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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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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Precomputing Process Noise Covariance for Onboard Sequential Filters

Process noise is often used in estimation filters to account for unmodeled and mismodeled accelerations in the dynamics. The process noise covariance acts to inflate the state covariance over propagation intervals, increasing the uncertainty in the state. In scenarios where the acceleration errors change significantly over time, the standard process noise covariance approach can fail to provide effective representation of the state and its uncertainty. Consider covariance analysis techniques provide a method to precompute a process noise covariance profile along a reference trajectory using known model parameter uncertainties. The process noise covariance profile allows significantly improved state estimation and uncertainty representation over the traditional formulation. As a result, estimation performance on par with the consider filter is achieved for trajectories near the reference trajectory without the additional computational cost of the consider filter. The new formulation also has the potential to significantly reduce the trial-and-error tuning currently required of navigation analysts. A linear estimation problem as described in several previous consider covariance analysis studies is used to demonstrate the effectiveness of the precomputed process noise covariance, as well as a nonlinear descent scenario at the asteroid Bennu with optical navigation.

onboard↗
DOE OSTI2021

Attack-Resilient Weighted $\ell_{1}$ Observer with Prior Pruning

Security related questions for Cyber Physical Systems (CPS) have attracted much research attention in searching for novel methods for attack-resilient control and/or estimation. Specifically, false data injection attacks (FDIAs) have been shown to be capable of bypassing bad data detection (BDD), while arbitrarily compromising the integrity of state estimators and robust controller even with very sparse measurements corruption. Moreover, based on the inherent sparsity of pragmatic attack signals, ℓ1 -minimization scheme has been used extensively to improve the design of attack-resilient estimators. For this, the theoretical maximum for the percentage of compromised nodes that can be accommodated has been shown to be 50%. In order to guarantee correct state recoveries for larger percentage of attacked nodes, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through some data-driven machine learning process. Existing results have shown strong positive correlation between the tolerated attack percentages and the precision of the prior information. In this paper, we present a pruning method to improve the precision of the prior information, given corresponding stochastic uncertainty characteristics of the underlying machine learning model. Then a weighted ℓ1 -minimization is proposed based on the pruned prior. The theoretical and simulation results show that the pruning method significantly improves the observer performance for much larger attack percentages, even when moderately accurate machine learning model used.

Resilient observer, Cyber-physical systems, prunin↗
DOE OSTI2018

The number of past and future regenerations of iron in the oceanand its intrinsic fertilization efficiency

Iron fertilization is explored by tracking dissolved iron (DFe) through its life cycle from injection by aeolian, sedimentary, and hydrothermal sources (birth) to burial in the sediments (death). We develop new diagnostic equations that count iron and phosphate regenerations with each passage through the biological pump and partition the ocean's DFe concentration according to the number of its past or future regenerations. We apply these diagnostics to a family of data-constrained estimates of the iron cycle with sources σ tot in the range 1.9 – 41 Gmol yr -1 . We find that for states with σ tot > 7 Gmol yr -1 , 50 % or more of the DFe inventory has not been regenerated in the past and 85 % or more will not be regenerated in the future. The globally averaged mean number of past or future regenerations scales with the bulk iron lifetime τ ~ σ tot - 1 and has a range of 0.05 – 2.2 for past and 0.01 – 1.4 for future regenerations. Memory of birth location fades rapidly with each regeneration, and DFe regenerated more than approximately five times is found in a pattern shaped by Southern Ocean nutrient trapping. We quantify the intrinsic fertilization efficiency of the unperturbed system at any point r in the ocean as the global export production resulting from the DFe at r per iron molecule. We show that this efficiency is closely related to the mean number of future regenerations that the iron will experience. At the surface, the intrinsic fertilization efficiency has a global mean in the range 0.7 – 7 mol P (mmol Fe) -1 across our family of state estimates and is largest in the central tropical Pacific, with the Southern Ocean having comparable importance only for high-iron-source scenarios.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Identification of linear stochastic systems through projection filters

A novel method is presented for identifying a state-space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relationship between the state-space model and the finite-difference model of linear stochastic systems derived through projection filters. It is proved that least-squares identification of a finite difference model converges to the model derived from the projection filters. System pulse response samples are computed from the coefficients of the finite difference model.

Chen, Chung-Wen↗
NASA NTRS1983

A function space approach to state and model error estimation for elliptic systems

An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed.

Rodriguez, G.↗
NASA NTRS1983

A function space approach to state and model error estimation for elliptic systems

An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal esimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed. While this paper focuses on theoretical developments, applications arising in the area of large structure static shape determination are contained in a closely related paper (Rodriguez and Scheid, 1982).

Rodriguez, G.↗
DOE OSTI2025

Thermal Weight Determination and Interstate Coupling in State-Averaged ADAPT-VQE

Characterizing electronic thermal states at low temperatures is an important but challenging task in quantum chemistry and condensed matter physics, making it a prime candidate for a useful application in quantum computing. One of the most successful methods for state preparation on quantum computers is the Adaptive, Problem-Tailored (ADAPT) Variational Quantum Eigensolver (VQE), which has recently been generalized to treat excited states within a state-averaged framework as well as Gibbs states. In this work, we introduce Helmholtz-Optimized Thermal (HOT) ADAPT-VQE, an ancilla-free strategy for preparing Gibbs states that directly minimizes the Helmholtz free energy by targeting the dominant eigenstates of the thermal ensemble. We demonstrate the usefulness of HOT-ADAPT-VQE by predicting the free energy of two model systems with strongly correlated ground states: (1) the Fe 2+ cation in a magnetic field and (2) a [Cu 2 O 7 ] 10– fragment of the Mott insulator La 2 CuO 4 . Our results demonstrate that HOT-ADAPT-VQE significantly improves upon Gibbs-state estimates from multistate variants of ADAPT-VQE, often with substantially shallower quantum circuits, making it a promising candidate for thermal-state calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Cyber Attack Resilient HVDC System (CARDS) (Final Scientific/Technical Report)

This report summarizes the activities conducted under the DOE-OE funded project DE-OE0000824, where ABB Inc. (ABB), in collaboration with University of Illinois at Urbana-Champaign (UIUC), Bonneville Power Administration (BPA), Argonne National Laboratories (ANL), and University of Idaho (UI) pursued the development of a system of defense for High Voltage Direct Current transmission systems against cyber-attacks (CARDS). An HVDC substation with CARDS features will be more capable of mitigating cyber-attacks especially those that seek to control or interrupt the power flow in the HVDC line. It empowers existing HVDC intelligent electronic devices to collaborate in identifying and blocking malicious intents to trip HVDC AC circuit breakers, block thyristor valves, misconfigure devices settings, even though the commands and the measurements comply with correct syntax. The CARDS functions utilize the physics of electromagnetic AC and DC systems, electric power engineering principles, and state estimation to bring more in-depth cyber defense closer to the protected HVDC substation devices. With the aim of advancing the state of the art, the team proceeded to develop a demonstrable system against cyber-attacks in an HVDC substation environment, via functional upgrades to existing firmware of commercial off-the-shelf HVDC station devices, and/or embedded hardware, without the need for additional instrumentation nor new instrumentation technologies. It is to ensure that the solutions resulting from the project will support the HVDC devices currently installed in the US electricity delivery infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

Motion estimation of objects in KC-135 microgravity

The simulated microgravity environment aboard a KC-135 aircraft flying along a parabolic trajectory was used to study the ability of an autonomous space robot to grasp a freely translating and rotating object. Since the KC-135 cabin environment and the instrumentation for the Extravehicular Activity Helper/Retriever (EVAHR) do not provide a practical intertial reference frame, estimators based on the extended Kalman filter algorithm were used to model the relative translational dynamics of the KC-135 and the EVAHR. The estimator algorithms require intensive mathematical computation and therefore, i860 real-time. Estimator design, implementation concerns, and issues specific to the KC-135 environment are discussed and the architecture of the KC-135 translational state estimator is depicted.

Hewgill, Lisa↗
DOE OSTI2021

Public data from three US states provide new insights into well integrity

Significance There are over 900,000 active oil and gas wells in the United States. Estimates of the percentage of these wells that leak during their lifetime have been limited by data availability. We mined publicly available records from state regulatory databases and synthesized the results of various well testing methods into a uniform dataset that describes the integrity of 105,031 wells. Our analysis of the dataset provides insight into regional well leakage frequencies in three US states (Colorado, New Mexico, and Pennsylvania), spatial and temporal leakage trends among vertical and directional wells, and the characteristics of leaked fluids. Our findings demonstrate the value of statewide well testing programs and highlight the challenges of interpreting disparate interjurisdictional well testing data.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2018

Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error

Bayesian state estimation of a dynamical system from a stream of noisy measurements is important in many geophysical and engineering applications where high dimensionality of the state space, sparse observations, and model error pose key challenges. In this work, three computationally feasible, approximate Gaussian data assimilation/filtering algorithms are considered in various regimes of turbulent 2D Navier--Stokes dynamics in the presence of model error. The first source of error arises from the necessary use of reduced models for the forward dynamics of the filters, while a particular type of representation error arises from the finite resolution of observations which mix up information about resolved and unresolved dynamics. Two stochastically parameterized filtering algorithms, referred to as cSPEKF and GCF, are compared with 3DVAR---a prototypical time-sequential algorithm known to be accurate for filtering dissipative systems for a suitably inflated “background” covariance. We provide the first evidence that the stochastically parameterized algorithms, which do not rely on detailed knowledge of the underlying dynamics and do not require covariance inflation, can compete with or outperform an optimally tuned 3DVAR algorithm, and they can overcome competing sources of error in a range of dynamical scenarios.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

On optimal filtering of GPS dual frequency observations without using orbit information

The concept of optimal filtering of observations collected with a dual frequency GPS P-code receiver is investigated in comparison to an approach for C/A-code units. The filter presented here uses only data gathered between one receiver and one satellite. The estimated state vector consists of a one-way pseudorange, ionospheric influence, and ambiguity biases. Neither orbit information nor station information is required. The independently estimated biases are used to form double differences where, in case of a P-code receiver, the wide lane integer ambiguities are usually recovered successfully except when elevation angles are very small. An elevation dependent uncertainty for pseudorange measurements was discovered for different receiver types. An exponential model for the pseudorange uncertainty was used with success in the filter gain computations.

Eueler, Hans-Juergen↗
NASA NTRS1986

Spline-based distributed system identification with application to large space antennas

A parameter and state estimation technique for distributed models is demonstrated through the solution of a problem generic to large space antenna system identification. Assuming the position of the reflective surface of the maypole (hoop/column) antenna to be approximated by the static two-dimensional, stretched-membrane partial differential equation with variable-stiffness coefficient functions, a spline-based approximation procedure is described that estimates the shape and stiffness functions from data set observations. For given stiffness functions, the Galerkin projection with linear spline-based functions is applied to project the distributed problem onto a finite-dimensional subspace wherein algebraic equations exist for determining a static shape (state) prediction. The stiffness functions are then parameterized by cubic splines and the parameters estimated by an output error technique. Numerical results are presented for data descriptive of a 100-m-diameter maypole antenna.

Banks, H. T.↗
DOE OSTI2023

Simulation of Impedance Changes with Aging in Lithium Titanate-based Cells Using Physics-Based Dimensionless Modeling

Quantifying aging effects in lithium-ion cells with chemistries that have a flat open circuit potential is challenging. We implement a physics-based electrochemical model to track changes in the electrochemical impedance response of lithium titanate-based cells. Frequency domain equations of a pseudo two-dimensional model are made dimensionless, and the corresponding non-dimensional parameters are estimated using a Levenberg-Marquardt routine. The model weighs the relative contributions of changes in diffusion, ionic conduction within the electrolyte phase against solid phase electronic conduction towards cell aging. Solid-phase diffusion, charge transfer resistance and double layer capacitance at the solid-liquid interface are accounted for in the particle impedance. The estimation routine tracks dimensionless parameters using accelerated cycling data from full cells over 1000 cycles. The model can be deployed within a short time for state estimation using physics-based models without requiring prior knowledge of the battery chemistry, format, or capacity.

25 ENERGY STORAGE↗
DOE OSTI2022

Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi timescale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. Here, the proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.

42 ENGINEERING↗
DOE OSTI2020

Computationally efficient Bayesian quantum state tomography

We describe a method for Bayesian quantum state estimation combining efficient parameterization, a pseudo-likelihood, and advanced numerical sampling techniques. Examples reveal significant computational speedup, indicating the approach’s promise in practical quantum state tomography.

Lukens, Joseph↗
NASA NTRS1985

Venus gravity west of Beta Regio

Doppler tracking data from the Pioneer Venus Orbiter (PVO) have been used to estimate the anomalous gravity field in the region of Venus west of Beta Regio. The analysis invokes a Kalman filter-smoother to solve the nonlinear spacecraft state estimation problem and a linear Bayesian estimator to perform the geophysical inversion. The topographic map for this region, derived from the PVO radar, has been filtered to have the same distortions and degree of smoothing as the gravity map. The undulations of the gravity are about 0.2 times as large as expected from the topography, on the assumption that the latter is uncompensated. A comparison of the gravity and topography by means of the spectral admittance is consistent with Airy compensation at a depth of 50 km, if the surface material has a density of 2.6 g/cu cm. However, this is not a unique interpretation.

Goldberg, Z. M.↗