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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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NASA NTRS1972

Solar flare forecasts based on mm-wavelength measurements

Solar radio temperature contour maps at a wavelength of 3.3 mm are analyzed to develop a method of forecasting solar flares of classes 1N and 1B. Fundamental to the analysis is the demonstration that most of the radio sources associated with the sunspot-plage systems are not spatially resolved by the radio telescope and, hence, only the peak temperature enhancements due to these local radio sources are employed. Evidence is presented indicating that flare-prone plage regions, which belong to recurrent families of flare-productive regions, exhibit a relationship between flare production and radio temperature characteristics considerably unlike that exhibited by virgin plage regions. The results are interpreted by means of graphs which permit the inference of class 1B or 1N flare probabilities based on the peak temperature enhancements measured at 3.3 mm. In particular, the forecast obtained is the probability that a given flare-prone plage region will produce a class 1B or 1N flare (two quoted probabilities) during the period of its disk transit.

White, K. P., III↗
NASA NTRS1982

Rotational modulation of the chromospheric activity in the young solar-type star, X-1 Orionis

The IUE satellite was used to observe one of the youngest G stars (GO V) for which Duncan (1981) derives an age of 6 x 10 to the 8th power years from the Li abundance. Rotational modulation was looked for in the emission flux in the chromospheric and transition region lines of this star. Variations in the Ca 11 K-lines profile were studied with the CHF telescope at Mauna Kea. Results show that the same modulation of the emission flux of Ca 11 due to stellar rotation is present in the transition region feature of C IV and probably of He II. For other UV lines the modulation is not apparent, due to a more complex surface distribution of the active areas or supergranulation network, or a shorter lifetime of the conditions which give rise to these features, or to the uncertainities in the measured line strengths. The Mg II emission flux is constant to within + or - 3.4% implying a rather uniform distribution of Mg II emission areas. The Ca II emission not only shows a measurable variation in intensity but also variations in detailed line profile shape when observed at high resolution.

Boesgaard, A. M.↗
NASA NTRS1992

Detection of a proton beam during the impulsive phase of a stellar flare

A transient event consistent with the predicted temporal and spectral signatures of an energetic proton beam was detected in the impulsive phase of a small flare on the red dwarf star AU Microscopii. It consisted of a prominent increase in the flux in the red wing of Lyman-alpha near 1223 A, simultaneously with the peak of a flare observed in the 1206 A transition region line of Si III. The probability that the red wing event was a chance fluctuation is one chance in 2.5 x 10 exp 4. This observation represents a confirmation of the prediction by Orrall and Zirker (1976) in which downstreaming protons accelerated during the impulsive phase of a flare charge exchange with ambient neutral hydrogen and emit Lyman-alpha radiation from 1 to 15 A redward of line center.

Woodgate, Bruce E.↗
NASA NTRS2022

Multidecadal Trends in Ozone Chemistry in the Baltimore-Washington Region

Over the past four decades, policy-led reductions in anthropogenic emissions have improved air quality over the Baltimore-Washington region (BWR). Most of the improvements in meeting the ozone air quality metrics (NAAQS) did not occur until the early 2000s despite large reductions in ozone precursors (NOx, CO, and volatile organic compounds (VOCs)) in the prior decades. We use observations of ozone and ozone precursors from satellites, ground-based sites, and the 2011 DISCOVER-AQ aircraft campaign in Maryland to illustrate how ozone chemistry in the BWR evolved between 1972 and 2019. Analysis of weekday vs weekend probability of ozone exceedance indicates the BWR transitioned to the NOx-limited regime by 2000–2003. A data-constrained box model agrees with this transition period and illustrates the key roles of reduced emissions of formaldehyde (HCHO), aromatics, and other VOCs since 1996, which reduced the peak of ozone production at the time of the transition and likely prevented the BWR from experiencing worsening surface air quality as the region transitioned to NOx-limited chemistry. Analysis of satellite observations of tropospheric column HCHO to NO2 analyzed using a new approach for evaluation of chemical regimes derived from DISCOVER-AQ data also provide a consistent depiction of the timing of the transition period that we infer from ground-based observations and the box model. Finally, despite significant improvements in air quality over the past two decades, the BWR still has not met the EPA standard for surface ozone. With predominantly NOx-limited ozone chemistry over the BWR, continued decreases in emission of NOx will slow the rate of ozone production and help improve air quality. We highlight emissions of NO2 from the diesel truck fleet as a worthwhile focus for future policy because emissions from this source appear to influence day-of-week variations in observed NO2, with an accompanying effect on ozone.

Ozone↗
NASA NTRS1989

Direct calculation of the reactive transition matrix by L-squared quantum mechanical variational methods with complex boundary conditions

A new formalism of the generalized Newton variational principle for the calculation of quantum mechanical state-to-state reaction probabilities is presented. The reformulation involves solving directly for the transition matrix rather than the reactance mtrix so that calculations may be carried out for individual columns of the transition matrix without obtaining solutions for all possible initial channels. The convergence of calculations with real and complex boundary conditions are compared for H + H2 - H2 + H, O + H2 - OH + H, and O + HD - OH + D and OD + H.

Sun, Yan↗
NASA NTRS2017

The Transiting Exocomets in the HD 172555 System

The Earth is thought to have formed dry, in a part of the Solar Nebula deficient in organic material, and to have acquired its organics and water through bombardment by minor bodies. Observations of this process in well-dated systems can provide insight into the probable origin and composition of the bombarding parent bodies. Transiting cometary activity has previously been reported in Ca II for the late-A member of the 241 Myr old Pictoris Moving Group member, HD 172555(Kiefer et al. 2014). We present HST STIS and COS spectra of HD 172555 demonstrating that the star has chromospheric emission and variable in falling gas features in transitions of silicon and carbon ions at times when no Fe II absorption is seen in the UV data, and no Ca II absorption is seen in contemporary optical spectra. The lack of CO absorption and stable gas absorption at the system velocity is consistent with the absence of a cold Kuiper belt analog (Riviere-Marichalar et al. 2012) in this system. The presence of infall in some species at one epoch and others at different epochs suggests that, like Pictoris, there may be more than one family of exocomets. If perturbed into star-grazing orbits by the same mechanism as for Pic, these data suggest that the wide planet frequency among A-early F stars in the PMG is at least 37.5, well above the frequency estimated for young moving groups independent of host star spectral type.

Pictoris Moving Group↗
DOE OSTI2021

A Projected Network Model of Online Disinformation Cascades

Within the past half-decade, it has become overwhelmingly clear that suppressing the spread of deliberate false and misleading information is of the utmost importance for protecting democratic institutions. Disinformation has been found to come from both foreign and domestic actors, but the effects from either can be disastrous. From the simple encouragement of unwarranted distrust to conspiracy theories promoting violence, the results of disinformation have put the functionality of American democracy under direct threat. Present scientific challenges posed by this problem include detecting disinformation, quantifying its potential impact, and preventing its amplification. We present a model on which we can experiment with possible strategies toward the third challenge: the prevention of amplification. This is a social contagion network model, which is decomposed into layers to represent physical, ''offline'', interactions as well as virtual interactions on a social media platform. Along with the topological modifications to the standard contagion model, we use state-transition rules designed specifically for disinformation, and distinguish between contagious and non-contagious infected nodes. We use this framework to explore the effect of grassroots social movements on the size of disinformation cascades by simulating these cascades in scenarios where a proportion of the agents remove themselves from the social platform. We also test the efficacy of strategies that could be implemented at the administrative level by the online platform to minimize such spread. These top-down strategies include banning agents who disseminate false information, or providing corrective information to individuals exposed to false information to decrease their probability of believing it. We find an abrupt transition to smaller cascades when a critical number of random agents are removed from the platform, as well as steady decreases in the size of cascades with increasingly more convincing corrective information. Finally, we compare simulated cascades on this framework with real cascades of disinformation recorded on Whatsapp surrounding the 2019 Indian election. We find a set of hyperparameter values that produces a distribution of cascades matching the scaling exponent of the distribution of actual cascades recorded in the dataset. We acknowledge the available future directions for improving the performance of the framework and validation methods, as well as ways to extend the model to capture additional features of social contagion.

42 ENGINEERING↗
DOE OSTI2023

Nonequilibrium design strategies for functional colloidal assemblies

Here, we use a nonequilibrium variational principle to optimize the steady-state, shear-induced interconversion of self-assembled nanoclusters of DNA-coated colloids. Employing this principle within a stochastic optimization algorithm allows us to identify design strategies for functional materials. We find that far-from-equilibrium shear flow can significantly enhance the flux between specific colloidal states by decoupling trade-offs between stability and reactivity required by systems in equilibrium. For isolated nanoclusters, we find nonequilibrium strategies for amplifying transition rates by coupling a given reaction coordinate to the background shear flow. We also find that shear flow can be made to selectively break detailed balance and maximize probability currents by coupling orientational degrees of freedom to conformational transitions. For a microphase consisting of many nanoclusters, we study the flux of colloids hopping between clusters. We find that a shear flow can amplify the flux without a proportional compromise on the microphase structure. This approach provides a general means of uncovering design principles for nanoscale, autonomous, functional materials driven far from equilibrium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks

To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than the dimension of the input vector (haystack). More recently learners known as artificial neural networks (ANN) have shown great successes in many machine learning tasks, in particular fitting nonlinear associations. Small learning rate, stochastic gradient descent algorithm and large training set help to cope with the explosion in the number of parameters present in deep neural networks. Yet few ANN learners have been developed and studied to find needles in nonlinear haystacks. Driven by a single hyperparameter, our ANN learner, like for sparse linear associations, exhibits a phase transition in the probability of retrieving the needles, which we do not observe with other ANN learners. To select our penalty parameter, we generalize the universal threshold of Donoho and Johnstone (Biometrika 81(3):425–455, 1994) which is a better rule than the conservative (too many false detections) and expensive cross-validation. In the spirit of simulated annealing, we propose a warm-start sparsity inducing algorithm to solve the high-dimensional, non-convex and non-differentiable optimization problem. We perform simulated and real data Monte Carlo experiments to quantify the effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Monitoring the state of the human airways by analysis of respiratory sound

A mechanism whereby sound is generated by the motion of vortices in the human lung is described. This mechanism is believed to be responsible for most of the sound which is generated both on inspiration and expiration in normal lungs. Mathematical expressions for the frequencies of sound generated, which depend only upon the axial flow velocity and diameters of the bronchi, are derived. This theory allows the location within the bronchial tree from which particular sounds emanate to be determined. Redistribution of pulmonary blood volume following transition from earth gravity to the weightless state probably alters the caliber of certain airways and doubtless alters sound transmission properties of the lung. We believe that these changes can be monitored effectively and non-invasively by spectral analysis of pulmonary sound.

Hardin, J. C.↗
NASA NTRS1990

Stratospheric ion and aerosol chemistry and possible links with cirrus cloud microphysics - A critical assessment

Aspects of stratospheric ion chemistry and physics are assessed as they relate to aerosol formation and the transport of aerosols to upper tropospheric regions to create conditions favorable for cirrus cloud formation. It is found that ion-induced nucleation and other known phase transitions involving ions and sulfuric acid vapor are probably not efficient processes for stratospheric aerosol formation, and cannot compete with condensation of sulfuric acid on preexisting particles of volcanic or meteoritic origin which are larger than about 0.15 micron in radius. Thus, galactic cosmic rays cannot have a significant impact on stratospheric aerosol population. Changes in the stratospheric aerosol burden due to volcanos are up to two orders of magnitude larger than changes in ion densities. Thus, volcanic activity may modulate the radiative properties of cirrus clouds.

Mohnen, Volker A.↗
NASA NTRS2000

A High Resolution Spectroscopic Observation of CAL 83 with XMM-Newton/RGS

We present the first high resolution photospheric X-ray spectrum of a Supersoft X-ray Source, the famous CAL 83 in the Large Magellanic Cloud. The spectrum was obtained with the Reflection Grating Spectrometer on XMM-Newton during the Calibration/Performance Verification phase of the observatory. The spectrum covers the range 20-40 A at an approximately constant resolution of 0.05 A, and shows very significant, intricate detail, that is very sensitive to the physical properties of the object. We present the results of an initial investigation of the spectrum, from which we draw the conclusion that the spectral structure is probably dominated by numerous absorption features due to transitions in the Gshells of the mid-2 elements and the M-shell of Fe, in addition to a few strong K-shell features due to CNO.

Paerels, Frits↗
NASA NTRS1979

Monitoring the state of the human airways by analysis of respiratory sound

A mechanism whereby sound is generated by the motion of vortices in the human lung is described. This mechanism is believed to be responsible for most of the sound which is generated both on inspiration and expiration in normal lungs. Mathematical expressions for the frequencies of sound generated, which depend only upon the axial flow velocity and diameters of the bronchi, are derived. This theory allows the location within the bronchial tree from which particular sounds emanate to be determined. Redistribution of pulmonary blood volume following transition from Earth gravity to the weightless state probably alters the caliber of certain airways and doubtless alters sound transmission properties of the lung. We believe that these changes can be monitored effectively and non-invasively by spectral analysis of pulmonary sound.

Lung/anatomy & histology/physiology↗
NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

Khlopenkov, Konstantin V.↗
NASA NTRS1991

Applying generalized stochastic Petri nets to manufacturing systems containing nonexponential transition functions

Generalized stochastic Petri nets (GSPNs) are applied to flexible manufacturing systems (FMSs). Throughput subnets and s-transitions are presented. Two FMS examples containing nonexponential distributions which were analyzed in previous papers by queuing theory and probability theory, respectively, are treated using GSPNs developed using throughput subnets and s-transitions. The GSPN results agree with the previous results, and developing and analyzing the GSPN models are straightforward and relatively easy compared to other methodologies.

Watson, James F., III↗
DOE OSTI2021

Learning Forecasts of Rare Stratospheric Transitions from Short Simulations

Abstract Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event’s progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.

Finkel, Justin↗
NASA NTRS1973

Thermomechanical behavior of a polynorbornadiene

A high-temperature linear amorphous hydrocarbon polymer synthesized cationically from 2,5-norbornadiene was studied thermomechanically with respect to physical transitions and stability in nitrogen. The glass transition temperature was determined to be 320 C (at less than 1 cps), which is probably the highest known for a linear hydrocarbon addition polymer. The thermomechanical technique of torsional braid analysis, together with thermogravimetric analysis, differential thermal analysis, infrared analysis, and solubility studies, was used to investigate the sequential events of the glass transition and degradation. The polymer is of particular interest since it is a high-temperature plastic which in the bulk form would probably need to be processed at high speeds in the vicinity of the glass transition temperature in an inert atmosphere. The presence of tertiary hydrogen atoms should render it amenable to degradation by the earth's environment.

Roller, M. B.↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗