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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Overview of the MSTI 2 on-orbit alignment

The Miniature Sensor Technology Integration (MSTI) 2 Spacecraft is a small 3-axis stabilized spacecraft designed to track mid-range missiles and estimate their state vectors. In order to accurately estimate the target state vector, the MSTI 2 spacecraft must have highly accurate knowledge of its own attitude. Errors in its attitude knowledge arise primarily from the errors in its Attitude Control System (ACS) sensors. The ACS sensors on the spacecraft include a scanning Earth Sensor (ES), a Sun Sensor (SS), and two 2-axis gyros. The On-Orbit Alignment (OOA) generated an error map of the ES and estimated the biases of the SS and the misalignment of the gyros. This paper discusses some of the error sources, and the techniques used to reduce the effects of these errors. The payload carried by the MSTI2 spacecraft is a high fidelity camera, which was aimed at the target using gimballed mirrors. By aiming it at a celestial target, the payload was used as a high-accuracy single-axis attitude reference. This attitude reference was compared to the attitude reference of the ACS sensors, and the errors were attributed to the ACS sensors.

Rygaard, Christopher A.↗
DOE OSTI2025

Optical galaxy cluster mock catalogs with realistic projection effects: Validations with the SDSS clusters

Galaxy clusters identified in optical imaging surveys suffer from projection effects: Physically unassociated galaxies along a cluster’s line of sight can be counted as its members and boost the observed richness (the number of cluster members). To model the impact of projection on cluster cosmology analyses, we apply a halo occupation distribution model to 𝑁-body simulations to simulate the red galaxies contributing to cluster members, and we use the number of galaxies in a cylinder along the line of sight (counts in cylinders) to model the impact of projection on cluster richness. We compare three projection models: uniform, quadratic, and Gaussian, and we convert between them by matching their effective cylinder volumes. We validate our mock catalogs using SDSS redMaPPer clusters’ data vectors, including counts vs richness, stacked lensing signal, spectroscopic redshift distribution of member galaxies, and richness remeasured on a redshift grid. We find the former two are insensitive to the projection model, while the latter two favor a quadratic projection model with a width of ≈180 ℎ −1 Mpc (equivalent to the volume of a uniform model with a width of 100 ℎ −1 Mpc and a Gaussian model with a width of 110 ℎ −1 Mpc, or a Gaussian redshift error of 0.04). Furthermore, our framework provides an efficient and flexible way to model optical cluster data vectors, paving the way for a simulation-based joint analysis for clusters, galaxies, and shear.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

WE-AB-207A-04: Random Undersampled Cone Beam CT: Theoretical Analysis and a Novel Reconstruction Method

Purpose: Reducing x-ray exposure and speeding up data acquisition motived studies on projection data undersampling. It is an important question that for a given undersampling ratio, what the optimal undersampling approach is. In this study, we propose a new undersampling scheme: random-ray undersampling. We will mathematically analyze its projection matrix properties and demonstrate its advantages. We will also propose a new reconstruction method that simultaneously performs CT image reconstruction and projection domain data restoration. Methods: By representing projection operator under the basis of singular vectors of full projection operator, matrix representations for an undersampling case can be generated and numerical singular value decomposition can be performed. We compared properties of matrices among three undersampling approaches: regular-view undersampling, regular-ray undersampling, and the proposed random-ray undersampling. To accomplish CT reconstruction for random undersampling, we developed a novel method that iteratively performs CT reconstruction and missing projection data restoration via regularization approaches. Results: For a given undersampling ratio, random-ray undersampling preserved mathematical properties of full projection operator better than the other two approaches. This translates to advantages of reconstructing CT images at lower errors. Different types of image artifacts were observed depending on undersampling strategies, which were ascribed to the unique singular vectors of the sampling operators in the image domain. We tested the proposed reconstruction algorithm on a Forbid phantom with only 30% of the projection data randomly acquired. Reconstructed image error was reduced from 9.4% in a TV method to 7.6% in the proposed method. Conclusion: The proposed random-ray undersampling is mathematically advantageous over other typical undersampling approaches. It may permit better image reconstruction at the same undersampling ratio. The novel algorithm suitable for this random-ray undersampling was able to reconstruct high-quality images.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Vector quantizer designs for joint compression and terrain categorization of multispectral imagery

Two vector quantizer designs for compression of multispectral imagery and their impact on terrain categorization performance are evaluated. The mean-squared error (MSE) and classification performance of the two quantizers are compared, and it is shown that a simple two-stage design minimizing MSE subject to a constraint on classification performance has a significantly better classification performance than a standard MSE-based tree-structured vector quantizer followed by maximum likelihood classification. This improvement in classification performance is obtained with minimal loss in MSE performance. The results show that it is advantageous to tailor compression algorithm designs to the required data exploitation tasks. Applications of joint compression/classification include compression for the archival or transmission of Landsat imagery that is later used for land utility surveys and/or radiometric analysis.

Gorman, John D.↗
NASA NTRS1990

Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

Liu, Kuojuey Ray↗
DOE OSTI2022

Methods for Determining the Infrared Complex Refractive Indices n (λ) and k (λ) from Organic Solid Powders: Comparison of a Sucrose Single Crystal and a Pressed Pellet

There exists a need to measure optical constants n and k for many materials, especially organic solids in the infrared spectral region, but at present, there are only two established methods for making such measurements: single-angle (SA) reflectance and infrared spectroscopic ellipsometry (IRSE). The former derives the n/k vectors via measurement of the change in the amplitude of light reflected from the sample and then uses the Kramers–Kronig transform, while the latter measures the change in polarization of the reflected light from the sample to derive n/k. Here we make a direct comparison between the n/k vectors derived from SA measurements of a sucrose single crystal and the n/k values obtained after the crystal had been crushed, ground to powder, and pressed into a pellet form. The pellet was measured not only by SA but also via the IRSE method, and we found that pressing specular-quality pellets was critical, especially for the SA data. The n/k values of this typical organic molecule all agree to within 0.04 root-mean-square error, confirming that a pressed pellet can be used instead of a crystal to derive the intrinsic n/k vectors. In conclusion, this result is important for the many organic materials that, unlike sugar, do not have centimeter-sized crystals readily available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

A block matching-based registration algorithm for localization of locally advanced lung tumors

Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm{sup 3}), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0.001). Left-right, anterior-posterior, and superior-inferior systematic BDE were 3.2, 2.4, and 4.4 mm, respectively, with random BDE of 2.4, 2.1, and 2.7 mm. Margins required to include both localization and delineation uncertainties ranged from 5.0 to 11.7 mm, an average of 40% less than required for bony alignment. Conclusions: BMR is a promising approach for automatic lung tumor localization. Further evaluation is warranted to assess the accuracy and robustness of BMR against other potential localization strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1985

Errors in reduction methods

A mathematical basis is given for comparing the relative merits of various techniques used to reduce the order of large linear and nonlinear dynamics problems during their numerical integration. In such techniques as Guyan-Irons, path derivatives, selected eigenvectors, Ritz vectors, etc., the nth order initial value problem of /y(dot) = f(y) for t greater than 0, y(0) given/ is typically reduced to the mth order (m is much less than n) problem of /z(dot) = g(z) for t greater than 0, z(0) given/ by the transformation y = Pz where P changes from technique to technique. This paper gives an explicit approximate expression for the reduction error e-i in terms of P and the Jacobian of f. It is shown that: (a) reduction techniques are more accurate when the time rate of change of the response y is relatively small; (b) the change in response between two successive stations contributes to the errors at future stations after the change in response is transformed by a filtering matrix H, defined in terms of P; (c) the error committed at a station propagates to future stations by a mixing and scaling matrix G, defined in terms of P, Jacobian and of f, and time increment h. The paper discusses the conditions under which the reduction errors may be minimized and gives guidelines for selecting the reduction basis vector, i.e., the columns of P.

Utku, S.↗
NASA NTRS1993

A model for the motion of the Philippine Sea plate consistent with NUVEL-1 and geological data

We investigate angular velocity vectors of the Philippine Sea (PH) plate relative to the adjacent major plates, Eurasia (EU) and Pacific (PA), and the smaller Caroline (CR) plate. Earthquake slip vector data along the Philippine Sea plate are inverted, subject to the constraint that EU-PA motion equals that predicted by the global relative plate model NUVEL-1. The resulting solution fails to satisfy geological constraints along the Caroline-Pacific boundary: convergence along the Mussau Trench and divergence along the Sorol Trough. We then seek solutions satisfying both the CR-PA boundary conditions and the Philippine Sea slip vector data, by adjusting the PA-PH and EU-PH best fitting poles within their error ellipses. We also consider northern Honshu to be part of the North American plate and impose the constraint that the Philippine Sea plate subducts beneath northern Honshu along the Sagmi Trough in a NNW-NW direction. Of the solutions satisfying these conditions, we select the best EU-PH as 48.2 deg N, 157.0 deg E, 1.09 deg/my, corresponding to a pole far from Japan and south of Kamchatka, and PA-PH, 1.2 deg N, 134.2 deg E, 1.00 deg/my. Predicted NA-PH and EU-PH convergence rates in central Honshu are consistent with estimated seismic slip rates. Previous estimates of the EU-PH pole close to central Honshu are inconsistent with extension within the Bonin backarc implied by earthquake slip vectors and NNW-NW convergence of the Bonin forearc at the Sagami Trough.

Seno, Tetsuzo↗
DOE OSTI2014

Adaptive h -refinement for reduced-order models: ADAPTIVE h -refinement for reduced-order models

Our work presents a method to adaptively refine reduced-order models a posteriori without requiring additional full-order-model solves. The technique is analogous to mesh-adaptive h-refinement: it enriches the reduced-basis space online by ‘splitting’ a given basis vector into several vectors with disjoint support. The splitting scheme is defined by a tree structure constructed offline via recursive k-means clustering of the state variables using snapshot data. This method identifies the vectors to split online using a dual-weighted-residual approach that aims to reduce error in an output quantity of interest. The resulting method generates a hierarchy of subspaces online without requiring large-scale operations or full-order-model solves. Furthermore, it enables the reduced-order model to satisfy any prescribed error tolerance regardless of its original fidelity, as a completely refined reduced-order model is mathematically equivalent to the original full-order model. Experiments on a parameterized inviscid Burgers equation highlight the ability of the method to capture phenomena (e.g., moving shocks) not contained in the span of the original reduced basis.

42 ENGINEERING↗
DOE OSTI2023

Dimension Reduction and Redundancy Removal through Successive Schmidt Decompositions

Quantum computers are believed to have the ability to process huge data sizes, which can be seen in machine learning applications. In these applications, the data, in general, are classical. Therefore, to process them on a quantum computer, there is a need for efficient methods that can be used to map classical data on quantum states in a concise manner. On the other hand, to verify the results of quantum computers and study quantum algorithms, we need to be able to approximate quantum operations into forms that are easier to simulate on classical computers with some errors. Motivated by these needs, in this paper, we study the approximation of matrices and vectors by using their tensor products obtained through successive Schmidt decompositions. We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms, which can be easily mapped onto quantum circuits. The examples include random data with different distributions, the Gram matrices of iris flower, handwritten digits, 20newsgroup, and labeled faces in the wild. Similarly, some quantum operations, such as quantum Fourier transform and variational quantum circuits with a small depth, may also be approximated with a few terms that are easier to simulate on classical computers. Furthermore, we show how the method can be used to simplify quantum Hamiltonians: In particular, we show the application to randomly generated transverse field Ising model Hamiltonians. The reduced Hamiltonians can be mapped into quantum circuits easily and, therefore, can be simulated more efficiently.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1969

Saturn V navigation update.

Navigation state vector update effect on lunar mission completion capability for Saturn 5, emphasizing hardware and scheme errors and accelerometer failure

Obrien, R. M.↗
NASA NTRS1976

System for imposing directional stability on a rocket-propelled vehicle

An improved system for use in imposing directional stability on a rocket-propelled vehicle is described. The system includes a pivotally supported engine-mounting platform, a gimbal ring mounted on the platform and adapted to pivotally support a rocket engine and an hydraulic actuator connected to the platform for imparting selected pivotal motion. An accelerometer and a signal comparator circuit for providing error intelligence indicative of aberration in vehicle acceleration is included along with an actuator control circuit connected with the actuator and responsive to error intelligence for imparting pivotal motion to the platform. Relocation of the engine's thrust vector is thus achieved for imparting directional stability to the vehicle.

Perkins, H.↗
NASA NTRS1986

Rendezvous BET Program

Computes relative positions of two vehicles in concentric orbits. LRBET3 program best-estimate-of-trajectory (BET) calculation for postflight trajectory analysis of Shuttle orbital rendezvous maneuvers. LRBET3 produces estimated measurements for reconstructing relative positions of two vehicles. Kalman filter and smoothing filter applied to relative measurement input data to estimate state vector, reduce noise, and produce BET output. BET calculation minimizes variances of all trajectory estimation errors. LRBET3 written in FORTRAN IV for batch execution.

Lear, W. M.↗
NASA NTRS1990

Disturbance-Accommodating Controller Would Aim Antenna

Proposed system for aiming large paraboloidal-dish antenna based on theory of disturbance-accommodating control. Existing methods of control combined to suppress systematic errors. Approach is to cancel static errors for precise pointing of antenna by treating systematic misalignment errors, as well as servo-commands, as disturbances to controlled system. In controller, another vector estimated simultaneously with estimation of state vector. Other vector represents disturbance state, used in determining more-complete control strategy. Aiming improved through sequence of modifications solely in existing software.

Gresham, L. L.↗
NASA NTRS1994

Progress in navigation filter estimate fusion and its application to spacecraft rendezvous

A new derivation of an algorithm which fuses the outputs of two Kalman filters is presented within the context of previous research in this field. Unlike other works, this derivation clearly shows the combination of estimates to be optimal, minimizing the trace of the fused covariance matrix. The algorithm assumes that the filters use identical models, and are stable and operating optimally with respect to their own local measurements. Evidence is presented which indicates that the error ellipsoid derived from the covariance of the optimally fused estimate is contained within the intersections of the error ellipsoids of the two filters being fused. Modifications which reduce the algorithm's data transmission requirements are also presented, including a scalar gain approximation, a cross-covariance update formula which employs only the two contributing filters' autocovariances, and a form of the algorithm which can be used to reinitialize the two Kalman filters. A sufficient condition for using the optimally fused estimates to periodically reinitialize the Kalman filters in this fashion is presented and proved as a theorem. When these results are applied to an optimal spacecraft rendezvous problem, simulated performance results indicate that the use of optimally fused data leads to significantly improved robustness to initial target vehicle state errors. The following applications of estimate fusion methods to spacecraft rendezvous are also described: state vector differencing, and redundancy management.

Carpenter, J. Russell↗
NASA NTRS1993

Postlaunch calibration of spacecraft attitude instruments

The accuracy of both onboard and ground attitude determination can be significantly enhanced by calibrating spacecraft attitude instruments (sensors) after launch. Although attitude sensors are accurately calibrated before launch, the stresses of launch and the space environment inevitably cause changes in sensor parameters. During the mission, these parameters may continue to drift requiring repeated on-orbit calibrations. The goal of attitude sensor calibration is to reduce the systematic errors in the measurement models. There are two stages at which systematic errors may enter. The first occurs in the conversion of sensor output into an observation vector in the sensor frame. The second occurs in the transformation of the vector from the sensor frame to the spacecraft attitude reference frame. This paper presents postlaunch alignment and transfer function calibration of the attitude sensors for the Compton Gamma Ray Observatory (GRO), the Upper Atmosphere Research Satellite (UARS), and the Extreme Ultraviolet Explorer (EUVE).

Davis, W.↗