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NASA NTRS2007

Oxidation State of Nakhlites as inferred from Fe-Ti oxide Equilibria and Augite/Melt Europium Partitioning

Recent studies have shown that Martian magmas had wide range of oxygen fugacities (fO2) and that this variation is correlated with the variation of La/Yb ratio and isotopic characteristics of the Martian basalts, shergottite meteorites. The origin of this correlation must have important information about mantle sources and Martian evolution. In order to understand this correlation, it is necessary to know accurate value of oxidation state of other Martian meteorite groups. Nakhlites, cumulate clinopyroxenites, are another major group of Martian meteorites and have distinctly different trace element and isotopic characteristics from shergottites. Thus, estimates of oxidation state of nakhlites will give us important insight into the mantle source in general. Several workers have estimated oxidation state of nakhlites by using Fe-Ti oxide equilibrium. However, Fe-Ti oxides may not record the oxidation state of the parent melt of the nakhlite because it is a late-stage mineral. Furthermore, there is no comprehensive study which analyzed all nakhlite samples at the same time. Therefore, in this study (1) we reduced the uncertainty of the estimate using the same electron microprobe and the same standards under the same condition for Fe-Ti oxide in 6 nakhlites and (2) we also performed crystallization experiments to measure partition coefficients of Eu into pyroxene in the nakhlite system in order to estimate fO2 when the pyroxene core formed (i.e. Eu oxybarometer [e.g. 2,6]).

Makishima, J.↗

NASA NTRS1994

Excimer laser produced plasmas in copper wire targets and water droplets

Elastically scattered incident radiation (ESIR) from a copper wire target illuminated by a KrF laser pulse at lambda = 248 nm shows a dinstinct two-peak structure which is dependent on the incident energy. The time required to reach the critical electron density (n(sub c) approximately = 1.8 x 10(exp 22) electrons/cu cm) is estimated at 11 ns based on experimental results. Detailed ESIR characteristics for water have been reported previously by the authors. Initiation of the broadband emission for copper plasma begins at 6.5 +/- 1.45 ns after the arrival of the laser pulse. However, the broadband emission occurs at 11 +/- 0.36 ns for water. For a diatomic substance such as water, the electron energy rapidly dissipates due to dissociation of water molecules, which is absent in a monatomic species such as copper. When the energy falls below the excitation energy of the lowest electron state for water, it becomes a subexcitation electron. Lifetimes of the subexcited electrons to the vibrational states are estimated to be of the order of 10(exp -9) s. In addition, the ionization potential of copper (440-530 nm) is approximately 6 eV, which is about two times smaller than the 13 eV ionization potential reported for water. The higher ionization potential contributes to the longer observed delay time for plasma formation in water. After initiation, a longer time is required for copper plasma to reach its peak value. This time delay in reaching the maximum intensity is attributed to the energy loss during the interband transition in copper.

Song, Kyo-Dong↗

DOE OSTI2024

Climate change-resilient snowpack estimation in the Western United States

Abstract In the 21st century, warmer temperatures and changing atmospheric circulation will likely produce unprecedented changes in Western United States snowfall 1–3 , with impacts on the timing, amount, and spatial patterns of snowpack 4–7 . The ~900 snow pillow stations are indispensable to water resource management by measuring snow-water equivalent (SWE) 8,9 in strategic but fixed locations 10,11 . However, this network may not be impacted by climate change in the same way as the surrounding area 12 and thus fail to accurately represent unmeasured locations; climate change thereby threatens our ability to measure the effects of climate change on snow. In this work, we show that maintaining the current peak SWE estimation skill is nonetheless possible. We find that explicitly including spatial correlations—either from gridded observations or learned by the model—improves skill at predicting distributed snowpack from sparse observations by 184%. Existing artificial intelligence methods can be useful tools to harness the many available sources of snowpack information to estimate snowpack in a nonstationary climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗

NASA NTRS2019

Assessment of TLE-Based Orbit Determination and Prediction for Cubesats

The IceCube satellite is a 3U cubesat which was deployed from the International Space Station Nanoracks in May 2017. IceCube uses onboard Global Positioning System (GPS) measurements for both science data processing and orbit determination; additionally, IceCube uses Two Line Element (TLE) sets provided by USSTRATCOM through Space Track for ground station acquisition. Once acquired, GPS measurements are downlinked and can be verified against nearby TLE states.TLE sets are typically used for cubesat acquisition data, however the accuracy of their propagation has a limited lifespan which can cause issues when using an older TLE to attempt spacecraft acquisition. This presentation details the merits of TLE-based orbit determination and prediction and discusses techniques for improving both. These refinements allow for improved cubesat acquisition as well as calibration and on-orbit checkout of the GPS. Using IceCube data as representative of a typical cubesat, TLE states are simulated as tracking data and processed through an Extended Kalman Filter. This filtered solution provides not only an ongoing estimate of spacecraft drag state parameters, but also improves the definitive orbit determination estimation as well as the propagation accuracy and therefore spacecraft acquisition. Next, using the definitive TLE-based solution, raw GPS measurements are introduced into the filter where the combined measurement types allow the GPS measurements to be subsequently calibrated with respect to an unknown timing bias. These analyses provide a more accurate state estimation and prediction of cubesats during early orbit operations. Additionally, these techniques showcase methods for TLE-based state estimation and GPS calibration when other methods of tracking are unavailable.

Smith, Alexander↗

DOE OSTI2023

Condition monitoring of permanent magnet AC machines for all-electric transportation systems: State of the art

The current state of the art on emerging and efficient techniques for condition monitoring of permanent magnet (PM) alternating-current (AC) machines deployed in electric vehicle (EV) applications is presented. The discussion includes the most common and specific types of faults in PM motors, such as rotor demagnetisation and stator inter-turn faults, respectively. Fault indicators, such as voltage (v s ) and current (i s ) signals and machine signatures based on motor back electromotive force (EMF) (E B ) and magnetic flux (φ), are taken into account as a measuring quantity in diagnosing motor faults. Other signatures, including thermal analysis, acoustic noise, and vibrations, are also illustrated as some of the emerging techniques in estimating the performance of EV motors while under operations. In addition, various fault modelling methods, condition monitoring techniques, and comprehensive approaches applied in diagnosing the effect of machine faults during its incipient stages are illustrated. Since most of the fault diagnostic techniques discussed here include only machine-based quantities as fault indices/indicators, the provided solutions are therefore found to be more reliable and accurate for diagnosing the motor faults. This comprehensive review study is inclusive of the existing fault diagnostic techniques, which are currently employed in industrial and commercial practices, in addition to the new methodologies proposed by the authors. All the given condition monitoring schemes therefore seem significantly vital in estimating the state of health of PM AC machines while under operation in all-electric transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2016

Initial Demonstration of the Real-Time Safety Monitoring Framework for the National Airspace System Using Flight Data

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper we demonstrate the framework's capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the capability of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.

Real-time Safety Monitoring↗

NASA NTRS2021

Southern Idaho Health & Air Quality II: Evaluating Atmospheric Mixing Height Estimations in the Western United States

Wildfires in the western United States have caused immense infrastructure damage and loss of human life in recent years. Wildfire smoke, which travels far from its original source, is also harmful to human health. Mixing height, which acts as a lid and prevents smoke from rising above a certain altitude in the lower troposphere, is a critical input in smoke dispersion and air quality models used by agencies that monitor wildfires. These models, coupled with forecaster expertise, are also used to decide when it is safe to execute a prescribed burn. The DEVELOPID team partnered with the National Weather Service (NWS) Fire Weather Program, Bureau of Land Management (BLM), National Interagency Fire Center (NIFC), and National Park Service (NPS) Fire Management Program Center (FMPC) to help improve reliability and confidence in mixing height estimations, and therefore the burn prescription decision-making process. To that end, the team developed a toolbox for measuring smoke-related aerosol mixing heights using Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask granules. CALIPSO mixing heights and NWS estimations covaried significantly and positively. However, substantial disagreement between the methods stymied the team’s attempts to quantify systematic bias in a meaningful way. The relative error between the methods was especially large at low mixing heights, which suggests that this method of validation may only be suitable at higher altitudes.

Dean Berkowitz↗

NASA NTRS2018

Orion Optical Navigation Progress Toward Exploration: Mission 1

Optical navigation of human spacecraft was proposed on Gemini and implemented successfully on Apollo as a means of autonomously operating the vehicle in the event of lost communication with controllers on Earth. It shares a history with the "method of lunar distances" that was used in the 18th century and gained some notoriety after its use by Captain James Cook during his 1768 Pacific voyage of the HMS Endeavor. The Orion emergency return system utilizing optical navigation has matured in design over the last several years, and is currently undergoing the final implementation and test phase in preparation for Exploration Mission 1 (EM-1) in 2019. The software development is being worked as a Government Furnished Equipment (GFE) project delivered as an application within the Core Flight Software of the Orion camera controller module. The mathematical formulation behind the initial ellipse fit in the image processing is detailed in Christian. The non-linear least squares refinement then follows the technique of Mortari as an estimation process of the planetary limb using the sigmoid function. The Orion optical navigation system uses a body fixed camera, a decision that was driven by mass and mechanism constraints. The general concept of operations involves a 2-hour pass once every 24 hours, with passes specifically placed before all maneuvers to supply accurate navigation information to guidance and targeting. The pass lengths are limited by thermal constraints on the vehicle since the OpNav attitude generally deviates from the thermally stable tail-to-sun attitude maintained during the rest of the orbit coast phase. Calibration is scheduled prior to every pass due to the unknown nature of thermal effects on the lens distortion and the mounting platform deformations between the camera and star trackers. The calibration technique is described in detail by Christian, et al. and simultaneously estimates the Brown-Conrady coefficients and the Star Tracker/Camera interlock angles. Accurate attitude information is provided by the star trackers during each pass. Figure 1 shows the various phases of lunar return navigation when the vehicle is in autonomous operation with lost ground communication. The midcourse maneuvers are placed to control the entry interface conditions to the desired corridor for safe landing. The general form of optical navigation on Orion is where still images of the Moon or Earth are processed to find the apparent angular diameter and centroid in the camera focal plane. This raw data is transformed into range and bearing angle measurements using planetary data and precise star tracker inertial attitude. The measurements are then sent to the main flight computer's Kalman filter to update the onboard state vector. The images are, of course, collected over an arc to converge the state and estimate velocity. The same basic technique was used by Apollo to satisfy loss-of-comm, but Apollo used manual crew sightings with a vehicle-integral sextant instead of autonomously processing optical imagery. The software development is past its Critical Design Review, and is progressing through test and certification for human rating. In support of this, a hardware-in-the-loop test rig was developed in the Johnson Space Center Electro-Optics Lab to exercise the OpNav system prior to integrated testing on the Orion vehicle. Figure 2 shows the rig, which the test team has dubbed OCILOT (Orion Camera In the Loop Optical Testbed). Analysis performed to date shows a delivery that satisfies an allowable entry corridor as shown in Figure 3.

Holt, Greg N.↗

DOE OSTI2024

Evaluating Compliance Inspection Data to Understand Emissions from Marginal Conventional Oil & Gas Wells in New York State

It is estimated that over half (~66%) of active oil and gas wells are marginally productive, producing less than 15 barrels of oil equivalent per day. This presentation showcases an evaluation of New York State compliance inspection reports and discusses the current capabilities of machine learning to identify high-risk MCWs that could be targeted during methane emission reduction efforts and future model deployments. Findings are valuable to the New York Department of Environmental Conservation and other state oil and gas regulatory agencies seeking to prioritize the spending of Methane Emission Reduction Program funds to plug marginal conventional wells with emissions.

compliance inspection data↗

DOE OSTI2025

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗

NASA NTRS1996

Application of Consider Covariance to the Extended Kalman Filter

The extended Kalman filter (EKF) is the basis for many applications of filtering theory to real-time problems where estimates of the state of a dynamical system are to be computed based upon some set of observations. The form of the EKF may vary somewhat from one application to another, but the fundamental principles are typically unchanged among these various applications. As is the case in many filtering applications, models of the dynamical system (differential equations describing the state variables) and models of the relationship between the observations and the state variables are created. These models typically employ a set of constants whose values are established my means of theory or experimental procedure. Since the estimates of the state are formed assuming that the models are perfect, any modeling errors will affect the accuracy of the computed estimates. Note that the modeling errors may be errors of commission (errors in terms included in the model) or omission (errors in terms excluded from the model). Consequently, it becomes imperative when evaluating the performance of real-time filters to evaluate the effect of modeling errors on the estimates of the state.

Lundberg, John B.↗

NASA NTRSDate not supplied

Satellite Based Precipitation Estimation in Orographic Regions within the Southwestern United States

Predicting precipitation-induced landslides requires accurate estimation of orographic precipitation. Many research studies have been done to estimate orographic precipitation using measurement/estimation methods that include rain gauges, ground-based radar, satellite-based estimates, and modeling. Each method has strengths and weaknesses, but none have been able to fully resolve orographic precipitation. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Mission (IMERG) early run product provides precipitation estimates at a 0.1° spatial resolution, at half-hour time scales, with only a 4-hour latency. This product is currently used for the global landslide hazard assessment, but there are known issues in mountainous terrain that the IMERG algorithm has not been able to fully resolve; and the sparse gauge density in these regions makes it even more difficult. In this study, precipitation events were identified using high temporal resolution (5-15 minute) precipitation observations from rain gauges in mountainous terrain in the southwestern United States. The brightness temperature from several infrared (IR) bands from the Geostationary Operational Environmental Satellite (GOES) 16 satellite were used to estimate precipitation with a k-nearest neighbor machine learning model. Compared to IMERG, the IR estimates from GOES-16 performed better at predicting gauge-identified precipitation events. Additionally, the IR-only based estimates were able to estimate precipitation when IMERG failed to detect precipitation, false negative events. While additional analysis is needed, results indicate the need for better integration of IR observations for more accurate precipitation estimation in mountainous regions.

Jessica Sutton↗

DOE OSTI2020

A Privacy Preserving Model-Free Optimization and Control Framework for Demand Response from Residential Thermal Loads

We consider the problem of optimizing the cost of procuring electricity for a large collection of homes managed by a load serving entity, by pre-cooling or pre-heating the thermal inertial loads in the homes to avoid procuring power during periods of peak electricity pricing. We would like to accomplish this objective in a completely privacy-preserving and model-free manner, that is, without direct access to the state variables (temperatures or power consumption) or the dynamical models (thermal characteristics) of individual homes, while guaranteeing personal comfort constraints of the consumers. We propose a two-stage optimization and control framework to address this problem. In the first stage, we use a long short-term memory (LSTM) network to predict hourly electricity prices, based on historical pricing data and weather forecasts. Given the hourly price forecast and thermal models of the homes, the problem of designing an optimal power consumption trajectory that minimizes the total electricity procurement cost for the collection of thermal loads can be formulated as a large-scale integer program (with millions of variables) due to the on-off cyclical dynamics of such loads. We provide a simple heuristic relaxation to make this large-scale optimization problem model-free and computationally tractable. In the second stage, we translate the results of this optimization problem into distributed open-loop control laws that can be implemented at individual homes without measuring or estimating their state variables, while simultaneously ensuring consumer comfort constraints. We demonstrate the performance of this approach on a large-scale test case comprising of 500 homes in the Houston area and benchmark its performance against a direct model-based optimization and control solution.

Sivaranjani, S.↗

DOE OSTI2020

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1997

An Investigation of the Migration of Africanized Honey Bees into the Southern United States

It is estimated that Apis mellifera scutellata, a honey bee subspecies from Africa, now extends over a 20 million square kilometer range that includes much of South America and practically all of Central America, and recently has been introduced to the southern United States. African honeybees were introduced into Brazil in 1956 by a Brazilian geneticist, Mr. Warwick Kerr. At the insistence of the Brazilian Ministry of Agriculture, in 1957, 26 colonies were accidentally released in a eucalyptus forest outside S5o Paulo. The swelling front of the bees was recorded as traveling between 80 and 500 kilometers a year. David Roubik, one of the original killer bee team members estimated that there were one trillion individual Africanized/African honey bees in Latin America. An estimate that is thought to be conservative.

Navarro, Hector↗

DOE OSTI2020

Variational Quantum Fidelity Estimation

Computing quantum state fidelity will be important to verify and characterize states prepared on a quantum computer. In this work, we propose novel lower and upper bounds for the fidelity F ( ρ , σ ) based on the ``truncated fidelity'' F ( ρ m , σ ) , which is evaluated for a state ρ m obtained by projecting ρ onto its m -largest eigenvalues. Our bounds can be refined, i.e., they tighten monotonically with m . To compute our bounds, we introduce a hybrid quantum-classical algorithm, called Variational Quantum Fidelity Estimation, that involves three steps: (1) variationally diagonalize ρ , (2) compute matrix elements of σ in the eigenbasis of ρ , and (3) combine these matrix elements to compute our bounds. Our algorithm is aimed at the case where σ is arbitrary and ρ is low rank, which we call low-rank fidelity estimation, and we prove that no classical algorithm can efficiently solve this problem under reasonable assumptions. Finally, we demonstrate that our bounds can detect quantum phase transitions and are often tighter than previously known computable bounds for realistic situations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Memristive Behavior Enabled by Amorphous–Crystalline 2D Oxide Heterostructure

The emergence of memristive behavior in amorphous–crystalline 2D oxide heterostructures, which are synthesized by atomic layer deposition (ALD) of a few-nanometer amorphous Al 2 O 3 layers onto atomically thin single-crystalline ZnO nanosheets, is demonstrated. The conduction mechanism is identified based on classic oxygen vacancy conductive channels. ZnO nanosheets provide a 2D host for oxygen vacancies, while the amorphous Al 2 O 3 facilitates the generation and stabilization of the oxygen vacancies. The conduction mechanism in the high-resistance state follows Poole–Frenkel emission, and in the the low-resistance state is fitted by the Mott–Gurney law. From the slope of the fitting curve, the mobility in the low-resistance state is estimated to be ≈2400 cm 2 V -1 s -1 , which is the highest value reported in semiconductor oxides. When annealed at high temperature to eliminate oxygen vacancies, Al is doped into the ZnO nanosheet, and the memristive behavior disappears, further confirming the oxygen vacancies as being responsible for the memristive behavior. The 2D heterointerface offers opportunities for new design of high-performance memristor devices.

2D heterostructures↗