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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

3D X-ray CT for BGA/CGA Workmanship Defect Detection

Commercial-off-the-shelf (COTS) advanced microelectronic technologies in high-reliability versions are now being considered for use in a number of National Aeronautics and Space Administration (NASA) electronic systems. One of the key drawbacks of advanced electronic packages with hidden solder joint interconnections, such as the column grid array (CGA), is that inspection can be challenging—whether visually or using X-rays. In general, inspection for solder joint integrity is poor, except for identifying shorts. The new, advanced X-ray systems, especially the 3D computer tomography version, may be able to provide the three-dimensional features that are extremely difficult to resolve under the 2D systems.This report presents both 2D and 3D X-ray images along with their representative optical photomicrographs for a number of advanced electronics package assemblies including 3D stack and CGA assemblies before and after thermal cycling.

Ghaffarian, Reza↗
NASA NTRSDate not supplied

Closing the Loop on Space Trash / Is a Circular Economy Possible?

A crew member will nominally consume 2.5 kg of water per crew-member per day, rejectapproximately 0.032 kg of dry fecal solid waste per crew member per day (dry basis)and generate approximately 2,122 kg of logistical waste per year on a long-durationcrewed mission. This topic seeks to push the conversation towards completing a closedloop system and circular economy for all materials and recoverable resources withregard to waste management, regenerative water use, and recycling. In all aspects,reduction of crew maintenance time, mass, power and volume are at the forefront oftechnology development. In addition, the longevity and hardware robustness is important(lasting >3 years). Overall water recovery of >98% is desired, as well as, condensingheat exchangers that remove water from air with collection rates up to 1.45 kg of waterper hour. There are many technologies on the ground being developed for recycling andtrash management or regenerative water use that have yet to introduce themselves forspace development and infusion. Here we look to open the conversation and cross cutthese platforms to bring out some innovation and exposure into the hidden crevasses ofthe waste that humans create and how to minimize it/repurpose it, for all of humanity.

ISRU↗
NASA NTRS2020

Fermi and Swift Observations of GRB 190114C: Tracing the Evolution of High-energy Emission from Prompt to Afterglow

We report on the observations of gamma-ray burst (GRB) 190114C by the Fermi Gamma-ray Space Telescope and the Neil Gehrels Swift Observatory. The prompt gamma-ray emission was detected by the Fermi GRB Monitor (GBM), the Fermi Large Area Telescope (LAT), and the Swift Burst Alert Telescope (BAT) and the long-lived afterglow emission was subsequently observed by the GBM, LAT, Swift X-ray Telescope (XRT), and Swift UV Optical Telescope. The early-time observations reveal multiple emission components that evolve independently, with a delayed power-law component that exhibits significant spectral attenuation above 40 MeV in the first few seconds of the burst. This power-law component transitions to a harder spectrum that is consistent with the afterglow emission observed by the XRT at later times. This afterglow component is clearly identifiable in the GBM and BAT light curves as a slowly fading emission component on which the rest of the prompt emission is superimposed. As a result, we are able to observe the transition from internal-shock- to external-shock-dominated emission. We find that the temporal and spectral evolution of the broadband afterglow emission can be well modeled as synchrotron emission from a forward shock propagating into a wind-like circumstellar environment. We estimate the initial bulk Lorentz factor using the observed high-energy spectral cutoff. Considering the onset of the afterglow component, we constrain the deceleration radius at which this forward shock begins to radiate in order to estimate the maximum synchrotron energy as a function of time. We find that even in the LAT energy range, there exist high-energy photons that are in tension with the theoretical maximum energy that can be achieved through synchrotron emission from a shock. These violations of the maximum synchrotron energy are further compounded by the detection of very high-energy (VHE) emission above 300 GeV by MAGIC concurrent with our observations. We conclude that the observations of VHE photons from GRB 190114C necessitates either an additional emission mechanism at very high energies that is hidden in the synchrotron component in the LAT energy range, an acceleration mechanism that imparts energy to the particles at a rate that is faster than the electron synchrotron energy-loss rate, or revisions of the fundamental assumptions used in estimating the maximum photon energy attainable through the synchrotron process.

M. Ajello↗
NASA NTRS2022

4. Parallels in Communication and Navigation Technology and Natural Phenomenon

This chapter represents an introspective search for hidden connections between technology and nature and inadvertent influence on biomimicry – especially as it pertains to links to electromagnetics in navigation and communication systems. Several devices, phenomena, and approaches to problem solving are considered and an attempt is made to identify linkages between experiences, observables and the way we think about and create new technology.

Advanced Air Mobility↗
NASA NTRS2020

Detecting differential ground displacements of civil structures in fast-subsiding metropolises with interferometric SAR and band-pass filtering

Ground displacements due to changes in soil conditions represent a threat to the stability of civil structures in many urban areas, worldwide. In fast-subsiding areas, regional subsidence (wavelength ~ 1,000’s m) can be dominantly high and, consequently, mask other signals at local scales (wavelength ~ 10–100’s m). Still, engineering and construction applications require a comprehensive knowledge of local-scale signals, which can threaten the stability of buildings and infrastructure. Here we present a new technique based on band-pass filters for uncovering local-scale signals hidden by regional subsidence as detected by interferometric SAR measurements. We apply our technique to a velocity field calculated from 21 high-resolution COSMO-SkyMed scenes acquired over Mexico City and obtain components of long (> 478 m), intermediate (42–478 m) and short (< 42 m) spatial wavelengths. Our results reveal that long-wavelength velocities exceed − 400 mm/year, whereas intermediate- and short-wavelength velocities are in the order of ± 15 mm/year. We show that intermediate-wavelength velocities are useful for retrieving signals such as uplift along elevated viaducts of Metro lines 4 and B, as well as differential displacements in Pantitlán station’s pedestrian overpass system and across sharp geotechnical boundaries in the piedmont of Sierra de Santa Catarina—where surface faulting occurs.

Darío Solano-Rojas↗
NASA NTRSDate not supplied

Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the current labor-intensive processes of science prioritization, specifically by the National Academies’ Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first exploratory demonstration-of-concept results from an application of AI/ML to Survey science prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language Processing (NLP) to reveal trends in published astrophysics research that may indicate science priorities and which could be applied to strategic planning. For the purpose of the work that we summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast amount of text to reveal complex relationships among research topics, including the growth or decline of science community activities in those topics over time. We trained ourselves and AI/ML algorithms by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010 recommendations and compare with the solicited white papers. Comparing our results with actual Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of papers published in a topic area is a good proxy measure for importance of this topic area of research. With this training complete, we identified candidate astrophysics astrophysics science priorities for the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application of AI can potentially significantly reduce the current workload of the Decadal Survey processes and reveal otherwise unrecognized characteristics in the body of astronomical research. We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue promising results further. Our most critical governing assumption was that increased (or decreased) research activity can be used to identify scientific or technology topic areas worthy of increased (or decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box” nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal “hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may soon enable widespread and welcome identification of and advocacy for science and technology priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a near-term, in-depth evaluation of appropriate applications of AI, including implications and consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.

Artificial Intelligence↗
NASA NTRS2022

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗
NASA NTRSDate not supplied

Towards a program of record of inland water quality: Exploiting present and heritage multispectral sensors for maximum information extraction

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.

Inland↗
NASA NTRSDate not supplied

The Virginia Earth System Science Scholars (VESSS) Experience: Inspiring High School Students in Stem Through Mentorship and Collaborative Skills Development

Since 2016, the Virginia Earth System Science Scholars (VESSS) program offered by the Virginia Space Grant Consortium (VSGC) has provided an interactive science, technology, engineering, and mathematics learning experience for Virginia high school juniors and seniors. The VESSS program was created through a partnership with VSGC, NASA Langley Research Center (LaRC), and Hampton University to engage students in learning about Earth’s climate change and its global impacts. High school students from all areas of Virginia learn about Earth science and climate change in a 16 week online course focused on the Earth’s surface and interior, atmospheric composition, weather, water energy cycles, and climate variability. Students that complete the online course are invited to a no-cost seven-day residential summer academy at NASA LaRC where students are divided into teams based on four of the major spheres of study for Earth: atmosphere, biosphere, hydrosphere, and lithosphere. Each team is guided by a Master Teacher and teams of undergraduate student interns who were also previous VESSS scholars. Students interview for team positions and are given a role reflecting an actual satellite design team position such as project scientist, research scientist, budget analyst, launch and orbit analyst, contamination analyst, and education and public outreach specialist. The students spend a week mentored by NASA and industry scientists and engineers as they learn how to select science goals and objectives, select science instruments, balance a mission budget, determine the mission launch location and orbit, and communicate the importance of the mission to the public. Each team produces a mission proposal, and at the end of the week, each team presents their portion of the mission to a panel of experts, followed by a question and answer period. Through this week students learn teamwork, communication, time management, meeting management, and research skills. Project work is supplemented by team-building activities, NASA facility tours, and lectures by acclaimed guest speakers such as Dr. Christine Darden, a NASA “Hidden Figure.” Students have conveyed through course evaluations and testimonials not only the vast personal learning and growth they achieved, but also the new perspectives they gained on future STEM career fields and opportunities to explore.

Elaine Seasly↗
NASA NTRSDate not supplied

Enabling in-time Prognostics with Surrogate Modeling through Physics-enhanced Dynamic Mode Decomposition Method

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the PHM domain, where far-ahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. The Koopman theory, however, provides a mechanism to tradeoff low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. In this way, DMD allows for configurable simulation accuracy dependent on the dimensionality of the Koopman operator. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the prog models python package.

prognostics and health management↗
NASA NTRSDate not supplied

Understanding Atmospheric Absorption Effects on UV Spectra from Sounding Rockets using a Spherical-Shells Model

Our team is working on building and calibrating the FURST sounding rocket, with an expected launch in mid-2023. The goal is to image the most complete and highest resolution UV spectra to date. To do this, preciseradiometric and wavelength calibration techniques have been developed. We describe below our model of O2 atmospheric absorption and couple that with simulated FURST images. With a high-enough SNR, we canestimate our ability to use absorption peaks for calibration, or for back-calculating atmospheric properties. If data is available, this method could be applied to older sounding rocket data to find hidden science.

MSFC, NASA, UAH, CSPAR, FURST, MSU, SHINE, Aeronom↗
NASA NTRSDate not supplied

Understanding Atmospheric Absorption Effects on UV Spectra from Sounding Rockets using a Spherical-Shells Model

Our team is working on building and calibrating the FURST sounding rocket, with an expected launch in mid-2023. The goal is to image the most complete and highest resolution UV spectra to date. To do this, precise radiometric and wavelength calibration techniques have been developed. We describe below our model of O2 atmospheric absorption and couple that with simulated FURST images. With a high-enough SNR, we can estimate our ability to use absorption peaks for calibration, or for back-calculating atmospheric properties. If data is available, this method could be applied to older sounding rocket data to find hidden science.

MSFC, NASA, UAH, CSPAR, FURST, MSU, SHINE, Aeronom↗
NASA NTRSDate not supplied

An Updated Model for the Effect of Atmospheric Absorption on Sounding Rockets

Our team is working on building and calibrating the FURST sounding rocket, with an expected launch in mid-2023. The goal is to image the most complete and highest resolution UV spectra to date. To do this, precise radiometric and wavelength calibration techniques have been developed. We describe below our model of O2 atmospheric absorption and couple that with simulated FURST images. With a high-enough SNR, we can estimate our ability to use absorption peaks for calibration, or for back-calculating atmospheric properties. If data is available, this method could be applied to older sounding rocket data to find hidden science.

MSFC, NASA, UAH, CSPAR, SPA, FURST, MSU↗
NASA NTRSDate not supplied

Why Developing A Full-Sun UV Spectrograph Sounding Rocket Is Difficult but Necessary

For a conference focused on a 20-year perspective of where we have been and where we are going, this talk will highlight specifically why we need a sounding rocket to lead the way in obtaining high-resolution UV spectroscopy of our sun-as-a-star. The currently available data is either sparse or at a much lower resolution when compared with Hubble (HST) spectra of other stars. Because of this, we cannot say with certainty if our Sun’s variability is typical. This limitation is hindering our quest to place our Sun in it’s rightful place on our star-charts. Ultraviolet light is a key component in determining this. Elements such as Hydrogen and Iron have strong emission lines here, and from them we can determine many things about the star from which they originate. The width of these spectral features can tell us about the relative plasma activity on the surface of the star, the abundance of those elements, and of course the temperatures. This has of course been done with many instruments that have very narrow field of views and/or narrow bandwidths. The only way to directly compare our Sun’s UV ”fingerprint” with other stars is to take an image of it in the same manner by which HST does: as a point source object. Our team’s aim has been to develop a sounding rocket capable of doing just that. Along the way, the many challenges we face are producing many solutions. A unique take on the traditional Rowland circle spectrograph will allow us to cover a large range of UV spectra with minimal moving parts. A novel in-situ gain calibration technique will be used in order to maintain the high level of radiometric accuracy desired. Naturally, complications arise when one takes UV measurements as you are flying out of earth’s atmosphere and back into it. Though it may be minor, the absorbed UV spectra from the atmosphere will have to be accounted for. In preparing for this, we have stumbled across a way in which those ”telluric” absorption lines may prove useful for spectral calibration purposes. In addition, raw data from previous sounding rocket missions may have hidden within them useful information for the atmospheric-physics community. In this talk, we will highlight the unique calibration problems and solutions we have faced, and how those are of greater importance to the greater heliophysics and atmospheric communities. With these amazing developments at hand, the future of heliophysics instrumentation is looking bright. This material is based upon work supported by the NSF EPSCoR RII-Track-1.2a (Non-invasive plasma diagnostics for LTP) Cooperative Agreement OIA-1655280. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

NASA, UAH, MSU, Sounding Rocket, Aeronomy, Instrum↗
NASA NTRS2022

Resolving the fine structure in the energy landscapes of repeat proteins

Ankyrin (ANK) repeat proteins are coded by tandem occurrences of patterns with around 33 amino acids. They often mediate protein–protein interactions in a diversity of biological systems. These proteins have an elongated non-globular shape and often display complex folding mechanisms. This work investigates the energy landscape of representative proteins of this class made up of 3, 4 and 6 ANK repeats using the energy-landscape visualisation method (ELViM). By combining biased and unbiased coarse-grained molecular dynamics AWSEM simulations that sample conformations along the folding trajectories with the ELViM structure-based phase space, one finds a three-dimensional representation of the globally funnelled energy surface. In this representation, it is possible to delineate distinct folding pathways. We show that ELViMs can project, in a natural way, the intricacies of the highly dimensional energy landscapes encoded by the highly symmetric ankyrin repeat proteins into useful low-dimensional representations. These projections can discriminate between multiplicities of specific parallel folding mechanisms that otherwise can be hidden in oversimplified depictions.

Murilo N. Sanches↗
NASA NTRS2021

Spitzer’s Last Look at Extragalactic Explosions: Long-Term Evolution of Interacting Supernovae

Here we present new, yet final, mid-infrared (mid-IR) data for supernovae (SNe) based on measurements with the Spitzer Space Telescope. Comparing our recent 3.6 and 4.5 μm photometry with previously published mid-IR and further multiwavelength data sets, we were able to draw some conclusions about the origin and heating mechanism of the dust in these SNe or in their environments, as well as about possible connection with circumstellar matter (CSM) originating from pre-explosion mass-loss events in the progenitor stars. We also present new results regarding both certain SN classes and single objects. We highlight the mid-IR homogeneity of SNe Ia-CSM, which may be a hint of their common progenitor type and of their basically uniform circumstellar environments. Regarding single objects, it is worth highlighting the late-time interacting Type Ib SNe 2003gk and 2004dk, for which we present the first-ever mid-IR data, which seem to be consistent with clues of ongoing CSM interaction detected in other wavelength ranges. Our current study suggests that long-term mid-IR follow-up observations play a key role in a better understanding of both pre- and post-explosion processes in SNe and their environments. While Spitzer is not available anymore, the expected unique data from the James Webb Space Telescope, as well as long-term near-IR follow-up observations of dusty SNe, can bring us closer to the hidden details of this topic.

Supernovae↗
NASA NTRS2023

Aviation 2072: Scenario Planning for Wicked Problems

The world is changing rapidly. And with it, so are the potential futures for humanity. Many of these potential futures pose hidden threats, while others offer new opportunities for aviation and the broader aerospace community. To mitigate these risks and capitalize on opportunities, organizations must work to deeply understand these potential future scenarios and uncover any underlying drivers. Organizations will be more equipped and informed when making strategic investment decisions by better understanding these threats, opportunities, and drivers . The future scenarios and their threats and opportunities described in this paper were identified through a series of extensive brainstorming workshops, collectively titled MADNESS (Mapping for Aviation Driven by Needs Emergence and Satiating Society). These workshops engaged NASA civil servants and contractors with diverse backgrounds through a facilitated process of future scenario development and critique. The participants were led through exercises focused on a time horizon spanning from 2022 to 2072 and on problems that aviation might solve and or create during this period. From the broad results, two patterns of critical uncertainty emerged: “availability” (scarcity versus abundance) and “transparency” (openness versus security and privacy). The successful iteration and ideation across the MADNESS workshops also suggests a repeatable mechanism for strategic risk exploration across a variety of NASA and industry stakeholders.

Mapping↗
NASA NTRS2025

Tutorial: MATLAB Implementation of a Successive Convexification Algorithm for 3 DoF Rocket Landings

The primary objective of this work is to fill in gaps and explore an alternate way of solving the 3 DoF rocket-powered landing problem presented in the 2016 AIAA paper by Szmuk, Ackimese, and Berning using successive convexification (SCvx). In the original paper, CVX, an automatic parsing package, was used to transcribe the high-level trajectory optimization problem into a format that could be read by a conic solver. The parsing step, generally computationally intensive, is hidden from the user. The use of CVX is sufficient for the generation of trajectories off-line due to the lack of runtime and flight software implementation constraints. For on-line applications, it is necessary to parse the problem for flight software implementation. References on hand-parsing powered descent guidance (PDG) problems are sparse. In this Tech Memo, the process of transcribing the 3 DoF PDG problem into the format required by MATLAB’s built-in second-order cone solver, coneprog.m, is presented in detail. Due to the abridged 3 DoF dynamics and the relatively simple nonlinearities, this reference is the natural starting point for anyone interested in grasping the concepts behind SCvx pertaining to PDG and the parsing step. Simulation results shown in this report were independently created by solving the problem using coneprog.m. The intent of this memo is to serve as a supplemental material to the original paper by breaking down the concept behind successive convexification and shed light into the parsing process. Readers are encouraged to first familiarize themselves with the material laid out in the original reference.

Alex Hayes↗