the zero equilibrium of a pair of van der Pol oscillators can be achieved only for small damping strength by using delayed velocity coupling. An interesting question arises naturally: can the zero equilibrium be stabilized for an arbitrarily large value of the damping strength? We prove that it can be. In addition, a simple condition is given on how to choose the feedback parameters to achieve such goal. We further investigate how the in-phase mode or the out-of-phase mode of a periodic solution is related to the stability boundary curve that it emerges from a Hopf bifurcation. Analytical expression of a periodic solution is derived using an integration method. Some illustrative examples show that the theoretical prediction and numerical simulation are in good agreement.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Cybersecurity Threat Profile for a Connected Lighting System

In anticipation of improved energy performance and cost savings, cities and building owners are increasingly considering “smart lighting initiatives” that aim to convert their collection of simple luminaires (i.e., lighting fixtures) into an intelligent connected lighting system (CLS) capable of remotely monitoring energy consumption and fault conditions, and possibly implementing adaptive lighting schemes. The U.S. Department of Energy (DOE) has set an national goal of tripling the energy efficiency and demand flexibility of the buildings sector by 2030, relative to 2020 levels 1. It is forecast that connected lighting systems can contribute to that goal by delivering 125 TWh of annual energy savings by 2035 2, equivalent to the annual output of 50 typical (500 MW) power plants. However, these energy savings and the DOE goal are put at significant risk if connected technologies are not adopted due to real or perceived cybersecurity concerns. Connected IoT devices such as these have historically been rife with vulnerabilities which sometimes put security considerations secondary to functionality and operability. What are the cybersecurity threats that will impact these systems, as formerly banal luminaires transition into intelligent connected devices that collect information about themselves, their surrounding environment, and possibly us? In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement. Fifty-seven threats were identified. Among our key findings: (1) 65% (37/57) of the threats did not involve the luminaires, but rather the other components needed to communicate with and manage them; (2) 63% (36/57) of the threats could have been mitigated through manufacturer-implemented defensive techniques or “controls”; and (3) 23% (13/57) of the threats were dependent on the network configuration. Recommendations based on the results of this work are made to key stakeholder groups. Notably, lighting technology developers are advised to address all threats that can be reasonably controlled with baked-in technology solutions (e.g., encryption or authentication controls), and employ some form of secure supply chain management and tracking where other parts (e.g., sensors, microprocessors) of a luminaire must also be built and manufactured with the proper security controls in place. Developers should also review threats involving assets not developed in-house to understand how connectivity with other devices will affect their product during system operation and determine if a compensating control for a defense-in-depth strategy will be needed. Finally, those interested in deploying CLS should compare the differences between cloud and on-premise models to determine which is more suitable for their needs and the abilities of their security team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k -means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Observations of Total Lightning Associated with Severe Convection During the Wet Season in Central Florida

This paper will discuss findings of a collaborative lightning research project between National Aeronautics and Space Administration, the Massachusetts Institute of Technology and the National Weather Service office In Melbourne Florida. In August 1996, NWS/MLB received a workstation which incorporates data from the KMLB WSR-88D, Cloud to Ground (CG) stroke data from the National Lightning Detection Network (NLDN), and 3D volumetric lightning data collected from the Kennedy Space Centers' Lightning Detection And Ranging (LDAR) lightning system. The two primary objectives of this lightning workstation, called Lightning Imaging Sensor Data Applications Display (USDAD), are to: observe how total lightning relates to severe convective storm morphology over central Florida, and compare ground based total lightning data (LDAR) to a satellite based lightning detection system. This presentation will focus on objective #1. The LISDAD system continuously displays CG and total lighting activity overlaid on top of the KMLB composite reflectivity product. This allows forecasters to monitor total lightning activity associated with convective cells occurring over the central Florida peninsula and adjacent coastal waters. The LISDAD system also keeps track of the amount of total lightning data, and associated KMLB radar products with individual convective cells occurring over the region. By clicking on an individual cell, a history table displays flash rate information (CG and total lightning) in one minute increments, along with radar parameter trends (echo tops, maximum dBz and height of maximum dBz) every 5 minutes. This history table Is updated continuously, without user intervention, as long as the cell is identified. Reviewing data collected during the 1997 wet season (21 cases) revealed that storms which produced severe weather (hall greater or = 0.75 in. or wind damage) typically showed a rapid rise In total lightning prior to the onset of severe weather. On average, flash rate increases of 25 FPM per minute over a time scale of approximately 5 minutes were common. These pulse severe storms typically reached values of 150 to 200 FPM with some cells exceeding 400 FPM. One finding which could have a direct application to the warning process is that the rapid increase in lightning typically occurred in advance of the warning issuance time. Comparisons between the ending time of the rapid rate increase and the time of when the warning was issued by NWS/MLB meteorologist exhibited a lead time of 8 minutes. It is conceivable that if close monitoring of the LISDAD system by operational meteorologist is routinely performed, warnings for pulse severe storms could be issued up to 4 to 6 minutes earlier than what is issued currently.

Sharp, D.↗
DOE OSTI2019

OPERATIONAL TOPOLOGY ESTIMATION OF THREE PHASE UNBALANCED POWER DISTRIBUTION SYSTEMS WITH OUTAGES

Distribution grid, in general, is constructionally loopy but a radially operated network. Opening and closing of suitable tie-switches allows transition from one radial topology to another. Tie-switches ensure reliability and provide restoration paths when faults (outages) occur. The estimation of the system's current operational topology is a precursor to implementing any optimal control actions (during normal operation) or restorative actions (during outage condition). The existing literature in topology detection suffers with several limitations including: (1) computationally intensive nonlinear formulations that are not scalable for large distribution systems, (2) methods requiring exhaustive search for the correct topology by enumerating the complete topological search space, and (3) approaches requiring large volumes of time-series measurements and are not applicable for estimating continuously changing operational topology. Furthermore, the existing literature on distribution grid topology estimation cannot be extended to estimate the operational topology during outage conditions. For operational topology estimation during outages, our objective is to estimate both energized radial feeder and the outaged sections. Starting with a radial topology, these methods identify outaged feeder sections and hence, are inapplicable when the current radial operational topology is not known. In this work, we present a mathematical programming approach to estimate the operational topology of a three-phase unbalanced power distribution system under both outage and normal operating conditions. The aforementioned limitations of existing literature were countered by modeling the problem as a mixed-integer linear programming (MILP) problem which minimizes the errors in available measurements while satisfying the power flow and structural constraints of the system. Specically, a minimum weighted least absolute value estimator is proposed that uses the instantaneous line flow measurements, historical/ forecasted load data, and ping measurements from a limited set of smart meters and solves an MILP to identify the operational topology based on a linearized three-phase unbalanced power flow model. The proposed approach is tested on IEEE 123-bus feeder and 1069 bus multi-feeder test system and the numerical results prove the applicability of the proposed method to large three phase unbalanced grids with high errors in measurements. It is observed that the approach performs well even at high percentages of measurement errors.

GANDLURU NAGA, ANANDINI↗
DOE OSTI2019

Data Analytics for Electrical Distribution Systems with Micro PMUs (GMLC 1.4.9 Technical Report)

This report describes how electrical utilities with residential, industrial, commercial and government customers make decisions using Data Analytics (DA) applications and discusses how OpenFMB was leveraged to share that data among disparate types of devices for DA. OpenFMB is a data standard developed by utilities for distributed communication and control of power system assets. Here we explored how OpenFMB can be used for sharing analytics and what those analytics could be for a set of representative grid devices. A review of DA applications performed by electrical utilities and customers is presented. An overview of how the testbed we used was built and configured is summarized. Several experiments which were carried out to explore the capabilities of OpenFMB alongside other data sharing mechanisms for gathering sensor measurements and sharing analytics is also discussed. And finally a comparison of OpenFMB and LBNL’s SPARK framework is summarized. Today’s electrical utilities focus on monitoring the real-time customer consumptions, offering new real-time tariff models based on customer usage habits (rewards and punishments), providing energy peak during peak load times (plan energy generation and distribution), improving the load management for transformers and power lines, and controlling the energy and grid resources efficiently. On the other hand, the DA applications for residential customers are based on modifying the customer electricity usage habit to save money, making accurate and timely decisions for electrical billing. However, the DA applications for industrial, commercial and government customers focus on creating efficient programs to improve the environmental condition (reducing CO₂ emission), developing better load forecasting for industrial and/or commercial activities. In this project, a testbed was built to explore how the OpenFMB framework can be used to distribute data among various types of representative grid edge devices. Micro PMUs (Phasor Measurement Unit) were used to collect and transmit data to an OpenPDC server alongside relays with a framework configured with a system using OpenFMB to collect and transmit data. The testbed was composed of a physical asset based on a 60 Hz AC radial power system constructed using SI-GRID components. The power system consisted of a 3-phase 14 volts line-to-ground (24 volts line-to-line) power source, three 3-phase line emulators, a programmable resistive load bank, two smart relays, two micro PMUs, and a (SCADA) and Phasor Data Concentrator (PDC) computer. This testbed configuration allowed us to perform breaker operation tests collecting data from the micro PMUs and from the relays’ sensors. During the micro PMU breaker operation tests we collected the measured and estimated fundamental voltages, currents, frequency as well as the total true, reactive and apparent power. The displacement power factor from two micro PMUs located in parallel power lines was also used. The software steps for the implementation of the Synchrophasor system are explained in detail in this report. The setting (1-2-3) and data collection (4-5) phases were performed with a free download open source software. In the setting phase, the micro PMUs were set (1), the data stream for each micro PMU was validated, and the connection and configuration files were created (2). The PDC computer was set using the connection and configuration files of the micro PMUs (3). In the data collection phase, the binary data from the micro PMUs was collected (4), and the binary data files of the micro PMUs was converted to analyze the test results (5). OpenFMB was configured to use protocol translators to read Modbus and transform the reading into the OpenFMB data model to be read for DA applications.Our experiments showed that by leveraging the OpenFMB data model and the micro PMUs together we could obtain better situational awareness of network faults, which in some cases could lead to failures in the system and would be less perceivable otherwise. For example, in one experiment the fundamental RMS current angle, and the fundamental displacement power factor (estimated by the ratio between the measured real and apparent total power) had a disturbance at the micro PMU connected to the beginning of the parallel power line because the breaker was opened without the operators or SCADA system’s knowledge, and no current was available. Based on the breaker state scenarios and micro PMU locations, the DA applications using the current angle and displacement power factor from the micro PMUs should be studied in detail to avoid a non-desired measurement that could affect the decision from the DA. Also, the sample rates for PMU, Relay and micro PMU devices were compared. The DA application improved the control and operation between the electrical utilities and their customers (residential, industrial, transportation, commercial and government). The higher the sample rate, the better the quality of the measurement for DA estimations.

42 ENGINEERING↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗