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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 523 records · Page 29

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NASA NTRS1979

The calculation of spacecraft potential: Comparison between theory and observation

A simple charge balance model based on the work of DeForest was adapted for the calculation of spacecraft potentials. The model was calibrated with ATS 5 plasma data. Once calibrated, the model was used to calculate the time-varying potential that was observed as a spacecraft passes in and out of eclipse. Errors on the order of + or - 800 volts were observed over a range of 0 to -10,000 volts. Possible applications of the model to large space structures are discussed.

Garrett, H. B.↗

NASA NTRS1982

A fast hidden line algorithm for plotting finite element models

Effective plotting of finite element models requires the use of fast hidden line plot techniques that provide interactive response. A high speed hidden line technique was developed to facilitate the plotting of NASTRAN finite element models. Based on testing using 14 different models, the new hidden line algorithm (JONES-D) appears to be very fast: its speed equals that for normal (all lines visible) plotting and when compared to other existing methods it appears to be substantially faster. It also appears to be very reliable: no plot errors were observed using the new method to plot NASTRAN models. The new algorithm was made part of the NPLOT NASTRAN plot package and was used by structural analysts for normal production tasks.

Jones, G. K.↗

NASA NTRS1992

Design and experimental validation of a flutter suppression controller for the active flexible wing

The synthesis and experimental validation of an active flutter suppression controller for the Active Flexible Wing wind tunnel model is presented. The design is accomplished with traditional root locus and Nyquist methods using interactive computer graphics tools and extensive simulation based analysis. The design approach uses a fundamental understanding of the flutter mechanism to formulate a simple controller structure to meet stringent design specifications. Experimentally, the flutter suppression controller succeeded in simultaneous suppression of two flutter modes, significantly increasing the flutter dynamic pressure despite modeling errors in predicted flutter dynamic pressure and flutter frequency. The flutter suppression controller was also successfully operated in combination with another controller to perform flutter suppression during rapid rolling maneuvers.

Waszak, Martin R.↗

NASA NTRS1994

Anticipatory Neurofuzzy Control

Technique of feedback control, called "anticipatory neurofuzzy control," developed for use in controlling flexible structures and other dynamic systems for which mathematical models of dynamics poorly known or unknown. Superior ability to act during operation to compensate for, and adapt to, errors in mathematical model of dynamics, changes in dynamics, and noise. Also offers advantage of reduced computing time. Hybrid of two older fuzzy-logic control techniques: standard fuzzy control and predictive fuzzy control.

Mccullough, Claire L.↗

NASA NTRS1998

Annual Research Briefs, 1998

The topics contained in this progress report are direct numerical simulation of turbulent non-premixed combustion with realistic chemistry; LES of non-premixed turbulent reacting flows with conditional source term estimation; measurements of the three-dimensional scalar dissipation rate in gas-phase planar turbulent jets; direct simulation of a jet diffusion flame; on the use of interpolating wavelets in the direct numerical simulation of combustion; on the use of a dynamically adaptive wavelet collocation algorithm in DNS (direct numerical simulation) of non-premixed turbulent combustion; 2D simulations of Hall thrusters; computation of trailing-edge noise at low mach number using LES and acoustic analogy; weakly nonlinear modeling of the early stages of bypass transition; interactions between freestream turbulence and boundary layers; interfaces at the outer boundaries of turbulent motions; largest scales of turbulent wall flows; the instability of streaks in near-wall turbulence; an implementation of the v(sup 2) - f model with application to transonic flows; heat transfer predictions in cavities; a structure-based model with stropholysis effects; modeling a confined swirling coaxial jet; subgrid-scale models based on incremental unknowns for large eddy simulations; subgrid scale modeling taking the numerical error into consideration; towards a near-wall model for LES of a separated diffuser flow; on the feasibility of merging LES with RANS (Reynolds Averaging Numerical simulation) for the near-wall region of attached turbulent flows; large-eddy simulation of a separated boundary layer; numerical study of a channel flow with variable properties; on the construction of high order finite difference schemes on non-uniform meshes with good conservation properties; development of immersed boundary methods for complex geometries; and particle methods for micro and macroscale flow simulations.

Spinks, Debra↗

NASA NTRS1998

Current Limitations on VLBI Accuracy

The contribution of VLBI to geophysics and geodesy arises from its ability to measure distances between stations in a network and to determine the orientation of stations in the network as well as the orientation of the network with respect to the external reference frame of extragalactic radio objects. Integrating nearly two decades of observations provides useful information about station positions and velocities and the orientation of the Earth, but the complications of the real world and the limitations of observing, modeling and analysis prevent recovery of all effects. Of the factors that limit the accuracy of seemingly straightforward geodetic parameters, the neutral propagation medium has been subject to the greatest scrutiny, but the treatment of the mapping function, the wet component and spatial/temporal inhomogeneities is still improving. These affect both the terrestrial scale and consistency over time. The modeling of non-secular site motions (tides and loading) has increased in sophistication, but there are some differences between the models and the observations. VLBI antennas are massive objects, so their behavior is quite unlike GPS monuments, but antenna deformations add some (generally) unmodeled signal. Radio sources used in geodetic VLBI observations are selected for strength and (relative) absence of structure, but apparent changes in position can leak into geodetic parameters. A linear rate of change of baseline or site parameters is the simplest model and its error improves with time span. However, in most cases the VLBI data distribution is insufficient to look for real non-linear behavior that might affect the average rate. A few sites have multiple VLBI antennas, and some show small differences in rate. VLBI intrinsically measures relative positions and velocities, but individual site positions and velocities are generally more useful. The creation of the VLBI terrestrial reference frame, which transforms relative information into individual results, is an empirical process that has intrinsic errors. While UT1 is uniquely measured by VLBI, the geographical distribution and availability of VLBI stations, especially in the southern hemisphere, and the consistency of the VLBI terrestrial reference frame may limit the accuracy of Earth orientation measurements. The effects of particular error sources on geodetic and geophysical parameters derived from VLBI data will be illustrated.

Ma, Chopo↗

NASA NTRS2003

Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

Neural networks were used to model wing bending-moment loads, torsion loads, and control surface hinge-moments of the Active Aeroelastic Wing (AAW) aircraft. Accurate loads models are required for the development of control laws designed to increase roll performance through wing twist while not exceeding load limits. Inputs to the model include aircraft rates, accelerations, and control surface positions. Neural networks were chosen to model aircraft loads because they can account for uncharacterized nonlinear effects while retaining the capability to generalize. The accuracy of the neural network models was improved by first developing linear loads models to use as starting points for network training. Neural networks were then trained with flight data for rolls, loaded reversals, wind-up-turns, and individual control surface doublets for load excitation. Generalization was improved by using gain weighting and early stopping. Results are presented for neural network loads models of four wing loads and four control surface hinge moments at Mach 0.90 and an altitude of 15,000 ft. An average model prediction error reduction of 18.6 percent was calculated for the neural network models when compared to the linear models. This paper documents the input data conditioning, input parameter selection, structure, training, and validation of the neural network models.

Allen, Michael J.↗

NASA NTRS2013

Forest Structure Characterization Using JPL's UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Tomography

This paper concerns forest remote sensing using JPL's multi-baseline polarimetric interferometric UAVSAR data. It presents exemplary results and analyzes the possibilities and limitations of using SAR Tomography and Polarimetric SAR Interferometry (PolInSAR) techniques for the estimation of forest structure. Performance and error indicators for the applicability and reliability of the used multi-baseline (MB) multi-temporal (MT) PolInSAR random volume over ground (RVoG) model are discussed. Experimental results are presented based on JPL's L-band repeat-pass polarimetric interferometric UAVSAR data over temperate and tropical forest biomes in the Harvard Forest, Massachusetts, and in the La Amistad Park, Panama and Costa Rica. The results are partially compared with ground field measurements and with air-borne LVIS lidar data.

UAVSAR (Uninhabited Aerial Vehicle SAR)↗

NASA NTRS2013

Forest Structure Characterization Using Jpl's UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Tomography

remote sensing↗

DOE OSTI2020

Exploring Hilbert space on a budget: Novel benchmark set and performance metric for testing electronic structure methods in the regime of strong correlation

This work explores the ability of classical electronic structure methods to efficiently represent (compress) the information content of full configuration interaction (FCI) wave functions. We introduce a benchmark set of four hydrogen model systems of different dimensionalities and distinctive electronic structures: a 1D chain, a 1D ring, a 2D triangular lattice, and a 3D close-packed pyramid. To assess the ability of a computational method to produce accurate and compact wave functions, we introduce the accuracy volume, a metric that measures the number of variational parameters necessary to achieve a target energy error. Using this metric and the hydrogen models, we examine the performance of three classical deterministic methods: (i) selected configuration interaction (sCI) realized both via an a posteriori (ap-sCI) and variational selection of the most important determinants, (ii) an a posteriori singular value decomposition (SVD) of the FCI tensor (SVD-FCI), and (iii) the matrix product state representation obtained via the density matrix renormalization group (DMRG). We find that the DMRG generally gives the most efficient wave function representation for all systems, particularly in the 1D chain with a localized basis. For the 2D and 3D systems, all methods (except DMRG) perform best with a delocalized basis, and the efficiency of sCI and SVD-FCI is closer to that of DMRG. For larger analogs of the models, the DMRG consistently requires the fewest parameters but still scales exponentially in 2D and 3D systems, and the performance of SVD-FCI is essentially equivalent to that of ap-sCI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Relationships among climatological vertical moisture structure, column water vapor, and precipitation over the central Amazon in observations and CMIP5 models

Bias and spread in Coupled Model Intercomparison Project Phase 5 simulated vertical specific humidity (q) structure are examined and related to both precipitation and column water vapor (cwv) near Manaus, Brazil, site of the recent Green Ocean Amazon campaign. Simulated seasonal mean q profiles are typically too dry, especially at low levels and during the local dry season, consistent with previously identified surface hydroclimate biases in the Amazon. Multimodel empirical orthogonal function analysis of the models' monthly climatological q profiles indicates two significant modes of ensemble spread in moisture vertical structure, with the leading mode peaked at low levels and the second mode in the lower free troposphere (LFT). While both modes project onto simulated cwv spread, only the first projects on precipitation, suggesting inconsistent sensitivity of simulated rainfall to LFT moisture. Finally, relative to observations, models with high cwv and low-level moisture errors tend to exhibit high precipitation error.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perserverance rover to the surface of Mars at Jezero Crater. The entry capsule carried a set of instrumentation installed on the heat shield and backshell, named the Mars 2020 Entry, Descent, and Landing Instrumentation. The instruments include pressure transducers, thermocouples, heat flux gauges, and radiometers to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, are located in close proximity on the backshell. Each sensor is exposed to roughly the same environment, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that location on the backshell. The approach makes use of the Kalman filter methodology for solving state estimation problems. The filter has a predictor/corrector structure in which simplified process models are used to propagate the aeroheating states forward through time, which are then updated based on the measurement data. The method has been tested on simulated datasets.

Christopher D. Karlgaard↗

NASA NTRS2003

Collaborative Research: Atlantic Ocean Tropical/Subtropical Processes from Seasonal to Decadal Time Scales: Model/Data, Model/Model Comparison and Model/Data Synthesis Through Assimilation

The effort of this first year of research has been focused on the assimilation of TOPEX/Poseidon altimetric data into a primitive equation model of the Atlantic tropical/subtropical circulation. A reduced-rank, stationary Kalman filter has been constructed to assimilate the altimetric sea surface height anomaly (SHA) into the model. The goal is to assess how the inter-hemispheric transports between the Atlantic subtropics and tropics are affected by the assimilation and how the subsurface thermocline structure , and its variability ,is dynamically constrained by the SHA. The model is a reduced-gravity primitive equation GCM of the upper Atlantic Ocean between 30 S and 30 N. The assimilation scheme is an approximation to the extended Kalman filter in which the error covariances of the state estimates are calculated only in a reduced- dimension subspace. The subspace is defined by the leading empirical orthogonal functions calculated from an unconstrained model calculation. Both an identical twin experiment using simulated SHA observations and assimilation of the real TOPEX data were performed. Results from the twin experiments demonstrate the ability of the method to constrain the ocean circulation and the subsurface temperature structure. The impact on the subsurface temperature structure of TOPEX assimilation was assessed using data from expandable bathythermographs. This showed a substantial improvement in the estimated temperature variability only within 13 degrees in latitude around the equator. The impact of TOPEX SHA assimilation on zonally integrated meridional transport across different latitudes was also estimated. Again within 13 degrees from the equator both the mean amplitude and interannual variability of the surface and subsurface transports were significantly enhanced, while the transports were insensitive to the assimilation in the subtropics.

Malannotte-Rizzoli, Paola↗

NASA NTRS1976

Solar limb brightening in submillimeter wavelengths

Differential two-beam scans of the sun in submillimeter wavelengths (350 microns to 1 millimeter) indicate limb brightening approaching 1 percent when the cosine of the angle from the normal equals 0.60. The observations also show considerable chromospheric structure, both in active and quiet regions, but with less relative amplitude than at millimeter and centimeter wavelengths. The limited angular resolution of the observing system, together with photometric errors due to fluctuating atmospheric transparency, make the brightness profile of the extreme limb uncertain. The observed degree of limb brightening is considerably less than that consistent with spherically symmetric model atmospheres based on continuum brightness-temperature measurements. The suppression of limb brightening suggests the existence of irregular granular structure with both horizontal and vertical characteristic sizes of the order of 1500 km. High-resolution images in the wings of the K-line show granular structure of about this horizontal scale.

Lindsey, C.↗

DOE OSTI2022

Regression of exchangeable relational arrays

Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally the coefficient estimators themselves, must account for dependence between elements of the array, e.g., relations involving the same actor. Existing estimators of standard errors that recognize such relational dependence rely on estimating extremely complex, heterogeneous structure across actors. This paper develops a new class of parsimonious coefficient and standard error estimators for regressions of relational arrays. Here we leverage an exchangeability assumption to derive standard error estimators that pool information across actors, and are substantially more accurate than existing estimators in a variety of settings. This exchangeability assumption is pervasive in network and array models in the statistics literature, but not previously considered when adjusting for dependence in a regression setting with relational data. We demonstrate improvements in inference theoretically, via a simulation study, and by analysis of a dataset involving international trade.

97 MATHEMATICS AND COMPUTING↗