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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Generalized Linear Covariance Analysis

This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

n/a↗
NASA NTRS2013

Dawn Orbit Determination Team : Trajectory Modeling and Reconstruction Processes at Vesta

The NASA Dawn spacecraft was launched on September 27, 2007 on a mission to study the asteroid belt's two largest objects, Vesta and Ceres. It is the first deep space orbiting mission to demonstrate solar-electric ion propulsion, providing the necessary delta-V to enable capture and escape from two extraterrestrial bodies. At this time, Dawn has completed its science campaign at Vesta and is currently on its journey to Ceres, where it will arrive in mid-2015. The spacecraft spent over a year in orbit around Vesta from July 2011 through August 2012, capturing science data during four dedicated orbit phases. In order to maintain the reference orbits necessary for science and enable the transfers between those orbits, precise and timely orbit determination was required. The constraints associated with low-thrust ion propulsion coupled with the relatively unknown a priori gravity and rotation models for Vesta presented unique challenges for the Dawn orbit determination team. While [1] discusses the prediction performance of the orbit determination products, this paper discusses the dynamics models, filter configuration, and data processing implemented to deliver a rapid orbit determination capability to the Dawn project.

Vesta↗
NASA NTRS2018

A Survey of Methods for Computing Best Estimates of Endoatmospheric and Exoatmospheric Trajectories

Beginning with the mathematical prediction of planetary orbits in the early seventeenth century up through the most recent developments in sensor fusion methods, many techniques have emerged that can be employed on the problem of endo and exoatmospheric trajectory estimation. Although early methods were ad hoc, the twentieth century saw the emergence of many systematic approaches to estimation theory that produced a wealth of useful techniques. The broad genesis of estimation theory has resulted in an equally broad array of mathematical principles, methods and vocabulary. Among the fundamental ideas and methods that are briefly touched on are batch and sequential processing, smoothing, estimation, and prediction, sensor fusion, sensor fusion architectures, data association, Bayesian and non Bayesian filtering, the family of Kalman filters, models of the dynamics of the phases of a rocket's flight, and asynchronous, delayed, and asequent data. Along the way, a few trajectory estimation issues are addressed and much of the vocabulary is defined.

Bernard, William P.↗
DOE OSTI2023

Process Cycle Modeling with AI

Here a convoluted process model-filtering technique is presented that can build and successfully train the structural property artifacts of materials after multiple heat treatment cycles.

Romanov, Vyacheslav↗
DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗
DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Alterations of the climate of a primitive equation model produced by filtering approximations and subsequent tuning and stochastic forcing

A comparison is made of the simulated climates of nonlinear models based on the primitive equations (PE), balance equations (BE), and quasi-geostrophic (QG) equations. The models and numerical procedures are identical in all possible respects. The models are highly truncated spectral forms of Lorenz's (1960) energy preserving two-layer model. Two means of making use of the information contained in the (presumed known) short-term prediction error statistics are investigated. An unrealistically high level of thermal forcing is used so that the model climates are sufficiently different to allow any improvements due to the empirical methods to be observed. The general tuning problem is outlined and the QG model is tuned, using data obtained from a PE model run, to minimize the mean squared short term prediction error.

Hoffman, R. N.↗
NASA NTRS2021

Geomagnetic Secular Variation Forecast using the NASA GEMS Ensemble Kalman filter: A Candidate SV Model for IGRF‑13

We have produced a 5-year mean secular variation (SV ) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been sub-mitted as a candidate predictive model of IGRF-13 for the period 2020–2025.

Andrew Tangborn↗
DOE OSTI2022

Large-Eddy Simulation of a wind turbine using a Filtered Actuator Line Model

When dealing with multirotor devices such as quadcopters or wind farms, the cost of blade-resolved large-eddy simulation (LES) becomes prohibitive. Combining LES with a family of lower-fidelity models, called actuator line models (ALMs), has grown in popularity in the past decade. ALM replaces full blade resolution with an array of actuator points or lines parameterized by aerodynamic lift/drag polar plots along the blades. Body forces computed based on these actuator points are then projected onto the LES flow mesh, mimicking the effect of rotating blades on the flow. However, the optimal projection radius and the associated LES grid size is often too restrictive for multirotor simulations. Recently, a new tip-correction-based filtered ALM (F-ALM) was proposed by Martinez-Tossas and Meneveau (2019), which allows coarser-than-optimal grids by avoiding the associated overprediction of thrust. In this work, F-ALM is implemented into a high-order, in-house LES code to simulate National Renewable Energy Laboratory Phase VI wind turbine. It is then followed by a comparison between the baseline ALM and the newly implemented F-ALM in terms of instantaneous and time-averaged flow fields and blade loads, revealing the advantages of F-ALM in preventing the overprediction of power on coarse grids. Finally, this encourages accurate and affordable simulations of multirotor devices in the future.

17 WIND ENERGY↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2022

Estimation of plasma properties using an extended Kalman filter with plasma global models

A physically-constrained extended Kalman filter (EKF) is applied to various zero-dimensional global models for the estimation of plasma properties using time-dependent experimental data such as the plasma density or ion flux. The capability of the EKF is demonstrated to estimate unknown system states simultaneously, such as reaction rate coefficients and the absorbed electron input power, which can be difficult, if not impossible, to measure experimentally. Global models accounting for pure argon reactions and argon-oxygen reactions are used in this work to demonstrate the ability of the filter to estimate dynamic and complex systems. Furthermore, the results obtained from the EKF plasma global model illustrate that model-data fusion techniques can be used to estimate plasma properties and processes for time-varying systems, such as pulsed discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Development of a Model Correlating Axial HEPA Filter Performance Between Elevated and Standard Conditions

The ASME AG-1 Code specifies minimum rated airflow based on standard cubic feet per minute (SCFM) for axial high efficiency particulate air (HEPA) filters and actual cubic feet per minute (ACFM) for radial HEPA filters with corresponding maximum pressure drop. A quantitative analysis can be performed to demonstrate the variation in filter efficiency and develop a correlation of performance between axial filters subjected to ACFM and SCFM. This study illustrates the variation in filter efficiency of axial HEPA filters subjected to ACFM and SCFM flow rate conditions at varying atmospheric pressures. It also presents the methods used to calculate correction factors to convert filter efficiency and pressure drop from elevated to standard conditions. The Axial Large Scale Test Stand (ALSTS) in the Institute for Clean Energy Technology (ICET) at Mississippi State University has been utilized to control aerosol concentration, flowrate, atmospheric pressure, relative humidity, and temperature. The filter test matrix consists of axial HEPA filters rated at 1,000 and 1,500 CFM with varying filter pack types. Each filter is subjected to varying pressures of 14.70 psi, 13.55 psi, 12.40 psi, and 11.25 psi, temperatures of 70 deg. F, 110 deg. F, and 150 deg. F, and relative humidities of 50% and 80%, resulting in twenty-four unique testing points. The corresponding SCFM flow rates are computed for each point in order to form a comparison. Evaluation of the effects of designating ACFM or SCFM will be based upon the filter efficiencies of generated Dioctyl Phthalate (DOP) particles at a diameter of 0.3 micrometers. The results show filter efficiency decreasing as atmospheric pressure decreases due to increased filter face velocity. A correlation model is developed to convert filter efficiency and pressure drop at elevated conditions to those of standard pressure, relative humidity, and temperature.

42 ENGINEERING↗
NASA NTRS1981

Geomagnetic modeling by optimal recursive filtering

The results of a preliminary study to determine the feasibility of using Kalman filter techniques for geomagnetic field modeling are given. Specifically, five separate field models were computed using observatory annual means, satellite, survey and airborne data for the years 1950 to 1976. Each of the individual field models used approximately five years of data. These five models were combined using a recursive information filter (a Kalman filter written in terms of information matrices rather than covariance matrices.) The resulting estimate of the geomagnetic field and its secular variation was propogated four years past the data to the time of the MAGSAT data. The accuracy with which this field model matched the MAGSAT data was evaluated by comparisons with predictions from other pre-MAGSAT field models. The field estimate obtained by recursive estimation was found to be superior to all other models.

Gibbs, B. P.↗
NASA NTRS1993

Theoretical model for a Stark anomalous dispersion optical filter

A theoretical model for the first atomic Stark anomalous dispersion optical filter is reported. The results show the filter may serve as a widely tunable narrow bandwidth and high throughput optical filter for freespace laser communications and remote sensing.

Yin, B.↗
NASA NTRS1991

Linear prediction filters for linear and nonlinear modeled geomagnetic activity

It is shown that the Faraday loop analog model of geomagnetic activity exhibits both the directly driven and loading-unloading magnetospheric responses to solar wind input. It is further shown that the directly driven component is a linear response to loading while the loading-unloading response is nonlinear. Linear prediction filters which relate model input to output are discussed. By either allowing or suppressing the loading-unloading model response filters that relate to nonlinear or linear dynamics, respectively, have been computed. Filters that described the directly driven response are finite ranged; they asymptote to zero with increasing lag on a time scale that is fixed by the dissipation rate of the model. Filters that describe the nonlinear total response are infinite ranged; they asymptote with increasing lag to large amplitude periodic oscillations. Some implications of these infinite ranged filters are discussed.

Klimas, A. J.↗
NASA NTRS2012

Improved Maneuver Reconstructions for the GRAIL Orbiters

Maneuver reconstructions for the Gravity Recovery and Interior Laboratory (GRAIL) A and B lunar orbiters were improved through updates to the orbit determination filter and dynamic models. Consistent reconstructions of the 27 GRAIL A and B maneuvers from the Trans-Lunar Cruise phase in the fall of 2011 through the Transition to Science Formation phase in February 2012 were performed. Standard methods of orbit determination were applied incorporating the latest dynamic models and filter strategies developed by the GRAIL Navigation and Science Teams, including a high resolution, 420 x 420 degree and order lunar spherical harmonic gravity field model. For Trans-Lunar Cruise for GRAIL-A (TLC-A), all maneuvers executed with delta V errors below 5.50 +/- 0.50 mm/s and pointing errors below 0.25 degrees. GRAIL-A lunar orbit maneuvers had delta V errors below 30.0 mm/s and pointing errors below 0.51 degrees. For TLC-B, all maneuvers executed with delta V errors below 8.60 +/- 1.41 mm/s and pointing errors below 0.300 degrees. GRAIL-B maneuvers in lunar orbit executed with maximum delta V errors of 25.0 mm/s and pointing error of 0.43 degrees. These maneuver reconstructions will enable the GRAIL Navigation Team to better characterize the main engine performance of each spacecraft. This will help the Navigation Team to navigate low (greater than 8 km) altitude orbits during the extended mission phase in the fall of 2012.

lunar mapping↗