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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 1,531 records · Page 85

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NASA NTRS1978

Study of aerodynamic technology for VSTOL fighter/attack aircraft: Vertical attitude concept

The aerodynamic technology for a vertical attitude VSTOL (VATOL) supersonic fighter/attack aircraft was studied. The selected configuration features a tailless clipped delta wing with leading-edge extension (LEX), maneuvering flaps, top-side inlet, twin dry engines and vectoring nozzles. A relaxed static stability is employed in conjunction with the maneuvering flaps to optimize transonic performance and minimize supersonic trim drag. Control for subaerodynamic flight is obtained by gimballing the nozzles in combination with wing tip jets. Emphasis is placed on the development of aerodynamic characteristics and the identification of aerodynamic uncertainties. A wind tunnel test program is proposed to resolve these uncertainties and ascertain the feasibility of the conceptual design. Ship interface, flight control integration, crew station concepts, advanced weapons, avionics, and materials are discussed.

Gerhardt, H. A.↗

NASA NTRS1987

Study of wind change for the development of loads reduction techniques for the space shuttle

Wind change statistics are analyzed for Vandenberg AFB, California (VAFB) and Kennedy Space Center, Florida (KSC). Means and standard deviations of wind component change and vector wind change modulus within 3-9 and 9-16 km altitude bands are tabulated. The contribution to 3.5 hr wind component change by wind perturbations in various wavelength bands is evaluated. Probability distributions of maximum 3.5 hr wind change in an altitude band are presented and a model for wind change at a specified altitude is tested with data derived from six data bases from VAFB and Santa Monica, California.

Adelfang, S. I.↗

NASA NTRS1987

Concurrent and vectorized mixed time, explicit nonlinear structural dynamics algorithms

A nonlinear structural dynamics program with an element library that exploits parallel processing is described. The aim is to exploit scheduling-allocation so that parallel processing and vectorization can effectively be treated in a general purpose program with explicit time integration and different time steps in different parts of the mesh. The program uses an element group scheme, which, as a by-product, also provides an automatic scheme for assigning different time steps to different parts of the mesh. The program has been tested on the Alliant FX/8; it shows a fivefold improvement in speed over compiler optimization.

Belytschko, Ted↗

NASA NTRS1988

The surface integral approach to radarclinometry

A radar image of the Lake Champlain West quadrangle in the Adirondack Mountains of the U.S. is synthesized and used to test the surface integral approach to radarclinometry. It is shown that the surface integral approach to radarclinometry possesses an inherent instability that can be avoided only if the radar reflectance function possesses a shallow slope over the range of operation and if terrain slopes are bounded to prevent their being either parallel or perpendicular to the Poynting vector of the radar irradiance. It is found that the noise associated with real SAR systems makes this instability worse. It is concluded that the value of the surface integral approach to radarclinometry shows little promise.

Wildey, Robert L.↗

NASA NTRS1991

Comparison of straight and 15 degree vectored nozzles using a six component thrust stand

This project compared the forces and moments produced by straight and 15 degree vectored nozzles. Using the six component thrust stand in the engines laboratory at California Polytechnic State University, several trials were performed. This data was then reduced using first a computer program and then later an electronic spreadsheet. This reduced data was graphed and compared. As a result of these comparisons some unexpected forces were discovered. Several more tests were run including a zero thrust test and a statistical comparison were done to discover the source of these discrepancies. As a direct result several nozzle changes were made and significant revisions to the thrust stand are being made.

Carpenter, Thomas W.↗

NASA NTRS2004

AveBoost2: Boosting for Noisy Data

AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the pre- vious base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. In previous work, we developed an algorithm, AveBoost, that constructed distributions orthogonal to the mistake vectors of all the previous models, and then averaged them to create the next base model s distribution. Our experiments demonstrated the superior accuracy of our approach. In this paper, we slightly revise our algorithm to allow us to obtain non-trivial theoretical results: bounds on the training error and generalization error (difference between training and test error). Our averaging process has a regularizing effect which, as expected, leads us to a worse training error bound for our algorithm than for AdaBoost but a superior generalization error bound. For this paper, we experimented with the data that we used in both as originally supplied and with added label noise-a small fraction of the data has its original label changed. Noisy data are notoriously difficult for AdaBoost to learn. Our algorithm's performance improvement over AdaBoost is even greater on the noisy data than the original data.

Oza, Nikunj C.↗

NASA NTRS2008

Fiber Optic Sensing Monitors Strain and Reduces Costs

In applications where stress on a structure may vary widely and have an unknown impact on integrity, a common engineering strategy has been overbuilding to ensure a sufficiently robust design. While this may be appropriate in applications where weight concerns are not paramount, space applications demand a bare minimum of mass, given astronomical per-pound launch costs. For decades, the preferred solution was the tactic of disassembly and investigation between flights. Knowing there must be a better way, Dr. Mark Froggatt, of Langley Research Center, explored alternate means of monitoring stresses and damage to the space shuttle. While a tear-it-apart-and-have-a-look strategy was effective, it was also a costly and time consuming process that risked further stresses through the very act of disassembly and reassembly. An alternate way of monitoring the condition of parts under the enormous stresses of space flight was needed. Froggatt and his colleagues at Langley built an early-warning device to provide detailed information about even minuscule cracks and deformations by etching a group of tiny lines, or grating, on a fiber optic cable five-thousandths of an inch thick with ultraviolet light. By then gluing the fiber to the side of a part, such as a fuel tank, and shining a laser beam down its length, reflected light indicated which gratings were under stress. Inferring this data from measurements in light rather than in bonded gauges saved additional weight. Various shuttle components now employ the ultrasonic dynamic vector stress sensor (UDVSS), allowing stress detection by measuring light beamed from a built-in mini-laser. By measuring changes in dynamic directional stress occurring in a material or structure, and including phase-locked loop, synchronous amplifier, and contact probe, the UDVSS proved especially useful among manufacturers of aerospace and automotive structures for stress testing and design evaluation. Engineers could ensure safety in airplanes and spaceships with a narrower, not overbuilt, margin of safety. For this development, in 1997, Discover Magazine named Froggatt a winner in the "Eighth Annual Awards for Technological Innovation" from more than 4,000 entries.

Source record↗

NASA NTRS2016

Orion GN&C Detection and Mitigation of Parachute Pendulosity

New techniques being employed by Orion guidance, navigation, and control (GN&C) using a reaction control system (RCS) under parachutes are described. Pendulosity refers to a pendulum-oscillatory mode that can occur during descent under main parachutes and that has been observed during Orion parachute drop tests. The pendulum mode reduces the ability of GN&C to maneuver the suspended vehicle resulting in undesirable increases to structural loads at touchdown. Parachute redesign efforts have been unsuccessful in reducing the pendulous behavior necessitating GN&C mitigation options. An observer has been developed to estimate the pendulum motion as well as the underlying wind velocity vector. Using this knowledge, the control system maneuvers the vehicle using two separate strategies determined by wind velocity magnitude and pendulum energy thresholds; at high wind velocities the vehicle is aligned with the wind direction and for cases with lower wind velocities and large pendulum amplitudes the vehicle is aligned such that it is perpendicular to the swing plane. Pendulum damping techniques using RCS thrusters are discussed but have not been selected for use onboard the Orion spacecraft. The techniques discussed in this paper will be flown on Exploration Mission 1 (EM-1).

Kane, Mark A.↗

NASA NTRS2001

Spatial scale of motion segmentation from speed cues

For the accurate perception of multiple, potentially overlapping, surfaces or objects, the visual system must distinguish different local motion vectors and selectively integrate similar motion vectors over space to segment the retinal image properly. We recently showed that large differences in speed are required to yield a percept of motion transparency. In the present study, to investigate the spatial scale of motion segmentation from speed cues alone, we measured the speed-segmentation threshold (the minimum speed difference required for 75% performance accuracy) for 'corrugated' random-dot patterns, i.e. patterns in which dots with two different speeds were alternately placed in adjacent bars of variable width. In a first experiment, we found that, at large bar widths, a smaller speed difference was required to segment and perceive the corrugated pattern of moving dots, while at small bar-widths, a larger speed difference was required to segment the two speeds and perceive two transparent surfaces of moving dots. Both the perceptual and segmentation performance transitions occurred at a bar width of around 0.4 degrees. In a second experiment, speed-segmentation thresholds were found to increase sharply when dots with different speeds were paired within a local pooling area. The critical pairing distance was about 0.2 degrees in the fovea and increased linearly with stimulus eccentricity. However, across the range of eccentricities tested (up to 15 degrees ), the critical pairing distance did not change much and remained close to the receptive field size of neurons within the primate primary visual cortex. In a third experiment, increasing dot density changed the relationship between speed-segmentation thresholds and bar width. Thresholds decreased for large bar widths, but increased for small bar widths. All of these results are well fit by a simple stochastic model, which estimates the probabilities of having identical or different motion vectors within a local pooling area whose size is the same as that of primate V1 neurons. Altogether, these results demonstrate that speed-based segmentation can function well, even at small spatial scales (i.e. high-spatial frequencies of spatial corrugation) and thereby emphasizes the critical role of a local pooling process early in the cortical motion-processing pathway.

NASA Discipline Neuroscience↗

DOE OSTI2019

Surveillance of Aedes aegypti indoors and outdoors using Autocidal Gravid Ovitraps in South Texas during local transmission of Zika virus, 2016 to 2018

The yellow fever mosquito, Aedes aegypti, has facilitated the re-emergence of dengue virus (DENV) and emergence of chikungunya virus (CHIKV) and Zika virus (ZIKV) in the Americas and the Caribbean. The recent transmission of these arboviruses in the continental United States has been limited, to date, to South Florida and South Texas despite Ae. aegypti occurring over a much larger geographical region within the country. The main goal of our study was to provide the first long term longitudinal study of Ae. aegypti and enhance the knowledge about the indoor and outdoor relative abundance of Ae. aegypti as a proxy for mosquito-human contact in South Texas, a region of the United States that is at high risk for mosquito-borne virus transmission. Here, the relative abundance of indoors and outdoors mosquitoes of households in eight different communities was described. Surveillance was done weekly from September 2016 to April 2018 using the CDC Autocidal Gravid Ovitraps in low- and middle-income communities. A total of 69 houses were included in this survey among which 36 were in the low-income communities (n = 11 for Donna, n = 15 for Progresso, n = 5 for Mesquite, n = 5 for Chapa) and 33 in middle-income communities (n = 9 for La Feria, n = 8 for Weslaco, n = 11 for McAllen, and n = 5 for Rio Rico). Overall, Ae. aegypti was the dominant species (59.2% of collections, n = 7255) followed by Culex spp. mosquitoes (27.3% of collections, n = 3350). Furthermore, we demonstrated for Ae. aegypti that 1) outdoor relative abundance was higher compared to indoor relative abundance, 2) low-income communities were associated with an increase in mosquito relative abundance indoors when compared to middle-income communities, 3) no difference was observed in the number of mosquitoes collected outdoors between low-income and middle-income communities, and 4) warmer months were positively correlated with outdoor relative abundance whereas no seasonality was observed in the relative abundance of mosquitoes indoors. Additionally, Ae. aegypti mosquitoes collected in South Texas were tested using a specific ZIKV/CHIKV multiplex real-time PCR assay, however, none of the mosquitoes tested positive. Our data highlights the occurrence of mosquitoes indoors in the continental United States and that adults are collected nearly every week of the calendar year. These mosquito data, obtained concurrently with local ZIKV transmission of 10 locally acquired cases in nearby communities, represent a baseline for future studies in the Lower Rio Grande Valley (LRGV) including vector control interventions relying on the oviposition behavior to reduce mosquito populations and pathogen transmission.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces

Here, this paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C 5 H 5 using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree–Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Automatic Partition of Orbital Spaces Based on Singular Value Decomposition in the Context of Embedding Theories

Here, we present a simple approach for orbital space partitioning to be employed in the projection-based embedding theory developed by Goodpaster and co-workers [Manby et al. J. Chem. Theory Comput. 2012, 8, 2564]. Once the atoms are assigned to the desired subspaces, the molecular orbitals are projected onto the atomic orbitals centered on active atoms and then singular value decomposed. The right singular vectors are used to rotate the initial molecular orbitals, taking the largest gap in the singular values spectrum to define the most suitable partition of the occupied orbital space. This scheme is free from numerical parameters, contrary to the Mulliken charge threshold or the completeness criterion previously used. The performance of this new prescription is assessed in a test set of several distinct reactions, the deprotonation of decanoic acid, the Diels-Alder reaction of 1,3-butadiene and octadecanonaene, the torsional potential of a retinal derivative, and the critical points along the reaction coordinate of an instance of the Menshutkin S N2 reaction inside a carbon nanotube.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Tokamak ITG-KBM transition benchmarking with the mixed variables/pullback transformation electromagnetic gyrokinetic scheme

Electromagnetic gyrokinetic simulation of high temperature plasma is required to predict confinement in magnetic fusion devices and has posed challenges for existing codes. In this paper, we demonstrate successful global gyrokinetic simulation of the ion temperature gradient-driven mode-kinetic ballooning mode transition in a toroidal fusion plasma test case using the mixed variables/pullback transformation (MV/PT) scheme with the particle-in-cell codes XGC and ORB5, and compare to results from a conventional continuum code from the literature. Furthermore, the MV/PT scheme combines explicit time integration with mitigation of the well-known electromagnetic gyrokinetic “cancelation problem.” We calculate eigenmodes in the electrostatic and parallel vector potentials, and find good agreement in growth rate, real frequency, and the normalized plasma pressure of mode transition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

The 3D Lyman- α forest power spectrum from eBOSS DR16

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman-α (Ly α) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of ~205 000 quasar spectra in the redshift range 2 ≤ z ≤ 4 at an effective redshift z = 2.334. We propose a pair-count spectral estimator in configuration space, weighting each pair by exp( i k ∙ r), for wave vector k and pixel pair separation r, effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly α skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly α skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range $(0.02 ≤ k ≤ 0.35)$ h Mpc -1 at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly α forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

VipariNama: RNA viral vectors to rapidly elucidate the relationship between gene expression and phenotype

Synthetic transcription factors have great promise as tools to help elucidate relationships between gene expression and phenotype by allowing tunable alterations of gene expression without genomic alterations of the loci being studied. However, the years-long timescales, high cost, and technical skill associated with plant transformation have limited their use. In this work, we developed a technology called VipariNama (ViN) in which vectors based on the tobacco rattle virus are used to rapidly deploy Cas9-based synthetic transcription factors and reprogram gene expression in planta. We demonstrate that ViN vectors can implement activation or repression of multiple genes systemically and persistently over several weeks in Nicotiana benthamiana, Arabidopsis (Arabidopsis thaliana), and tomato (Solanum lycopersicum). By exploring strategies including RNA scaffolding, viral vector ensembles, and viral engineering, we describe how the flexibility and efficacy of regulation can be improved. We also show how this transcriptional reprogramming can create predictable changes to metabolic phenotypes, such as gibberellin biosynthesis in N. benthamiana and anthocyanin accumulation in Arabidopsis, as well as developmental phenotypes, such as plant size in N. benthamiana, Arabidopsis, and tomato. These results demonstrate how ViN vector-based reprogramming of different aspects of gibberellin signaling can be used to engineer plant size in a range of plant species in a matter of weeks. In summary, ViN accelerates the timeline for generating phenotypes from over a year to just a few weeks, providing an attractive alternative to transgenesis for synthetic transcription factor-enabled hypothesis testing and crop engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Validation of deformable image registration algorithms on CT images of ex vivo porcine bladders with fiducial markers

Purpose: The spatial accuracy of deformable image registration (DIR) is important in the implementation of image guided adaptive radiotherapy techniques for cancer in the pelvic region. Validation of algorithms is best performed on phantoms with fiducial markers undergoing controlled large deformations. Excised porcine bladders, exhibiting similar filling and voiding behavior as human bladders, provide such an environment. The aim of this study was to determine the spatial accuracy of different DIR algorithms on CT images ofex vivo porcine bladders with radiopaque fiducial markers applied to the outer surface, for a range of bladder volumes, using various accuracy metrics. Methods: Five excised porcine bladders with a grid of 30–40 radiopaque fiducial markers attached to the outer wall were suspended inside a water-filled phantom. The bladder was filled with a controlled amount of water with added contrast medium for a range of filling volumes (100–400 ml in steps of 50 ml) using a luer lock syringe, and CT scans were acquired at each filling volume. DIR was performed for each data set, with the 100 ml bladder as the reference image. Six intensity-based algorithms (optical flow or demons-based) implemented in theMATLAB platform DIRART, a b-spline algorithm implemented in the commercial software package VelocityAI, and a structure-based algorithm (Symmetric Thin Plate Spline Robust Point Matching) were validated, using adequate parameter settings according to values previously published. The resulting deformation vector field from each registration was applied to the contoured bladder structures and to the marker coordinates for spatial error calculation. The quality of the algorithms was assessed by comparing the different error metrics across the different algorithms, and by comparing the effect of deformation magnitude (bladder volume difference) per algorithm, using the Independent Samples Kruskal-Wallis test. Results: The authors found good structure accuracy without dependency on bladder volume difference for all but one algorithm, and with the best result for the structure-based algorithm. Spatial accuracy as assessed from marker errors was disappointing for all algorithms, especially for large volume differences, implying that the deformations described by the registration did not represent anatomically correct deformations. The structure-based algorithm performed the best in terms of marker error for the large volume difference (100–400 ml). In general, for the small volume difference (100–150 ml) the algorithms performed relatively similarly. The structure-based algorithm exhibited the best balance in performance between small and large volume differences, and among the intensity-based algorithms, the algorithm implemented in VelocityAI exhibited the best balance. Conclusions: Validation of multiple DIR algorithms on a novel physiological bladder phantom revealed that the structure accuracy was good for most algorithms, but that the spatial accuracy as assessed from markers was low for all algorithms, especially for large deformations. Hence, many of the available algorithms exhibit sufficient accuracy for contour propagation purposes, but possibly not for accurate dose accumulation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Reference Computational Meshing Strategy for Computational Fluid Dynamics Simulation of Departure from Nucleate Boiling

The objective of this effort is to establish a strategy and process for generation of suitable computational mesh for computational fluid dynamics simulations of departure from nucleate boiling in a 5 by 5 fuel rod assembly held in place by PWR mixing vane spacer grids. This mesh generation process will support ongoing efforts to develop, demonstrate and validate advanced multi-phase computational fluid dynamics methods that enable more robust identification of dryout conditions and DNB occurrence.Building upon prior efforts and experience, multiple computational meshes were developed using the native mesh generation capabilities of the commercial CFD code STAR-CCM+. These meshes were used to simulate two test cases from the Westinghouse 5 by 5 rod bundle facility. The sensitivity of predicted quantities of interest to the mesh resolution was then established using two evaluation methods, the Grid Convergence Index method and the Least Squares method. This evaluation suggests that the Least Squares method can reliably establish the uncertainty associated with local parameters such as vector velocity components at a point in the domain or surface averaged quantities such as outlet velocity magnitude. However, neither method is suitable for characterization of uncertainty in global extrema such as peak fuel surface temperature, primarily because such parameters are not necessarily associated with a fixed point in space. This shortcoming is significant because the current generation algorithm for identification of DNB event conditions relies on identification of such global extrema. Ongoing efforts to identify DNB based on local surface conditions will address this challenge.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗