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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

A Tail Buffet Loads Prediction Method for Aircraft at High Angles of Attack

Aircraft designers commit significant resources to the design of aircraft in meeting performance goals. Despite fulfilling traditional design requirements, many fighter aircraft have encountered buffet loads when demonstrating their high angle-of-attack maneuver capabilities. As a result, during test or initial production phases of fighter development programs, many new designs are impacted, usually in a detrimental way, by resulting in reassessing designs or limiting full mission capability. These troublesome experiences usually stem from overlooking or completely ignoring the effects of buffet during the design phase of aircraft. Perhaps additional requirements are necessary that addresses effects of buffet in achieving best aircraft performance in fulfilling mission goals. This paper describes a reliable, fairly simple, but quite general buffet loads analysis method to use in the initial design phases of fighter-aircraft development. The method is very similar to the random gust load analysis that is now commonly available in a commercial code, which this analysis capability is based, with some key modifications. The paper describes the theory and the implementation of the methodology. The method is demonstrated on a JSF prototype example problem. The demonstration also serves as a validation of the method, since, in the paper, the analysis is shown to nearly match the flight data. In addition, the paper demonstrates how the analysis method can be used to assess candidate design concepts in determining a satisfactory final aircraft configuration.

Pototzky, Anthony S.↗
NASA NTRS1987

Use of a minimum rate of change formalism to quantify variability of extragalactic X-ray sources

A method to obtain rigorous, quantitative constraints on the rate of variability is suggested. The method is motivated by the case when the existence of time variability is unequivocal, but the statistical uncertainties are too large to apply more direct methods such as power spectrum analysis. Conceptually the data are fitted to a function of time and a set of free parameters. The method of Lagrange multipliers is used to solve for that parameter set which minimizes the value of the derivative at time t(m). The result can be related physically to the minimum efficiency with which rest mass must be converted to radiation energy. Slightly different formulations of the general principal are used to estimate maximum source size scales associated with the variability.

Schwartz, Daniel A.↗
NASA NTRS1973

Use of the single-breath method of estimating cardiac output during exercise-stress testing.

The single-breath cardiac output measurement technique of Kim et al. (1966) has been modified for use in obtaining cardiac output measurements during exercise-stress tests on Apollo astronauts. The modifications involve the use of a respiratory mass spectrometer for data acquisition and a digital computer program for data analysis. The variation of the modified method for triplicate steady-state cardiac output measurements was plus or minus 1 liter/min. The combined physiological and methodological variation seen during a set of three exercise tests on a series of subjects was 1 to 2.5 liter/min. Comparison of the modified method with the direct Fick technique showed that although the single-breath values were consistently low, the scatter of data was small and the correlation between the two methods was high. Possible reasons for the low single-breath cardiac output values are discussed.

Buderer, M. C.↗
DOE OSTI2021

The XFaster Power Spectrum and Likelihood Estimator for the Analysis of Cosmic Microwave Background Maps

We present the XFaster analysis package, XFaster is a fast, iterative angular power spectrum estimator based on a diagonal approximation to the quadratic Fisher matrix estimator. XFaster uses Monte Carlo simulations to compute noise biases and filter transfer functions and is thus a hybrid of both Monte Carlo and quadratic estimator methods. In contrast to conventional pseudo-C ℓ based methods, the algorithm described here requires a minimal number of simulations, and does not require them to be precisely representative of the data to estimate accurate covariance matrices for the bandpowers. The formalism works with polarization-sensitive observations and also data sets with identical, partially overlapping, or independent survey regions. The method was first implemented for the analysis of BOOMERanG data (Netterfield et al. 2002; Jones et al. 2006), and also used as part of the Planck analysis (Rocha et al. 2011). Here, we describe the full, publicly available analysis package, written in Python, as developed for the analysis of data from the 2015 flight of the SPIDER instrument (SPIDER Collaboration 2021). The package includes extensions for self-consistently estimating null spectra and for estimating fits for Galactic foreground contributions. We show results from the extensive validation of XFaster using simulations, and its application to the SPIDER data set.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

High-Content Clonogenic Survival Screen to Identify Chemoradiation Sensitizers

The combination of cytotoxic chemotherapy with radiation therapy (CRT) has resulted in significant improvements in clinical outcomes for patients with many locally advanced unresectable cancers. Only a small proportion of patients achieve pathologic complete responses to CRT; combination of CRT with targeted agents offers the promise of further improving treatment responses. However, numerous clinical trials have failed to show an improvement in clinical outcomes with the addition of targeted agents. To increase the accessibility of our screening method and accelerate the pace at which novel combinations with CRT are identified and incorporated into standard practices for treatments, we report details on screening method optimization, data generation, and downstream data analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of N scans = 139 SCXI images across N exp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Machine learning of high dimensional data on a noisy quantum processor

Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google’s universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distributed Acoustic Sensing (DAS) on Opportunistic Networks_ A Feasibility Study Utilizing Fiber Optic Infrastructure at NEON Sites for DAS

This study demonstrates the feasibility of using Distributed Acoustic Sensing (DAS) on the opportunistic optical fiber infrastructure presented by NEON (National Ecological Observatory Network) sites. The test took place at the NEON Terrestrial Site designated D10 CPER. Unlit portions of the local fiber network were successfully spliced together and optimized for length and a Silixa iDAS sensor was installed. The resulting array (composed of 12 spliced fiber runs) totaled 875m in length. Over a period of 2 weeks, we gathered a high spatial and temporal resolution data set to characterize discernable signals and understand background noise. The data set was reviewed to extract wave signatures of identifiable origin, both anthropogenic and otherwise. Continent wide distribution of these sites makes them an interesting candidate for DAS and identifying measurable signal sources informs future DAS application on opportunistic fiber networks. These observations validate the utility of both the NEON and similarly small fiber networks (<1km) for geoscience, environmental, and security DAS applications and indicate possibilities for further inquiry in methods for DAS data collection and analysis.

42 ENGINEERING↗
DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Comprehensive Chemical Fingerprinting by Multidimensional GC and Supervised Machine Learning

This project leveraged advances in machine learning based data analysis techniques and untargeted analytical methods for organic analysis to progress nuclear nonproliferation technologies beyond current capabilities. The developed approaches can be used to detect and identify complex chemical fingerprints of facilities of interest. These techniques have been developed for fields such as metabolomics and genomics but have not been applied to nuclear nonproliferation applications. Adaptation of these techniques for volatile organic compound analysis has far reaching application within the scientific community including environmental chemistry, atmospheric physics, and climate sciences.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

First Experimental Test of the Ratio Method

The ratio is a new reaction observable suggested to extract accurately structure information on halo nuclei. It corresponds to the ratio of differential cross sections for scattering and breakup, which is predicted to remove the uncertainty related to the reaction dynamics. We present here the first experimental test of the method for the 11 Be + 12 C collision at E Lab = 20A MeV performed at Texas A&M University. Differential cross sections for scattering and inclusive one-neutron breakup have been measured with the new detector array BlueSTEAl. The ratio of cross sections is very smooth and independent of the projectile-target interaction, which demonstrates the validity of the ratio method. We extend our analysis to existing 11 Be + 208 Pb data, confirming that the method works well on any target.

11Be↗
DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Validity of trace element cosmothermometer

A reply is made to previous criticisms of the claim that the range of accretion of ordinary chondrites can be narrowed to 500-420 K (from 560-405 K) using volatile metals as cosmothermometers. Questions concerning uncertainties in vapor pressure data, entropy of mixing, methods of analysis, uncertainties in condensation curves, metamorphic transport, alloy formation, condensation of Tl, condensation of In, and concordance with other thermometers (FeO contents of silicates) are addressed. It is concluded that the original claim is valid.

Anders, E.↗
NASA NTRS1976

Amino acids in modern and fossil woods

The amino acid composition and the extent of racemization in several modern and fossil woods are reported. The method of analysis is described, and data are presented on the total amino acid concentration, the amino acid ratios, and the enantiomeric ratios in each sample. It is found that the amino acid concentration per gram of dry wood decreases with age of the sample, that the extent of racemization increases with increasing age, and that the amounts of aspartic acid, threonine, and serine decrease relative to valine with increasing age. The relative racemization rates of amino acids in wood, bone, and aqueous solution are compared, and it is shown that racemization in wood is much slower than in bone or aqueous solution. Racemization results for woods from the Kalambo Falls area of Zambia are used to calculate a minimum age of 110,000 years for the transition between the Sangoan and Acheulian industries at that site. This result is shown to be consistent with numerous radiometric dates for older Acheulian sites in Africa and to compare well with geologically inferred dates for the beginning of the Eemian and the end of the Acheulian industry in southern Africa.

Lee, C.↗
NASA NTRS1988

Rheology melts and magmatic suspensions. I - Design and calibration of concentric cylinder viscometer with application to rhyolitic magma

The design and calibration of concentric cylinder viscometer for rhyolitic magma applications are described together with the methods of data reduction and error analysis. Experimental data are presented on two rhyolitic magmas (melt plus a small fraction of vapor) under conditions of varying temperature (1100-1350 C) and shear rate (0.05-13.0/sec) at 100 kPa total pressure. Data obtained include a first reported measurement of a normal stress coefficient for magma.

Spera, Frank J.↗
NASA NTRS1990

Improved Analysis Of Heat-Pulse Data

Equations derived to improve analysis of experimental data in flash method for measurement of thermal diffusivity and heat capacity. In flash method, pulse of radiant energy from flashlamp or other source deposited on front face of small, thin specimen, and temperature of rear face monitored as function of time. New equations account for both losses and exponentially decaying pulses.

Vining, C. B.↗