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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Mind Your Ps and Qs: The Interrelation between Period (P) and Mass-ratio (Q) Distributions of Binary Stars

We compile observations of early-type binaries identified via spectroscopy, eclipses, long-baseline interferometry, adaptive optics, common proper motion, etc. Each observational technique is sensitive to companions across a narrow parameter space of orbital periods P and mass ratios q = M{sub comp}/M {sub 1}. After combining the samples from the various surveys and correcting for their respective selection effects, we find that the properties of companions to O-type and B-type main-sequence (MS) stars differ among three regimes. First, at short orbital periods P ≲ 20 days (separations a ≲ 0.4 au), the binaries have small eccentricities e ≲ 0.4, favor modest mass ratios 〈q〉≈0.5, and exhibit a small excess of twins q > 0.95. Second, the companion frequency peaks at intermediate periods log P (days) ≈ 3.5 (a ≈ 10 au), where the binaries have mass ratios weighted toward small values q ≈ 0.2–0.3 and follow a Maxwellian “thermal” eccentricity distribution. Finally, companions with long orbital periods log P (days) ≈ 5.5–7.5 (a ≈ 200–5000 au) are outer tertiary components in hierarchical triples and have a mass ratio distribution across q ≈ 0.1–1.0 that is nearly consistent with random pairings drawn from the initial mass function. We discuss these companion distributions and properties in the context of binary-star formation and evolution. We also reanalyze the binary statistics of solar-type MS primaries, taking into account that 30% ± 10% of single-lined spectroscopic binaries likely contain white dwarf companions instead of low-mass stellar secondaries. The mean frequency of stellar companions with q > 0.1 and log P (days) < 8.0 per primary increases from 0.50 ± 0.04 for solar-type MS primaries to 2.1 ± 0.3 for O-type MS primaries. We fit joint probability density functions f(M{sub 1},q,P,e)≠f(M{sub 1})f(q)f(P)f(e) to the corrected distributions, which can be incorporated into binary population synthesis studies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

Spacelab J: Microgravity and life sciences

Spacelab J is a joint venture between NASA and the National Space Development Agency of Japan (NASDA). Using a Spacelab pressurized long module, 43 experiments will be performed in the areas of microgravity and life sciences. These experiments benefit from the microgravity environment available on an orbiting Shuttle. Removed from the effects of gravity, scientists will seek to observe processes and phenomena impossible to study on Earth, to develop new and more uniform mixtures, to study the effects of microgravity and the space environment on living organisms, and to explore the suitability of microgravity for certain types of research. Mission planning and an overview of the experiments to be performed are presented. Orbital research appears to hold many advantages for microgravity science investigations, which on this mission include electronic materials, metals and alloys, glasses and ceramics, fluid dynamics and transport phenomena, and biotechnology. Gravity-induced effects are eliminated in microgravity. This allows the investigations on Spacelab J to help scientists develop a better understanding of how these gravity-induced phenomena affect both processing and products on Earth and to observe subtle phenomena that are masked in gravity. The data and samples from these investigations will not only allow scientists to better understand the materials but also will lead to improvements in the methods used in future experiments. Life sciences research will collect data on human adaptation to the microgravity environment, investigate ways of assisting astronauts to readapt to normal gravity, explore the effects of microgravity and radiation on living organisms, and gather data on the fertilization and development of organisms in the absence of gravity. This research will improve crew comfort and safety on future missions while helping scientists to further understand the human body.

Source record↗
NASA NTRS2002

Rapid, specific determination of iodine and iodide by combined solid-phase extraction/diffuse reflectance spectroscopy

A new, rapid methodology for trace analysis using solid-phase extraction is described. The two-step methodology is based on the concentration of an analyte onto a membrane disk and on the determination by diffuse reflectance spectroscopy of the amount of analyte extracted on the disk surface. This method, which is adaptable to a wide range of analytes, has been used for monitoring ppm levels of iodine and iodide in spacecraft water. Iodine is used as a biocide in spacecraft water. For these determinations, a water sample is passed through a membrane disk by means of a 10-mL syringe that is attached to a disk holder assembly. The disk, which is a polystyrene-divinylbenzene composite, is impregnated with poly(vinylpyrrolidone) (PVP), which exhaustively concentrates iodine as a yellow iodine-PVP complex. The amount of concentrated iodine is then determined in only 2 s by using a hand-held diffuse reflectance spectrometer by comparing the result with a calibration curve based on the Kubelka-Munk function. The same general procedure can be used to determine iodide levels after its facile and exhaustive oxidation to iodine by peroxymonosulfate (i.e., Oxone reagent). For samples containing both analytes, a two-step procedure can be used in which the iodide concentration is calculated from the difference in iodine levels before and after treatment of the sample with peroxymonosulfate. With this methodology, iodine and iodide levels in the 0.1-5.0 ppm range can be determined with a total workup time of approximately 60 s with a RSD of approximately 6%.

Non-NASA Center↗
NASA NTRS2009

Jet Noise Modeling for Suppressed and Unsuppressed Aircraft in Simulated Flight

This document describes the development of further extensions and improvements to the jet noise model developed by Modern Technologies Corporation (MTC) for the National Aeronautics and Space Administration (NASA). The noise component extraction and correlation approach, first used successfully by MTC in developing a noise prediction model for two-dimensional mixer ejector (2DME) nozzles under the High Speed Research (HSR) Program, has been applied to dual-stream nozzles, then extended and improved in earlier tasks under this contract. Under Task 6, the coannular jet noise model was formulated and calibrated with limited scale model data, mainly at high bypass ratio, including a limited-range prediction of the effects of mixing-enhancement nozzle-exit chevrons on jet noise. Under Task 9 this model was extended to a wider range of conditions, particularly those appropriate for a Supersonic Business Jet, with an improvement in simulated flight effects modeling and generalization of the suppressor model. In the present task further comparisons are made over a still wider range of conditions from more test facilities. The model is also further generalized to cover single-stream nozzles of otherwise similar configuration. So the evolution of this prediction/analysis/correlation approach has been in a sense backward, from the complex to the simple; but from this approach a very robust capability is emerging. Also from these studies, some observations emerge relative to theoretical considerations. The purpose of this task is to develop an analytical, semi-empirical jet noise prediction method applicable to takeoff, sideline and approach noise of subsonic and supersonic cruise aircraft over a wide size range. The product of this task is an even more consistent and robust model for the Footprint/Radius (FOOTPR) code than even the Task 9 model. The model is validated for a wider range of cases and statistically quantified for the various reference facilities. The possible role of facility effects will thus be documented. Although the comparisons that can be accomplished within the limited resources of this task are not comprehensive, they provide a broad enough sampling to enable NASA to make an informed decision on how much further effort should be expended on such comparisons. The improved finalized model is incorporated into the FOOTPR code. MTC has also supported the adaptation of this code for incorporation in NASA s Aircraft Noise Prediction Program (ANOPP).

Stone, James R.↗
NASA NTRS2011

Biochip microsystem for bioinformatics recognition and analysis

A system with applications in pattern recognition, or classification, of DNA assay samples. Because DNA reference and sample material in wells of an assay may be caused to fluoresce depending upon dye added to the material, the resulting light may be imaged onto an embodiment comprising an array of photodetectors and an adaptive neural network, with applications to DNA analysis. Other embodiments are described and claimed.

Lue, Jaw-Chyng↗
NASA NTRSDate not supplied

Portable Flow Device Using Fourier Ptychography Microscopy and Deep Learning for Detection of Biosignatures

A proof-of-concept, compact, portable Fourier Ptychographic Microscope (FPM) to perform wide field-of-view, high spatial resolution imaging (<1 μm) for biosignature motility in liquid samples, is presented. The FPM has the potential to be developed as a space-based payload for future landers destined to the Ocean Worlds. A portable FPM using an existing Fourier ptychography (FP) algorithm adapted for reconstruction is demonstrated. A NVIDIA Jetson Nano board and camera combined with FP, is used to computationally reconstruct sub-micron resolution images. Additionally, deep learning was employed to perform inferencing prediction which enables the on-edge FPM device.

Fourier↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
DOE OSTI2022

Signal-Compensation-Based Adaptive PID Control for Fused Magnesia Smelting Processes

For fused magnesia smelting processes, the proportional-integral-derivative (PID) controller is difficult to regulate the smelting currents within desired ranges due to unexpected variations of the dynamics caused by material feeding operations. To solve this problem, we first proposed its dynamic model composed of a linear model and an uncertain nonlinear dynamic system that describes the unknown time-varying dynamics of nonlinearity and strong couplings among controlled variables. Then, using the accurately calculated value of the uncertain part at the previous sampling time instant and the tracking error that reflects the influence of its change rate, we developed a feedforward and a one-step optimal control to obtain two signal compensators to eliminate the impact of frequent variations of the uncertainties. For the compensators, we proposed a weight selection method to minimize the sum of the squared tracking error with ensured closed-loop stability. Based on the abovementioned modeling and control structure, we finally developed a novel adaptive PID controller augmented with signal compensators to control the whole process. Industrial applications have shown that the proposed method can always keep the tracking error and the control input within their targeted ranges, leading to a significantly reduced energy consumption per ton and increased fused magnesia production.

Wang, Weizhou↗
NASA NTRS2010

Automated, Miniaturized Instrument for Measuring Gene Expression in Space

To facilitate astrobiological studies on the survival and adaptation of microorganisms and mixed microbial cultures to space environment, we have been developing a fully automated, miniaturized system for measuring their gene expression on small spacecraft. This low-cost, multi-purpose instrument represents a major scientific and technological advancement in our ability to study the impact of the space environment on biological systems by providing data on cellular metabolism and regulation orders of magnitude richer than what is currently available. The system supports growth of the organism, lyse it to release the expressed RNA, label the RNA, read the expression levels of a large number of genes by microarray analysis of labeled RNA and transmit the measurements to Earth. To measure gene expression we use microarray technology developed by CombiMatrix, which is based on electrochemical reactions on arrays of electrodes on a semiconductor substrate. Since the electrical integrity of the microarray remains intact after probe synthesis, the circuitry can be employed to sense nucleic acid binding at each electrode. CombiMatrix arrays can be sectored to allow multiple samples per chip. In addition, a single array can be used for several assays. The array has been integrated into an automated microfluidic cartridge that uses flexible reagent blisters and pinch pumping to move liquid reagents between chambers. The proposed instrument will help to understand adaptation of terrestrial life to conditions beyond the planet of origin, identify deleterious effects of the space environment, develop effective countermeasures against these effects, and test our ability to sustain and grow in space organisms that can be used for life support and in situ resource utilization during long-duration space exploration. The instrument is suitable for small satellite platforms, which provide frequent, low cost access to space. It can be also used on any other platform in space, including the ISS. It can be replicated and used with only small modifications in multiple biological experiments with a broad range of goals in mind.

Pohorille, A.↗
NASA NTRS1988

Temporal and spatial adaptive algorithm for reacting flows

A numerical integration scheme for quasi-one-dimensional unsteady flows with finite-rate chemistry is developed and demonstrated. The governing Euler and species-conservation equations are derived; the integration method and the spatial and temporal grid embedding techniques are explained; and results for sample problems involving stream-tube flow with one dissociating gas, shock-tube flow with one dissociating gas, and diverging-channel flow with multiple reactions are presented in extensive graphs and briefly characterized. Accuracy comparable to that of globally fine grid solutions is obtained with significant savings in CPU time.

Pervaiz, Mehtab M.↗
DOE OSTI2024

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

Abstract We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

Heinrich, Lukas (ORCID:0000000240487584)↗
DOE OSTI2015

Photon-in photon-out hard X-ray spectroscopy at the Linac Coherent Light Source

X-ray free-electron lasers (FELs) have opened unprecedented possibilities to study the structure and dynamics of matter at an atomic level and ultra-fast timescale. Many of the techniques routinely used at storage ring facilities are being adapted for experiments conducted at FELs. In order to take full advantage of these new sources several challenges have to be overcome. They are related to the very different source characteristics and its resulting impact on sample delivery, X-ray optics, X-ray detection and data acquisition. Here it is described how photon-in photon-out hard X-ray spectroscopy techniques can be applied to study the electronic structure and its dynamics of transition metal systems with ultra-bright and ultra-short FEL X-ray pulses. In particular, some of the experimental details that are different compared with synchrotron-based setups are discussed and illustrated by recent measurements performed at the Linac Coherent Light Source.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Ecophysiological and genomic analyses of a representative isolate of highly abundant Bacillus cereus strains in contaminated subsurface sediments

Abstract Bacillus cereus strain CPT56D‐587‐MTF (CPTF) was isolated from the highly contaminated Oak Ridge Reservation (ORR) subsurface. This site is contaminated with high levels of nitric acid and multiple heavy metals. Amplicon sequencing of the 16S rRNA genes (V4 region) in sediment from this area revealed an amplicon sequence variant (ASV) with 100% identity to the CPTF 16S rRNA sequence. Notably, this CPTF‐matching ASV had the highest relative abundance in this community survey, with a median relative abundance of 3.77% and comprised 20%–40% of reads in some samples. Pangenomic analysis revealed that strain CPTF has expanded genomic content compared to other B. cereus species—largely due to plasmid acquisition and expansion of transposable elements. This suggests that these features are important for rapid adaptation to native environmental stressors. We connected genotype to phenotype in the context of the unique geochemistry of the site. These analyses revealed that certain genes (e.g. nitrate reductase, heavy metal efflux pumps) that allow this strain to successfully occupy the geochemically heterogenous microniches of its native site are characteristic of the B. cereus species while others such as acid tolerance are mobile genetic element associated and are generally unique to strain CPTF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Tug and payload-to-Orbiter interface requirements

This paper presents results of a study to identify, evaluate, and develop Tug plus payload-to-Orbiter accommodation requirements. Interface areas include structural, mechanical, fluids, environmental, avionics, and safety. Each interface area was investigated to determine the best physical and operational method of accomplishing the required functions with an overriding goal of establishing sample and flexible interface requirements suitable for Tug, Tug payloads, and other cargo. The recommended system for supporting the Tug and its payload from the Orbiter employs a cylindrical load-carrying structure called a deployment adapter that has a rotational capability for Tug deployment. The design and operation of the deployment adapter and other peripheral interface equipment such as crew compartment monitor and control panels and cargo bay fluid and electrical interface panels are discussed.

Bock, E. H.↗
NASA NTRS1991

The NASA/MSFC global reference atmospheric model: 1990 version (GRAM-90). Part 1: Technical/users manual

A technical description of the NASA/MSFC Global Reference Atmospheric Model 1990 version (GRAM-90) is presented with emphasis on the additions and new user's manual descriptions of the program operation aspects of the revised model. Some sample results for the new middle atmosphere section and comparisons with results from a three dimensional circulation model are provided. A programmer's manual with more details for those wishing to make their own GRAM program adaptations is also presented.

Justus, C. G.↗