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Resource Estimates for Lindbladian-Based Gibbs State Preparation on Fault-Tolerant Quantum Computers
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Population and growth estimates of urban areas in the state of Sao Paulo utilizing LANDSAT images
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The 2Sigma(+)-2Pi separation in KO
The 2Sigma(+)-2Pi separation in KO is investigated using large basis sets and high levels of correlation treatment. Relativistic effects are included at the Dirac-Fock level and reduce the separation only slightly. The basis set superposition error is considered in detail. On the basis of these calculations, our best estimate places the 2Pi3/2 state about 200/cm above the ground 2Sigma(+) state, in agreement with our previous estimate.
Space Shuttle propulsion parameter estimation using optimal estimation techniques, volume 1
The mathematical developments and their computer program implementation for the Space Shuttle propulsion parameter estimation project are summarized. The estimation approach chosen is the extended Kalman filtering with a modified Bryson-Frazier smoother. Its use here is motivated by the objective of obtaining better estimates than those available from filtering and to eliminate the lag associated with filtering. The estimation technique uses as the dynamical process the six degree equations-of-motion resulting in twelve state vector elements. In addition to these are mass and solid propellant burn depth as the ""system'' state elements. The ""parameter'' state elements can include aerodynamic coefficient, inertia, center-of-gravity, atmospheric wind, etc. deviations from referenced values. Propulsion parameter state elements have been included not as options just discussed but as the main parameter states to be estimated. The mathematical developments were completed for all these parameters. Since the systems dynamics and measurement processes are non-linear functions of the states, the mathematical developments are taken up almost entirely by the linearization of these equations as required by the estimation algorithms.
A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines
Monitoring a wind turbine requires intensive instrumentation, which would be too cost-prohibitive to deploy to an entire wind plant. This work presents a technique that uses readily available measurements to estimate signals that would otherwise require additional instrumentation. This study presents a digital twin concept with a focus on estimating wind speed, thrust, torque, tower-top position, and loads in the tower using supervisory control and data acquisition (SCADA) measurements. The model combines a linear state-space model obtained using OpenFAST linearizations, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The measurements are: top acceleration, generator torque, pitch, and rotational speed. The article extends previous work that derived the linear state-space model using a different method. The new implementation, based on OpenFAST linearization capability, allows for a systematic extension of the method to more states, inputs, outputs, and to the offshore environment. Results from the two methods are compared, and the validation is made with additional measurements using the GE 1.5-MW turbine located at the National Renewable Energy Laboratory test site. Real-time damage equivalent loads of the tower bottom moment are estimated with an average accuracy of approximately 10%. Overall, the results from this proof of concept are encouraging, and further application of the model will be considered.
Estimability and regulability of linear systems
A linear state-space system will be said to be estimable if in estimating its state from its output the posterior error covariance matrix is strictly smaller than the prior covariance matrix. It will be said to be regulable if the quadratic cost of state feedback control is strictly smaller than the cost when no feedback is used. These properties, which are shown to be dual, are different from the well known observability and controllability properties of linear systems. Necessary and sufficient conditions for estimability and regulability are derived for time variant and time invariant systems, in discrete and continuous time.
Estimability and regulability of linear systems
A linear state-space system will be said to be estimable if in estimating its state from its output the posterior error covariance matrix is strictly smaller than the prior covariance matrix. It will be said to be regulable if the quadratic cost of state feedback control is strictly smaller than the cost when no feedback is used. These properties, which are are shown to be dual, are different from the well known observability and controllability properties of linear systems. Necessary and sufficient conditions for estimability and regulability are derived for time variant and time invariant systems, in discrete and continuous time.
The Role of Data Assimilation in the Study of Regional and Global Variability of the Hydrological Cycle
In the coming years, researchers will have at their disposal a host of new observations from advanced space-based sensors (e.g. the Tropical Rainfall Measuring Mission, EOS Terra and PM missions) providing, among other things, a more complete and accurate description of various components of the Earth's hydrological cycle. Also, increasingly more sophisticated and comprehensive geophysical models will provide researchers better tools for simulating the hydrological cycle, and for carrying out mechanistic studies of the role of moist processes in the climate system. In addition, new data sets generated with global four-dimensional data assimilation (4DDA) systems will provide comprehensive and complete information on both the state and forcing of the climate system. Ideally, the 4DDA systems optimally incorporate all relevant information from the observations together with a first guess from a state-of-the-art geophysical model to produce a "best" estimate of the climate state. Furthermore, to the extent that the assimilating models are realistic and are constrained by the observations, they should provide reliable estimates of the associated physical processes or climate forcing fields. While operational weather centers now have a considerable history of providing reliable estimates of the basic atmospheric state variables, the associated processes or diagnostic fields (which are less well constrained by the observations and sensitive to errors in the model's physical parameterizations) are still considered experimental and of uncertain quality. In this study we will examine the current generation of reanalysis products to assess the capabilities of 4DDA systems to represent components of the hydrological cycle. The focus is on the role of the model in providing consistent estimates of moist processes. We will also assess whether current observations provide sufficient constraints on these model- generated fields.
A unified approach to modelling the charge state of monatomic hydrogen and other defects in crystalline silicon
There are a number of existing models for estimating the charge states of defects in silicon. In order of increasing complexity, these are (a) the Fermi-Dirac distribution, (b) the Shockley-Last model, (c) the Shockley-Read-Hall model, and (d) the Sah-Shockley model. In this work, we demonstrate their consistency with the general occupancy ratio α, and show that this parameter can be universally applied to predict the charge states of both monovalent and multivalent deep levels, under either thermal equilibrium or steady-state conditions with carrier injection. The capture cross section ratio is shown to play an important role in determining the charge state under non-equilibrium conditions. The application of the general occupancy ratio is compared with the quasi-Fermi levels, which are sometimes used to predict the charge states in the literature, and the conditions where the latter can be a good approximation are identified. The general approach is then applied to the prediction of the temperature- and injection level-dependent charge states for the technologically important case of multivalent monatomic hydrogen, and several other key monovalent deep levels including Fe, Cr, and the boron-oxygen complex in silicon solar cells. For the case of hydrogen, we adapt the model of Herring et al., which describes the charge states of hydrogen in thermal equilibrium, and generalize it for non-equilibrium conditions via the inclusion of the general occupancy ratio, while retaining the pre-factors which make the model more complete. Based on these results, the impact of temperature and injection on the hydrogenation of the key monovalent defects, and other pairing reactions, are discussed, demonstrating that the presented model provides a rigorous methodology for understanding the impact of charge states.
Context and future directions for integrating forest carbon into sub-national climate mitigation planning in the RGGI region of the U.S.
International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group, we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative region of the Eastern U.S. For each state we reviewed the (a) policy framework for climate mitigation, (b) GHG reduction goals, (c) inclusion of forest activities in the state’s climate action plan, (d) existing science used to quantify forest carbon estimates, and (e) stated needs for forest carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.
A study of the effects of state transition matrix approximations
The effects of using an approximate state transition matrix in orbit estimation are investigated. The approximate state transition matrix results when higher order geopotential terms in the equations of motion are ignored in the formation of the variational equations. Two methods of orbit estimation are considered: the differential correction procedure (DC) and the extended Kalman filter (EKF). The system used for the study is the Research & Development version of the Goddard Trajectory Determination System. The effects of the approximation are analyzed on a number of orbits. These orbits of various inclinations and semimajor axes. Other parameters studied include geopotential models and DC arc length.
Isolating Added Mass Load Components of CPAS Main Clusters
The current simulation for the Capsule Parachute Assembly System (CPAS) lacks fidelity in representing added mass for the 116 ft Do ringsail Main parachute. The availability of 3-D models of inflating Main canopies allowed for better estimation the enclosed air volume as a function of time. This was combined with trajectory state information to estimate the components making up measured axial loads. A proof-of-concept for an alternate simulation algorithm was developed based on enclosed volume as the primary independent variable rather than drag area growth. Databases of volume growth and parachute drag area vs. volume were developed for several flight tests. Other state information was read directly from test data, rather than numerically propagated. The resulting simulated peak loads were close in timing and magnitude to the measured loads data. However, results are very sensitive to data curve fitting and may not be suitable for Monte Carlo simulations. It was assumed that apparent mass was either negligible or a small fraction of enclosed mass, with little difference in results.
Subsurface Sources of CO 2 in the Contiguous United States. Volume 1: Discovered Reservoirs
Twenty-one CO 2 fields in the contiguous states contain an estimated 311 Tcf of CO 2 gas-initially-in-place (GIIP). Of that, 168 Tcf (54 percent) is estimated to be accessible and technically recoverable. The estimated economically recoverable resource (ERR) is 96.4 Tcf, based on a CO 2 price of 1.06 $/mcf ($20/tonne) at the field gate. Cumulative production to date is 18.9 Tcf, leaving 77.5 Tcf remaining or net ERR. The Big Piney-LaBarge field in Wyoming contains an estimated net ERR of 52 Tcf, 67 percent of the total for the United States. The remaining ERR in reservoirs that feed into the Permian Basin and Gulf Coast is on the order of 10-20 years of supply.
Identification of nonlinear system parameters in joints using the force-state mapping technique
A procedure is presented for identifying the potentially strong nonlinear properties of structural members, such as joints, by expressing the force transmitted by the member as a function of its mechanical state. By explicitly including position and rate dependent effects, the surface of transmitted force versus state, the force-state map, has distinct, unique, superposable features for common structural nonlinearities, even those which appear to indicate hysteresis on a force-stroke presentation. An analysis is performed on the influence of true memory effects, transient response, and uncertainty in the measurements and system mass on the precision of the procedure. The successful identification of simulated data verifies the accuracy of the identification algorithm. Tests are then conducted on three actual joint models, with incomplete state measurements typical of an actual testing environment. The ability of the procedure to estimate the complete state vector and to analyze and reconstruct the measured nonlinear characteristics is demonstrated.
Agricultural Industry Advanced Vehicle Technology: Benchmark Study for Reduction in Petroleum Use
Diesel use on farms in the United States has remained relatively constant since 1985, decreasing slightly in 2009, which may be attributed to price increases and the economic recession. During this time, the United States’ harvested area also has remained relatively constant at roughly 300 million acres. In 2010, farm diesel use was 5.4% of the total United States diesel use. Crops accounting for an estimated 65% of United States farm diesel use include corn, soybean, wheat, hay, and alfalfa, respectively, based on harvested crop area and a recent analysis of estimated fuel use by crop. Diesel use in these cropping systems primarily is from tillage, harvest, and various other operations (e.g., planting and spraying) (Figure 3). Diesel efficiency is markedly variable due to machinery types, conditions of operation (e.g., soil type and moisture), and operator variability. Farm diesel use per acre has slightly decreased in the last two decades and diesel is now estimated to be less than 5% of farm costs per acre. This report will explore current trends in increasing diesel efficiency in the farm sector. The report combines a survey of industry representatives, a review of literature, and data analysis to identify nascent technologies for increasing diesel efficiency.
Minimax Quantum Tomography: Estimators and Relative Entropy Bounds
A minimax estimator has the minimum possible error (“risk”) in the worst case. Here we construct the first minimax estimators for quantum state tomography with relative entropy risk. The minimax risk of nonadaptive tomography scales as O (1/$\sqrt{N}$ ) —in contrast to that of classical probability estimation, which is O (1/N) —where N is the number of copies of the quantum state used. We trace this deficiency to sampling mismatch: future observations that determine risk may come from a different sample space than the past data that determine the estimate. Lastly, this makes minimax estimators very biased, and we propose a computationally tractable alternative with similar behavior in the worst case, but superior accuracy on most states.