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DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1999

Tropical Intraseasonal Air-Sea Exchanges during the 1997 Pacific Warming

The Madden Julian Oscillations (MJO) and associated westerly wind (WW) events account for much of the tropical intraseasonal variability (TISV). The TISV has been suggested as an important stochastic forcing that may be one of the underlying causes for the observed irregularities of the El Nino-Southern Oscillation (ENSO). Recent observational studies and theories of interannual to interdecadal-scale variability suggest that ENSO may arise from different mechanisms depending on the basic states. The Pacific warming event of 1997, being associated with a period of strong MJO and WW events, serves as a natural experiment for studying the possible role of TISV in triggering an ENSO event. We have performed a combined statistical and composite analysis of surface WW events based on the assimilated surface wind and sea level pressure for the period of 1980-1993, the SSM/I wind for the period of 1988-1997, and OLR. Results indicates that extratropical forcing contribute significantly to the evolution of MJO and establishment of WW events over the Pacific warm pool. Following the major WW events, there appeared an eastward extension of equatorial warm SST anomalies from the western Pacific warm pool. Such tropical-extratropical interaction is particularly clear in the winter of 96-97 that leads to the recent warming event in 1997/98. From the above discussion, our current study on this subject is based on the hypothesis that 1) there is an enhanced air-sea interaction associated with TISV and the northerly surges from the extratropics in the initial phase of the 97/98 warming event, and 2) the relevant mechanisms are functions of the basic state of the coupled system (in terms of SST distribution and atmospheric mean circulation) that varies at the interannual and interdecadal time scale. We are analyzing the space-time structure of the northerly surges, their association with air-sea fluxes and upper ocean responses during the period of September 1996 to June 1997. The estimate of daily values of latent heat fluxes is based on NSCAT wind, SST, and ECMWF surface air temperature and SSM/I water vapor data (Chou et al. 1997). To understand the relevant mechanisms, we will analyze the origin of the northerly surges in terms of atmospheric instability associated with the extratropical circulation, and the mutual influence between the tropical heating and the extratropical circulation. In this meeting, we will report the analysis addressing the first part of the above hypothesis.

Sui, C.-H.↗

NASA NTRS2018

Fuzzy Modeling and Parallel Distributed Compensation for Aircraft Flight Control from Simulated Flight Data

A method is described that combines fuzzy system identification techniques with Parallel Distributed Compensation (PDC) to develop nonlinear control methods for aircraft using minimal a priori knowledge, as part of NASA’s Learn-to-Fly initiative. A fuzzy model was generated with simulated flight data, and consisted of a weighted average of multiple linear time invariant state-space cells having parameters estimated using the equation-error approach and a least-squares estimator. A compensator was designed for each subsystem using Linear Matrix Inequalities (LMI) to guarantee closed-loop stability and performance requirements. This approach is demonstrated using simulated flight data to automatically develop a fuzzy model and design control laws for a simplified longitudinal approximation of the F-16 nonlinear flight dynamics simulation. Results include a comparison of flight data with the estimated fuzzy models and simulations that illustrate the feasibility and utility of the combined fuzzy modeling and control approach.

Weinstein, Rose↗

NASA NTRS2005

The Space Technology 7 disturbance reduction system

The NASA New Millennium Program Space Technology 7 (ST7) project was selected for the purpose of flight validation of technology for minimizing disturbances on spacecraft and freely-floating test masses. Measurements of the motion of test masses are used for determining the gravitational field in its location, providing information on the nearby mass distribution or looking gravitational waves generated by distant star systems. Any unmeasured forces acting on the test mass cause a deviation from a purely gravitational trajectory and so limit the accuracy of the gravity field estimation. The ST7 project will validate the capability to eliminate unwanted forces to a level 3000 times lower than the current state of the art. The ST7 design is based on the concept of a freely-floating test mass surrounded by a spacecraft which shields the test mass from unwanted disturbances. since some disturbance of the test mass can be caused by motion of the spacecraft itself with respect to the test mass, the disturbance reduction system includes the capability of precisely measuring the position of the spacecraft with respect to the test mass and control of the spacecraft position to within 1/100 of a wavelength of light.. The overall system performance of the ST7 project will be infused into space observatories for gravitational waves such as the Laser Interferometer Space Antenna project, and into future mission for mapping the time-variable Earth gravity field. The ST7 precision control capability also has applications for separated-spacecraft interferometric imaging projects. ST7 will consist of an instrument package and a set of microthrusters, which will be attached to the European Space Agency’s LISA Pathfinder spacecraft with launch scheduled for 2008.

Folkner, W. M.↗

DOE OSTI2024

Advanced Power Electronics and Electric Machines

The advanced power electronics and electric machines (APEEM) research group at the National Renewable Energy Laboratory (NREL) has developed world-class experimental and modeling capabilities for designing and evaluating efficient and reliable power electronics and electric machines thermal management systems. They also design, fabricate and characterize advanced power electronics packaging, and are developing state-of-health monitoring techniques. These researchers deliver safe, reliable, high performing, power-dense components that allow seamless integration between renewable energy sources, electric transportation, and the grid, helping to make widespread electric vehicle (EV) adoption and greenhouse gas emissions reduction more feasible. This document outlines the group's major capabilities in the areas of power electronics; module development and characterization; thermal modeling and management; thermomechanical reliability analysis of devices, modules, inverters/converters, and electric machines; physics-of-failure-based reliability analysis; and microelectronics. It also overviews the group's state-of-the-art equipment for fluid-based thermal management; thermal measurement & characterization; thermomechanical reliability analysis; micro- and power electronics measurement & characterization; and prototype fabrication, as well as the group's world-class modeling and simulation capabilities.

advanced gate drivers↗

DOE OSTI2022

Economics of Crop Rotations With and Without Carinata for Sustainable Aviation Fuel Production in the SE United States

In 2019, the aviation sector in the United States emitted 255 million metric tons of carbon dioxide (CO 2 ) emissions, i.e., about five percent of the total domestic CO 2 emissions from the energy sector. The sustainable aviation fuel (SAF) derived from carinata (Brassica carinata) could reduce CO 2 emissions of the aviation sector in the United States. Therefore, it is important to estimate changes in farm economics with and without carinata for ascertaining its production feasibility. In this context, the current study first assesses a combination of 12 popular rotations of corn, cotton, peanut, and soybean with winter crops of winter wheat and carinata in South Georgia over 4 years. Then, the net present values (NPVs) of 292 feasible cropping systems over 4 years are calculated. Finally, this study develops a risk model for ascertaining the probability distributions of NPVs for selected cropping systems subject to uncertainties related to prices and yields of summer and winter crops. Carinata in the corn-corn-soybean rotation has the highest NPV ($\$$2,996/ha). The least risky rotation is cotton-cotton-peanut, with a 58.9% probability of a positive NPV. Carinata can decrease the risk level of crop rotations by 8.1%, only if a contract price of $\$$440.9/t is offered. Therefore, a risk averse, risk neutral, or risk acceptant farmer can potentially include carinata in the rotation. Overall, carinata would increase the profitability of farm operations and decrease risk in the SE United States, and therefore, a high likelihood exists, that farmers would adopt it for meeting the growing demand for SAF in the United States.

09 BIOMASS FUELS↗

NASA NTRS2009

GPS Navigation for the Magnetospheric Multi-Scale Mission

In 2014. NASA is scheduled to launch the Magnetospheric Multiscale Mission (MMS), a four-satellite formation designed to monitor fluctuations in the Earth's magnetosphere. This mission has two planned phases with different orbits (1? x 12Re and 1.2 x 25Re) to allow for varying science regions of interest. To minimize ground resources and to mitigate the probability of collisions between formation members, an on-board orbit determination system consisting of a Global Positioning System (GPS) receiver and crosslink transceiver was desired. Candidate sensors would be required to acquire GPS signals both below and above the constellation while spinning at three revolutions-per-minute (RPM) and exchanging state and science information among the constellation. The Intersatellite Ranging and Alarm System (IRAS), developed by Goddard Space Flight Center (GSFC) was selected to meet this challenge. IRAS leverages the eight years of development GSFC has invested in the Navigator GPS receiver and its spacecraft communication expertise, culminating in a sensor capable of absolute and relative navigation as well as intersatellite communication. The Navigator is a state-of-the-art receiver designed to acquire and track weak GPS signals down to -147dBm. This innovation allows the receiver to track both the main lobe and the much weaker side lobe signals. The Navigator's four antenna inputs and 24 tracking channels, together with customized hardware and software, allow it to seamlessly maintain visibility while rotating. Additionally, an extended Kalman filter provides autonomous, near real-time, absolute state and time estimates. The Navigator made its maiden voyage on the Space Shuttle during the Hubble Servicing Mission, and is scheduled to fly on MMS as well as the Global Precipitation Measurement Mission (GPM). Additionally, Navigator's acquisition engine will be featured in the receiver being developed for the Orion vehicle. The crosslink transceiver is a 1/4 Watt transmitter utilizing a TDMA schedule to distribute a science quality message to all constellation members every ten seconds. Additionally the system generates one-way range measurements between formation members which is used as input to the Kalman filter. In preparation for the MMS Preliminary Design Review (PDR), the Navigator was required to pass a series of Technology Readiness Level (TRL) tests to earn the necessary TRL-6 classification. The TRL-6 level is achieved by demonstrating a prototype unit in a relevant end-to-end environment. The IRAS unit was able to meet all requirements during the testing phase, and has thus been TRL-6 qualified

Bamford, William↗

NASA NTRS2004

Masses and Luminosities of X-Ray Binaries

Using SIM, we will perform narrow-angle observations of several X-ray binaries to determine their orbits, and we will observe about 50 X-ray binary systems in wide-angle mode to measure their distances and proper motions. Sources with mass estimates for the compact component of greater than 3 solar masses are generally called black hole candidates since this mass is above the theoretical neutron star limit. Narrow-angle observations of these sources provide a direct test of the dynamical mass estimates on which the black hole evidence is based. Better measurements of the black hole masses will provide constraints on possible evolutionary paths that lead to black hole formation. When combined with X-ray data, mass measurements may provide additional constraints on the black hole spin. Precise mass determinations of neutron star systems can address the question of whether neutron stars can be significantly more massive than 1.4 solar masses, which would eliminate soft models of the neutron star equations of state. The wide-angle observations will probe the Galactic distribution of X-ray binaries through parallaxes and proper motions. They will also eliminate the uncertainties in the luminosities of individual sources, which is currently up to a full order of magnitude. This will enable more detailed comparisons of X-ray observations to physical models such as advection-dominated accretion flows (ADAFs). We intend to carry out the following measurements: 1) Determine the orbits of two black hole candidates to measure the black hole masses; 2) Obtain precise mass measurements for two neutron star systems to constrain neutron star equations of state; 3) Determine the distances and thus luminosities of selected representatives of various classes of X-ray binaries (black hole candidates, neutron stars, jet sources); 4) In the process of distance determination, proper motions will also be measured, from which the age of the population can be estimated.

Quirrenbach, Andreas↗

NASA NTRS2017

Version 3 of the SMAP Level 4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) Level 4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture as well as related land surface states and fluxes from 31 March 2015 to present with a latency of 2.5 days. The ensemble-based L4_SM algorithm is a variant of the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system and ingests SMAP L-band (1.4 GHz) Level 1 brightness temperature observations into the Catchment land surface model. The soil moisture analysis is non-local (spatially distributed), performs downscaling from the 36-km resolution of the observations to that of the model, and respects the relative uncertainties of the modeled and observed brightness temperatures. Prior to assimilation, a climatological rescaling is applied to the assimilated brightness temperatures using a 6 year record of SMOS observations. A new feature in Version 3 of the L4_SM data product is the use of 2 years of SMAP observations for rescaling where SMOS observations are not available because of radio frequency interference, which expands the impact of SMAP observations on the L4_SM estimates into large regions of northern Africa and Asia. This presentation investigates the performance and data assimilation diagnostics of the Version 3 L4_SM data product. The L4_SM soil moisture estimates meet the 0.04 m3m3 (unbiased) RMSE requirement. We further demonstrate that there is little bias in the soil moisture analysis. Finally, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system.

GEOS-↗

DOE OSTI2020

Inferring the Dynamics of the State Evolution During Quantum Annealing

To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer, which then returns a user-specified number of low-energy solutions. It would be useful to know what happens in the quantum processor during the anneal process so that one could design better algorithms or suggest improvements to the hardware. However, existing quantum annealers are not able to directly extract such information from the processor. Hence, in this work we propose to use advanced features of D-Wave 2000Q to indirectly infer information about the dynamics of the state evolution during the anneal process. Specifically, D-Wave 2000Q allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the anneal. Furthermore, using this feature, we design a set of modified anneal schedules whose outputs can be used to generate information about the states of the system at user-defined time points during a standard anneal. With this process, called "slicing", we obtain approximate distributions of lowest-energy anneal solutions as the anneal time evolves. We use our technique to obtain a variety of insights into the annealer, such as the state evolution during annealing, when individual bits in an evolving solution flip during the anneal process and when they stabilize, and we introduce a technique to estimate the freeze-out point of both the system as well as of individual qubits.

42 ENGINEERING↗

NASA NTRS1976

A distribution benefits model for improved information on worldwide crop production. Volume 2: Application to various crops

ECON's distribution benefits model has been applied to worldwide distribution of corn, rye, oats, barley, soybeans, and sugar, and to domestic distribution of potatoes. The results indicate that a LANDSAT system with thematic mapper might produce benefits to the United States of about $119 million per year, due to more efficient distribution of these commodities. The benefits to the rest of the world have also been calculated, with a breakdown between trade benefits and those associated with internal use patterns. By far the greatest part of the estimated benefits are assigned to corn, with smaller benefits assigned to soybeans and the small grains (rye, oats, and barley).

Andrews, J.↗

NASA NTRS1990

A study of fracture mechanisms in ATD roller bearing

The purpose was to investigate how microstructures, especially anisotropy, affects internal stresses and the overall mechanical response of bearings. Samples with the stress axis along the aligned carbide direction possessed high modulus values compared to those with their axis perpendicular to the carbide stringers. The difference in the modulus was found to be more than a factor of two. A series of experiments was conducted on rolled samples to further investigate this effect; the two sets of results were consistent with each other. The degree of anisotropy of the microstructure in terms of the carbide and matrix orientations was determined using x-ray diffraction. The stress state determination was conducted using neutron diffraction. It was found that there was little variation in the distribution of the internal stresses amongst different samples, indicating that small changes in the processing and geometrical conditions did not result in significant variations in the internal stress. A nominal tensile hoop stress of 39 ksi was obtained for the inner raceway. Furthermore, during the course of the investigation, it was apparent that there was a need to determine the stress state induced by the shrink fitting process. Therefore, a series of experiments was conducted using strain gages to identify the stress distribution in a shrink fitting process in three different types of geometries. Correlations were obtained to estimate the highest stress values in the outer and inner groove geometry. A finite element program based on the ANSYS system was developed to compute the stress distribution in the inner raceway geometry. This analysis indicates that the highest tensile stress in the system occurs at the ID of the ring with a stress value of over 5 times that of the applied radial stress. Results from all these facets were correlated with one another. It appears that the material does not fail as a result of any one single factor, but results from a combination of the various factors investigated.

Zee, Ralph H.↗

NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗

DOE OSTI2024

Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing a enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlight potential aspects of the model for real-life applications.

Mathieu, Reynold↗

NASA NTRS1990

Delay compensation in integrated communication and control systems. I - Conceptual development and analysis

A procedure for compensating for the effects of distributed network-induced delays in integrated communication and control systems (ICCS) is proposed. The problem of analyzing systems with time-varying and possibly stochastic delays could be circumvented by use of a deterministic observer which is designed to perform under certain restrictive but realistic assumptions. The proposed delay-compensation algorithm is based on a deterministic state estimator and a linear state-variable-feedback control law. The deterministic observer can be replaced by a stochastic observer without any structural modifications of the delay compensation algorithm. However, if a feedforward-feedback control law is chosen instead of the state-variable feedback control law, the observer must be modified as a conventional nondelayed system would be. Under these circumstances, the delay compensation algorithm would be accordingly changed. The separation principle of the classical Luenberger observer holds true for the proposed delay compensator. The algorithm is suitable for ICCS in advanced aircraft, spacecraft, manufacturing automation, and chemical process applications.

Luck, Rogelio↗

DOE OSTI2024

Multi-State Transportation Electrification Impact Study: Preparing the Grid for Light-, Medium-, and Heavy-Duty Electric Vehicles

Recent U.S. Environmental Protection Agency (EPA) notices of proposed rulemakings for GHG emissions standards for light-, medium-, and heavy-duty on-road vehicles would accelerate ongoing advancements already happening in the industry because of private investment, consumer demand, state-level policies, and federal incentives. As the EPA finalizes these regulations, questions persist regarding the cost of the requisite charging infrastructure and associated upgrades to the nation's electric grid. With support from the U.S. Department of Energy, U.S. Joint Office of Energy and Transportation, and the EPA, a multidisciplinary team conducted a Multi-State Transportation Electrification Impact Study that quantitatively assesses the incremental investment necessary to enable the levels of vehicle electrification expected to be induced by pending EPA regulations and to estimate the potential value of deferred investments in electric distribution infrastructure stemming from proactive vehicle-grid integration planning and deployment. This study finds the simulated incremental capital cost of charging infrastructure (including grid upgrades) to be at least 2.5 times smaller than the lifetime net benefits of vehicle electrification (including fuel savings but excluding the value of avoided emissions). Additionally, the incremental distribution grid upgrade cost of the EPA Action-Unmanaged scenario was found to be approximately 3% of existing utility distribution system investments (on an annual basis). Finally, the potential for managed charging to defer distribution grid upgrades was found to be significant with costs found to decrease from $2.3 billion to an incremental cost of $1 billion across five states in the Action-Managed scenario (relative to the No Action-Unmanaged scenario).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Transportation Electrification Impact Study (TEIS)

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

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↗